mckinsey-technology-trends-outlook-2024.pdf

Technology Trends
Outlook 2024
July 2024

McKinsey & Company
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Insights across trends
The AI revolution
Generative AI
Applied AI
Industrializing machine learning
Building the digital future
Next-generation software development
Digital trust and cybersecurity
Compute and connectivity frontiers
Advanced connectivity
Immersive-reality technologies
Cloud and edge computing
Quantum technologies
4
13
14
20
25
30
31
36
43
44
49
54
59
65
66
71
77
82
87
88
94
Cutting-edge engineering
Future of robotics
Future of mobility
Future of bioengineering
Future of space technologies
A sustainable world
Electrification and renewables
Climate technologies beyond
electrification and renewables
Contents
3
Technology Trends Outlook 2024

Insights across trends
Despite challenging overall market conditions in
2023, continuing investments in frontier technologies
promise substantial future growth in enterprise adoption.
Generative AI (gen AI) has been a standout trend since
2022, with the extraordinary uptick in interest and
investment in this technology unlocking innovative
possibilities across interconnected trends such as
robotics and immersive reality. While the macroeconomic
environment with elevated interest rates has affected
equity capital investment and hiring, underlying
indicators—including optimism, innovation, and longer-
term talent needs—reflect a positive long-term trajectory
in the 15 technology trends we analyzed.
These are among the findings in the latest McKinsey
Technology Trends Outlook, in which the McKinsey
Technology Council identified the most significant
technology trends unfolding today (to know more about
the Council, see the sidebar “About the McKinsey
Technology Council”). This research is intended to help
executives plan ahead by developing an understanding
of potential use cases, sources of value, adoption drivers,
and the critical skills needed to bring these opportunities
to fruition.
Our analysis examines quantitative measures of
interest, innovation, investment, and talent to gauge the
momentum of each trend. Recognizing the long-term
nature and interdependence of these trends, we also
delve into the underlying technologies, uncertainties,
and questions surrounding each trend. (For more about
new developments in our research, please see the
sidebar “What’s new in this year’s analysis” on page 9; for
more about the research itself, please see the sidebar
“Research methodology” on pages 10–11.)
New and notable
The two trends that stood out in 2023 were gen AI and
electrification and renewables. Gen AI has seen a spike
of almost 700 percent in Google searches from 2022
to 2023, along with a notable jump in job postings and
investments. The pace of technology innovation has
been remarkable. Over the course of 2023 and 2024,
the size of the prompts that large language models
(LLMs) can process, known as “context windows,” spiked
from 100,000 to two million tokens. This is roughly the
difference between adding one research paper to a
model prompt and adding about 20 novels to it. And the
modalities that gen AI can process have continued to
increase, from text summarization and image generation
to advanced capabilities in video, images, audio, and text.
This has catalyzed a surge in investments and innovation
aimed at advancing more powerful and efficient
computing systems.
The large foundation models that power generative
AI, such as LLMs, are being integrated into various
enterprise software tools and are also being employed
for diverse purposes such as powering customer-facing
chatbots, generating ad campaigns, accelerating
drug discovery, and more. We expect this expansion
to continue, pushing the boundaries of AI capabilities.
Senior leaders’ awareness of gen AI innovation has
increased interest, investment, and innovation in
AI technologies and other trends, such as robotics,
which is a new addition to our trends analysis this year.
Advancements in AI are ushering in a new era of more
capable robots, spurring greater innovation and a wider
range of deployments.
About the McKinsey Technology Council
Technology is a catalyst for new opportunities, from inventing new products and services,
expanding the productivity frontier and capturing more value in our day-to-day work. The
McKinsey Technology Council helps business leaders understand frontier technologies and the
potential application to their businesses.
We look at a spectrum of technologies, from generative AI, machine learning, and quantum
computing to space technologies that are shaping new opportunities and applications. The McKinsey
Technology Council convenes a global group of more than 100 scientists, entrepreneurs, researchers,
and business leaders. We research, debate, and advise executives from all industries as they navigate the
fast-changing technology landscape.
—Lareina Yee, senior partner, McKinsey; chair, McKinsey Technology Council
4

Electrification and renewables was the other
trend that bucked the economic headwinds,
posting the highest investment and interest
scores among all the trends we evaluated.
Job postings for this sector also showed a
modest increase.
Although many trends faced declines in
investment and hiring in 2023, the long-term
outlook remains positive. This optimism is
supported by the continued longer-term
growth in job postings for the analyzed
trends (up 8 percent from 2021 to 2023)
and enterprises’ continued innovation and
heightened interest in harnessing these
technologies, particularly for future growth.
In 2023, technology equity investments
fell by 30 to 40 percent to approximately
$570 billion due to rising financing costs
and a cautious near-term growth outlook,
prompting investors to favor technologies
with strong revenue and margin potential.
This approach aligns with the strategic
perspective leading companies are
adopting, in which they recognize that
fully adopting and scaling cutting-edge
technologies is a long-term endeavor. This
recognition is evident when companies
diversify their investments across a
portfolio of several technologies, selectively
intensifying their focus on areas most likely
to push technological boundaries forward.
While many technologies have maintained
cautious investment profiles over the past
year, gen AI saw a sevenfold increase
in investments, driven by substantial
advancements in text, image, and video
generation.
Despite an overall downturn in private
equity investment, the pace of innovation
has not slowed. Innovation has accelerated
in the three trends that are part of the “AI
revolution” group: generative AI, applied AI,
and industrializing machine learning. Gen
AI creates new content from unstructured
data (such as text and images), applied
AI leverages machine learning models
for analytical and predictive tasks, and
industrializing machine learning accelerates
and derisks the development of machine
learning solutions. Applied AI and
industrializing machine learning, boosted by
the widening interest in gen AI, have seen
the most significant uptick in innovation,
reflected in the surge in publications and
patents from 2022 to 2023. Meanwhile,
electrification and renewable-energy
technologies continue to capture high
interest, reflected in news mentions and
web searches. Their popularity is fueled
by a surge in global renewable capacity,
their crucial roles in global decarbonization
efforts, and heightened energy security
needs amid geopolitical tensions and
energy crises.
The talent environment largely echoed the
investment picture in tech trends in 2023.
The technology sector faced significant
layoffs, particularly among large technology
companies, with job postings related to
the tech trends we studied declining by
26 percent—a steeper drop than the
17 percent decrease in global job postings
overall. The greater decline in demand for
tech-trends-related talent may have been
fueled by technology companies’ cost
reduction efforts amid decreasing revenue
growth projections. Despite this reduction,
the trends with robust investment and
innovation, such as generative AI, not only
maintained but also increased their job
postings, reflecting a strong demand for
new and advanced skills. Electrification and
renewables was the other trend that saw
positive job growth, partially due to public
sector support for infrastructure spending.
Even with the short-term vicissitudes in
talent demand, our analysis of 4.3 million
job postings across our 15 tech trends
underscored a wide skills gap. Compared
with the global average, fewer than half of
potential candidates have the high-demand
tech skills specified in job postings. Despite
the year-on-year decreases for job postings
in many trends from 2022 to 2023, the
number of tech-related job postings in 2023
still represented an 8 percent increase from
2021, suggesting the potential for longer-
term growth (Exhibit 1).
+8%
−17%
−26%
tech trends job postings
from 2021 to 2023
global job postings
from 2022 to 2023
tech trends job postings
from 2022 to 2023
5

Climate technologies
beyond electrification
and renewables
Industrializing
machine learning
Immersive-reality
technologies
Future of mobility
Applied AI
Next-generation
software
development
Future of
bioengineering
Advanced
connectivity
Future of
robotics
Quantum
technologies
Future of space
technologies
Generative
AI
Digital trust and
cybersecurity
Electrification
and renewables
Cloud and edge
computing
2021 2023
2021 2023
Annual change in tech trend job postings, 2021–23, millions of postings¹
Despite a one-year drop in job postings, demand for jobs in many
technology trends has increased over two years.
and renewables
Industrializing
machine learning
Immersive-reality
technologies
Future of mobility
Applied AI
Next-generation
software
development
Future of
bioengineering
Advanced
connectivity
Future of
robotics
Quantum
technologies
Future of space
technologies
Generative
AI
Digital trust and
cybersecurity
Electrification
and renewables
1
Out of 130 million surveyed job postings (extrapolated Jan–Oct 2023). Job postings are not directly equivalent to numbers of new or existing jobs.
Source: McKinsey’s proprietary Organizational Data Platform, which draws on licensed, de-identified public professional profile data
McKinsey & Company
Cloud and edge
computing
0
0.2
0.4
0.6
0
0.2
0.4
0
0.2
0.6
0.8
1.0
1.2
1.4
0
0.2
0.4
0.6
0
0.2
0.4
0
0.2
0.6
0.8
1.0
1.2
1.4
2021 2023
Building the digital future
AI revolution Compute and connectivity Cutting-edge engineering A sustainable world
+52%
change
–37%
+49% –34% +39% –38%
+77% –36%
2021 2022 2023
Cumulative change in tech trend job postings, 2021–23, millions of postings¹
+6% –23% +29% –9% +110% +111% +29% –20% +44% –17%
–18% +18% +341% +3% +19%
–6% +20% +48% +73%
–5% change
–1% –14% +14%
+0% –1%
+33% –29%
+34% –11%
+55%
–5%
+72%
+1%
+49% –34% +39% –38%
+77% –36%
+32% –24% +55% –36%
0
0.2
0.4
0.6
0
0.2
0.4
0
0.2
0.8
1.0
0
0.2
0.4
0.6
0
0.2
0.4
0
0.2
0.8
1.0
Exhibit 1
6
■ ■ ■ ■ ■
J ______ 1 __
1 ----
~

Adoption curve of technology trends, adoption score
Technologies progress through different stages, with some at the leading
edge of innovation and others approaching large-scale adoption.
McKinsey & Company
Higher adoption
Lower adoption
Web <2024>
<TechTrends-L0>
Exhibit <3> of <3>
1 Frontier
innovation
2 Experimenting
3 Piloting
4 Scaling
5 Fully scaled
Adoption
Applied AI
Generative AI
Next-gen software development
Climate technologies beyond
electrification and renewables
Future of bioengineering¹
Future of mobility¹
Future of robotics¹
Future of space technologies¹
4
3
2
1
¹Trend is more relevant to certain industries, resulting in lower overall adoption across industries compared with adoption within relevant industries.
Source: McKinsey technology adoption survey data; McKinsey analysis
Exhibit 2
Enterprise technology
adoption momentum
The trajectory of enterprise technology adoption
is often described as an S-curve that traces the
following pattern: technical innovation and exploration,
experimenting with the technology, initial pilots in
the business, scaling the impact throughout the
business, and eventual fully scaled adoption (Exhibit
2). This pattern is evident in this year’s survey analysis
of enterprise adoption conducted across our 15
technologies. Adoption levels vary across different
industries and company sizes, as does the perceived
progress toward adoption.
We see that the technologies in the S-curve’s early
stages of innovation and experimenting are either
on the leading edge of progress, such as quantum
technologies and robotics, or are more relevant to
a specific set of industries, such as bioengineering
and space. Factors that could affect the adoption of
these technologies include high costs, specialized
applications, and balancing the breadth of technology
investments against focusing on a select few that may
offer substantial first-mover advantages.
As technologies gain traction and move beyond
experimenting, adoption rates start accelerating, and
companies invest more in piloting and scaling. We see
this shift in a number of trends, such as next-generation
software development and electrification. Gen AI’s rapid
advancement leads among trends analyzed, with about
a quarter of respondents self-reporting that they are
scaling its use. More mature technologies, like cloud
and edge computing and advanced connectivity,
continued their rapid pace of adoption, serving
as enablers for the adoption of other emerging
technologies as well (Exhibit 3).
7

Self-reported adoption level by tech trend, 2023,1 % of respondents
1Respondents may interpret these categories differently based on their organizations. As such, the results should be considered as indicative of organizations’
self-assessments, rather than precise measurements. 2For a deeper look at our AI-related trends, see “The state of AI in early 2024: Gen AI adoption spikes
and starts to generate value,” McKinsey, May 30, 2024.
Source: McKinsey technology adoption survey data
More-mature technologies are more widely adopted, often serving as
enablers for more-nascent technologies.
McKinsey & Company
Experimenting
Not investing Piloting Scaling Fully scaled
Web <2024>
<TechTrends-L1>
Exhibit <2> of <3>
22
17
10
11
8
10
7
8
5
5
4
3
5
3
14
14
18
18
14
18
17
16
18
16
18
17
22
18
15
25
33
26
26
37
37
37
37
45
46
43
50
41
47
57
13
16
20
21
18
15
19
20
16
18
20
15
19
20
13
26
20
26
24
23
20
20
19
16
15
15
15
13
15
12
Future of robotics
Future of mobility
Climate technologies beyond electrification and
renewables
Applied AI
Generative AI2
Exhibit 3
The process of scaling technology adoption also
requires a conducive external ecosystem where user
trust and readiness, business model economics,
regulatory environments, and talent availability play
crucial roles. Since these ecosystem factors vary by
geography and industry, we see different adoption
scenarios playing out. For instance, while the leading
banks in Latin America are on par with their North
American counterparts in deploying gen AI use cases,
the adoption of robotics in manufacturing sectors varies
significantly due to differing labor costs affecting the
business case for automation.
As executives navigate these complexities, they
should align their long-term technology adoption
strategies with both their internal capacities and
the external ecosystem conditions to ensure the
successful integration of new technologies into
their business models. Executives should monitor
ecosystem conditions that can affect their prioritized
use cases to make decisions about the appropriate
investment levels while navigating uncertainties and
budgetary constraints on the way to full adoption (see
the “Adoption developments across the globe” sections
within each trend that showcase examples of adoption
dimensions for the trends or particular use cases therein
that executives should monitor). Across the board,
leaders who take a long-term view—building up their
talent, testing and learning where impact can be found,
and reimagining the businesses for the future—can
potentially break out ahead of the pack.
8
■ ■ ■ ■ ■
II II II
II II II
c==JI
II II
II
II
II
II II -
II I
--■
II I
--

9
The 15 tech trends
This report lays out considerations for all 15 technology
trends. For easier consideration of related trends,
we grouped them into five broader categories: the AI
revolution, building the digital future, compute and
connectivity frontiers, cutting-edge engineering, and a
sustainable world. Of course, there’s significant power
and potential in looking across these groupings when
considering trend combinations.
To describe the state of each trend, we developed scores
for innovation (based on patents and research) and
interest (based on news and web searches). We also
sized investments in relevant technologies and rated
their level of adoption by organizations (Exhibit 4).
What’s new in this year’s analysis
This year, we reflected the shifts in the
technology landscape with two changes on the
list of trends: digital trust and cybersecurity
(integrating what we had previously described
as Web3 and trust architectures) and the future
of robotics. Robotics technologies’ synergy
with AI is paving the way for groundbreaking
innovations and operational shifts across the
economic and workforce landscapes. We also
deployed a survey to measure adoption levels
across trends.
1.00
0
0 1.0
0.8
0.6
0.4
0.2
0
1.0
0.8
0.6
0.4
0.2
1 2 3 4 5
Interest,2
score
(0 = lower; 1 = higher)
Innovation,1
score
(0 = lower;
1 = higher)
Adoption level, score
(1 = frontier innovation;
5 = fully scaled)
McKinsey & Company
Innovation, interest, investment, and adoption, by technology trend, 2023
Each trend is scored based on its level of innovation, interest, investment,
and adoption.
250 150 75 20
Equity investment, $ billion
Applied AI
Electrification/
renewables
Future of
mobility
Generative AI
Climate technologies beyond electrification and renewables
Future of robotics
Note: Innovation and interest scores for the 15 trends are relative to one another. All 15 trends exhibit high levels of innovation and interest compared with
other topics and are also attracting significant investment.
1
The innovation score combines the 0–1 scores for patents and research, which are relative to the trends studied. The patents score is based on a measure
of patent filings, and the research score is based on a measure of research publications.
2
The interest score combines the 0–1 scores for news and searches, which are relative to the trends studied. The news score is based on a measure of news
publications, and the searches score is based on a measure of search engine queries.
Exhibit 4
•
•
•
• • • • •

Research methodology
To assess the development of each technology trend, our team collected data on five tangible
measures of activity: search engine queries, news publications, patents, research publications,
and investment. For each measure, we used a defined set of data sources to find occurrences of
keywords associated with each of the 15 trends, screened those occurrences for valid mentions
of activity, and indexed the resulting numbers of mentions on a 0–1 scoring scale that is relative
to the trends studied. The innovation score combines the patents and research scores; the
interest score combines the news and search scores. (While we recognize that an interest
score can be inflated by deliberate efforts to stimulate news and search activity, we believe that
each score fairly reflects the extent of discussion and debate about a given trend.) Investment
measures the flows of funding from the capital markets into companies linked with the trend.
Data sources for the scores include the following:
— Patents. Data on patent filings are sourced from Google Patents, where the data highlight
the number of granted patents.
— Research. Data on research publications are sourced from Lens.
— News. Data on news publications are sourced from Factiva.
— Searches. Data on search engine queries are sourced from Google Trends.
— Investment. Data on private-market and public-market capital raises (venture capital and
corporate and strategic M&A, including joint ventures), private equity (including buyouts and
private investment in public equity), and public investments (including IPOs) are sourced from
PitchBook.
— Talent demand. Number of job postings is sourced from McKinsey’s proprietary
Organizational Data Platform, which stores licensed, de-identified data on professional
profiles and job postings. Data are drawn primarily from English-speaking countries.
In addition, we updated the selection and definition of trends from last year’s report to reflect
the evolution of technology trends:
— The future of robotics trend was added since last year’s publication.
— Data sources and keywords were updated. For data on the future of space technologies
investments, we used research from McKinsey’s Aerospace & Defense Practice.
10

Research methodology
(continued)
Finally, we used survey data to calculate the enterprise-wide adoption scores for each trend:
— Survey scope. The survey included approximately 1,000 respondents from 50 countries.
— Geographical coverage. Survey representation was balanced across Africa, Asia, Europe,
Latin America, the Middle East, and North America.
— Company size. Size categories, based on annual revenue, included small companies
($10 million to $50 million), medium-size companies ($50 million to $1 billion), and large
companies (greater than $1 billion).
— Respondent profile. The survey was targeted to senior-level professionals knowledgeable
in technology, who reported their perception of the extent to which their organizations were
using the technologies.
— Survey method. The survey was conducted online to enhance reach and accessibility.
— Question types. The survey employed multiple-choice and open-ended questions for
comprehensive insights.
— Definition of enterprise-wide adoption scores:
• 1: Frontier innovation. This technology is still nascent, with few organizations investing in or
applying it. It is largely untested and unproven in a business context.
• 2: Experimentation. Organizations are testing the functionality and viability of the
technology with a small-scale prototype, typically done without a strong focus on a near-
term ROI. Few companies are scaling or have fully scaled the technology.
• 3: Piloting. Organizations are implementing the technology for the first few business use
cases. It may be used in pilot projects or limited deployments to test its feasibility and
effectiveness.
• 4: Scaling. Organizations are in the process of scaling the deployment and adoption of the
technology across the enterprise. The technology is being scaled by a significant number
of companies.
• 5: Fully scaled. Organizations have fully deployed and integrated the technology across
the enterprise. It has become the standard and is being used at a large scale as companies
have recognized the value and benefits of the technology.
11

Aakanksha Srinivasan
Ahsan Saeed
Alex Arutyunyants
Alex Singla
Alex Zhang
Alizee Acket-Goemaere
An Yan
Anass Bensrhir
Andrea Del Miglio
Andreas Breiter
Ani Kelkar
Anna Massey
Anna Orthofer
Arjit Mehta
Arjita Bhan
Asaf Somekh
Begum Ortaoglu
Benjamin Braverman
Bharat Bahl
Bharath Aiyer
Bhargs Srivathsan
Brian Constantine
Brooke Stokes
Bryan Richardson
Carlo Giovine
Celine Crenshaw
Daniel Herde
Daniel Wallance
David Harvey
Delphine Zurkiya
Diego Hernandez Diaz
Douglas Merrill
Elisa Becker-Foss
Emma Parry
Eric Hazan
Erika Stanzl
Everett Santana
Giacomo Gatto
Grace W Chen
Hamza Khan
Harshit Jain
Helen Wu
Henning Soller
Ian de Bode
Jackson Pentz
Jeffrey Caso
Jesse Klempner
Jim Boehm
Joshua Katz
Julia Perry
Julian Sevillano
Justin Greis
Kersten Heineke
Kitti Lakner
Kristen Jennings
Liz Grennan
Luke Thomas
Maria Pogosyan
Mark Patel
Martin Harrysson
Martin Wrulich
Martina Gschwendtner
Massimo Mazza
Matej Macak
Matt Higginson
Matt Linderman
Matteo Cutrera
Mellen Masea
Michiel Nivard
Mike Westover
Musa Bilal
Nicolas Bellemans
Noah Furlonge-Walker
Obi Ezekoye
Paolo Spranzi
Pepe Cafferata
Robin Riedel
Ryan Brukardt
Samuel Musmanno
Santiago Comella-Dorda
Sebastian Mayer
Shakeel Kalidas
Sharmila Bhide
Stephen Xu
Tanmay Bhatnagar
Thomas Hundertmark
Tinan Goli
Tom Brennan
Tom Levin-Reid
Tony Hansen
Vinayak HV
Yaron Haviv
Yvonne Ferrier
Zina Cole
Michael Chui
McKinsey Global Institute
partner, Bay Area
Roger Roberts
Partner,
Bay Area
Mena Issler
Associate partner,
Bay Area
Lareina Yee
Senior partner, Bay Area; chair,
McKinsey Technology Council
About the authors
The authors wish to thank the following McKinsey colleagues for their contributions to this research:
We appreciate the contributions of members of QuantumBlack, AI by McKinsey, to the insights on the AI-related trends.
They also wish to thank the external members of the McKinsey Technology Council for their insights and perspectives,
including Ajay Agrawal, Azeem Azhar, Ben Lorica, Benedict Evans, John Martinis, and Jordan Jacobs.
12

The AI revolution
13

Generative AI
The trend—and why it matters
Generative AI (gen AI) has been making significant strides,
pushing the boundaries of machine capabilities. Gen AI models
are trained on vast, diverse data sets. They take unstructured
data, such as text, as inputs and produce unique outputs—also
in the form of unstructured data—ranging from text and code to
images, music, and 3D models.
Over the past year, we’ve seen remarkable advancements in
this field, with text generation models such as OpenAI’s GPT-4,
Anthropic’s Claude, and Google’s Gemini producing content that
mimics human-generated responses, as well as with image-
generation tools such as DALL-E 3 and Midjourney creating
photorealistic images from text descriptions. OpenAI’s recent
launch of Sora, a text-to-video generator, further showcases
the technology’s potential. Even music composition is being
revolutionized, with models such as Suno creating original pieces
in various styles.
Gen AI has sparked widespread interest, with individuals and
organizations across different regions and industries exploring its
potential. According to the latest McKinsey Global Survey on the
state of AI, 65 percent of respondents say their organizations are
regularly using gen AI in at least one business function, up from
one-third last year,1
and gen AI use cases have the potential to
generate an annual value of $2.6 trillion to $4.4 trillion.2
However, it’s important to recognize the risks that accompany the
use of this powerful technology, including bias, misinformation, and
deepfakes. As we progress through 2024 and beyond, we anticipate
organizations investing in the risk mitigation, operating model,
talent, and technological capabilities required to scale gen AI.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Private- and public-
market capital raises for
relevant technologies
Patents Patent
filings for technologies
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
publications on topics
associated with trend
0.2
0.4
0.6
0.8
News
Talent demand
Research
Searches
1.0
Patents
Equity
investment
Scoring the trend
THE AI REVOLUTION Score by vector (0 = lower; 1 = higher)
Generative AI
Gen AI saw a surge in 2023, driven by ChatGPT’s
late-2022 launch, alongside earlier models such as
DALL-E 2 and Stable Diffusion. Gen AI saw significant
growth from 2022 to 2023 across each quantitative
dimension, such as a sevenfold increase in the number
of searches and investments, reflecting a strong sense
of excitement about the trend.
Industries affected: Aerospace and defense;
Agriculture; Automotive and assembly; Aviation,
travel, and logistics; Business, legal, and profes-
sional services; Chemicals; Construction and
building materials; Consumer packaged goods;
Education; Electric power, natural gas, and utilities;
Financial services; Healthcare systems and
services; Information technology and electronics;
Media and entertainment; Metals and mining; Oil
and gas; Pharmaceuticals and medical products;
Public and social sectors; Real estate; Retail;
Semiconductors; Telecommunications
Adoption score, 2023
$36 +111%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
2019
1.0
0
2023
1
“The state of AI in early 2024: Gen AI adoption spikes and starts to generate value,” McKinsey, May 30, 2024.
2
The economic potential of generative AI: The next productivity frontier, McKinsey, June 14, 2023.
14

Latest developments
Gen AI is a fast-growing and constantly innovating trend,
with recent developments including the following:
— Multimodal generative models are on the rise. As
gen AI continues to evolve and gain more attention in
various industries, it’s becoming increasingly clear that
multimodality will play a pivotal role. By combining text,
images, sounds, and videos, AI models can generate
outputs applicable across a wide range of industries
and business functions. This pursuit of multimodality
is intensifying across leading players such as OpenAI
and Google (with its Lumiere AI web app). For example,
Google’s Gemini showcases a powerful multimodal
system capable of processing information in various
formats, including text, code, tables, images, and
even audio.
— Powerful open-source models are challenging their
closed-source counterparts in performance and
developer adoption. While significant investments
are encouraging the development of proprietary large
language models (LLMs), such as GPT-4 with vision
(GPT-4V), the AI community is also witnessing a surge in
open-source models, such as Llama 3. This momentum
is fueled by the enthusiasm of developers and users
who welcome the unprecedented access to build
innovative tools and study complex systems. The
accessibility of open-source models is attracting a
growing developer base.
— The context window in natural-language processing
(NLP) is expanding. This expansion allows for longer and
smarter prompts. For instance, in early 2024, Google
released the largest context window in the market with
Gemini 1.5 Pro, which has a standard context window of
128,000 tokens, with the potential to reach two million
tokens.3
This larger context window enables the model
to generate more coherent and contextually relevant
responses by considering a larger amount of text.
However, expanding prompt size can paradoxically lead
to models getting “lost in the middle,” as they tend to
focus on specific parts of the text while avoiding the rest.
— LLMs are increasingly being embedded into various
enterprise tools. We are witnessing a significant uptick
in the integration of LLMs into various enterprise
tools. This surge is fueled by the growing demand for
automation, efficiency, personalized user experiences,
and the capacity to decipher complex patterns that
can lead to actionable insights. Consequently, a rising
number of vendors are choosing to integrate LLMs into
their applications and tools. This trend is especially
prominent in the marketing and customer care domains,
with Salesforce Einstein and ServiceNow serving as
prime examples.
— The multiagent approach has gained significant traction
with the rapid development of LLMs and continued
innovation. Companies now recognize the benefits
of employing multiple language models that work in
harmony rather than relying on a single model. This
approach offers a fresh perspective on tackling complex
challenges by leveraging the capabilities of multiple AI
agents, each specializing in different domains, to solve
a single problem collaboratively. By working together,
these agents can not only accelerate problem-solving
but also leverage varied perspectives and expertise to
deliver more effective and efficient solutions. Some
of the tools using this approach tend to be unstable,
but as models improve, their throughput should
significantly increase, making them increasingly
relevant for the future.
3
The Keyword [Google’s official blog], “Gemini breaks new ground with a faster model, longer context, AI agents and more,” blog entry by Demis Hassabis,
May 14, 2024.
15

Job postings by title, 2019–23, thousands
Demand
Generative AI
Talent and labor markets
Roles related to gen AI have experienced significant and rapid growth in talent demand since 2019, with a 111
percent increase in job postings compared with 2023. This growth is driven by increased interest and invest-
ment in the field. Almost all roles in gen AI, except for regulation affairs directors, have seen a notable rise in
demand, particularly for individual contributor roles. Organizations are now focusing on scaling and expanding
their internal capabilities to harness the potential of gen AI, leading to a sharp increase in demand for data
scientists, software engineers, and data engineers.
Talent availability, % share of postings requiring skill
Talent availability, ratio of talent to demand
Skills availability
Proficiency in gen AI necessitates expertise in AI, machine learning, and programming languages such as
Python. The availability of high-level gen AI skills is notable, with individuals citing proficiency in this area as
necessary to capture employers’ attention. There are significant skills overlaps with the “applied AI” and
“industrializing machine learning” trends (please refer to those trends for more details).
Data scientist
Software engineer
Data engineer
Software developer
Project manager
Regulation affairs director
Product manager
Machine learning engineer
0
1
2
3
4
6
5
2019 2023
0.3×
4.1×
2.9× 3.7×
0.4×
12.1×
0.2×
34 22 20 13 12 12
6
Artificial
intelligence
Machine
learning
Python Data
analysis
Software
engineering
Generative
AI
Regulatory
compliance
Artificial
intelligence
Machine
learning
Python Data
analysis
Software
engineering
Generative
AI
Regulatory
compliance
16

Adoption developments across the globe
Gen AI emerges as a front-runner in the trends landscape,
sharing the top spot with electrification and renewables
for the highest percentage of respondents scaling its
implementation. This underscores its significance as a
pivotal, high-growth trend to closely monitor throughout
the year.
Many companies have made progress throughout the year
on adopting gen AI and are currently working on scaling
it across their businesses. While gen AI adoption has
surged across various sectors, the technology, media, and
telecommunications sector has notably emerged as a leader
in the deployment of the technology.
The lack of availability of local language support poses
challenges to adoption globally. Some countries, including
India, Japan, and countries in the Middle East, have pushed
to develop their own LLMs. In Africa, the prioritization of
data locality and proximity hampers the building of LLMs.
Significant progress has been made recently with the
emergence of multilingual models. Multilingual capabilities
could become essential for any LLM, with the primary focus
shifting to the degree of localization, including the use of
slang, technical terms, and other nuances.
Adoption dimensions
The adoption trajectory of advanced technologies varies for
each technology and each use case within that technology.
Advancements along the following dimensions could enable
reaching the next level of adoption for gen AI:
— a clearly defined ROI for widespread horizontal and
vertical use cases by sector, along with a demonstrated
ability to control risks and ensure safety with the
development and deployment of new AI solutions
— decreased computational costs, alongside improvement
in overall AI efficiencies (for example, improving latency)
‘Since gen AI captured public attention at the end of
2022, a significant amount of focus has been placed on
delivering value through foundation models. Many are
already demonstrating cross-industry value, such as
coding acceleration or sales and marketing use cases,
as well as domain-specific models, such as protein
engineering or chemistry discovery foundation models.
The field continues to improve quickly with new tools—
for example, multimodal, agent-based models. Companies
should concentrate on building capabilities in this domain
and prioritize areas of focus to ensure they capture early
value and aren’t left behind.’
– Matej Macak, partner, London
17

In real life
Real-world examples involving the use of gen AI include
the following:
— ING, a global financial institution, leveraged gen AI to
enhance customer service in the Netherlands, one of its
key markets.4
While the current classic chatbot usually
resolves 40 to 45 percent of those chats, that leaves
another 16,500 customers a week who need to speak
with a live agent for help. Recognizing gen AI’s potential,
ING developed a bespoke customer-facing chatbot to
provide immediate, tailored assistance. This resulted
in helping 20 percent more customers avoid long wait
times and offering instant gratification in the first seven
weeks of use compared with the previous solution. The
chatbot is expected to reach 37 million customers as it
expands across ten markets.
— Recursion, a biotech company, has developed a new
gen AI platform and trained an LLM to accelerate
drug discovery. This platform enables scientists to
simultaneously access multiple machine learning
models that can process large amounts of proprietary
biological and chemical data sets to save time during
drug development.
— Itaú Unibanco, Latin America’s largest private sector
bank, created ad campaigns dedicated to women
football athletes using AI.5
The campaign highlighted
a 1941 Brazilian law that banned women from playing
football. It used AI-generated images from conversations
with real players and historians, among others, to create
the “Brazilian teams that have never existed” campaign,
paving the way for a new generation of AI-based media
advertising.
— Nubank is piloting a gen AI virtual assistant to boost
customer service.6
The virtual assistant focuses on
delivering personalized credit-related options within
the Nubank ecosystem. It helps customers understand
their credit card usage, explore collateralized credit
opportunities, and use its online payment service NuPay
without affecting their credit limits. Additionally, it may
offer personal loans based on the customer’s profile
and eligibility. For the initial phase, this innovative
solution will be available exclusively to members of
NuCommunity, Nubank’s customer engagement
platform. The assistant, developed using GPT-4 and
Nubank’s proprietary tools, was designed to evolve and
improve through continuous customer interactions,
ensuring a dynamic, customer-focused service.
Underlying technologies
Multiple types of software and hardware power gen AI
across the entire tech stack. These include the following:
— Application layer. Typically, this is the interface that the
end user interacts with (for example, chat).
— Integration/tooling layer. Sitting between the application
layer and foundation model, this layer integrates with
other systems to retrieve information, filter responses,
save inputs and outputs, distribute work, and enable
new features. Examples include the large-language-
programming framework LangChain and vector
databases such as Pinecone and Weaviate.
— Foundation models. These are deep learning models
trained on vast quantities of unstructured, unlabeled
data that can be used for a wide range of tasks out of the
box or adapted to specific tasks through fine-tuning.
— Digital infrastructure. This involves using the digital
abstraction of physical infrastructure to support
data storage, processing, and computation. Digital
abstraction includes databases (for example, SQL and
NoSQL) and core tech services (for example, compute,
storage, and networking).
— Physical infrastructure. This encompasses hardware that
enables computational, data storage, and networking
needs, including data centers, AI accelerator chips,
and data center mechanical, electrical, and plumbing
technologies.
4
“Banking on innovation: How ING uses generative AI to put people first,” McKinsey, accessed May 2024.
5
“Brazilian teams that have never existed,” Ads of the World, Clio Awards, accessed May 2024.
6
“Nubank begins testing with generative artificial intelligence to enhance customers’ experience with credit,” Nubank press release, October 18, 2023.
18

Key uncertainties
The major uncertainties affecting gen AI include the
following:
— Cybersecurity and privacy concerns are prevalent,
notably regarding data leakage risks and vulnerabilities
(including customer and protected data).
— Ethical considerations surround the responsible use of
gen AI, including data governance, justice and fairness,
accountability, and explainability.
— Regulation and compliance might affect research into
gen AI and its potential applications.
— Copyright ownership and protection of content
generated by open-source models remains an
unanswered question.
— Environmental impact may increase as training models
expend exponentially more computational resources.
— Inaccuracies are the most recognized and experienced
risk for gen AI uses,7
and they can affect use cases
across the gen AI value chain.
Big questions about the future
Companies and leaders may want to consider a few
questions when moving forward with gen AI:
— How will the cost of model creation evolve, and how will it
affect competitive dynamics?
— Will enterprise adoption experience the same level
of exponential growth and monetization as seen in
consumer adoption?
— How will the market develop in terms of open-source
solutions versus closed-source?
— How should companies approach gen-AI-related risks,
including data privacy and security, equity, fairness,
compliance, and copyright protections?
— What strategies should policy makers adopt to address
the risk of social engineering from third-party LLM
solutions?
— When will error rates and avoidance of hallucinations get
to an acceptable level for large-scale implementations of
gen AI in everyday use cases?
— Which workers will see their roles shift due to gen AI, and
to what extent will they be affected?
— As technological advancements such as gen AI models,
accelerators, and throughput continue to evolve, what
are the primary use cases that companies should
prioritize, and how should they position themselves for
future relevance in terms of their degree of involvement,
whether as shapers, takers, or makers?
19
7
“The state of AI in early 2024,” May 30, 2024.
‘Gen AI is currently at the exciting nexus of demonstrated
proof of value, rapid innovation, significant public and private
investment, and widespread consumer interest. The year 2023
was the year of pilots, and, moving forward, we can expect
to see two important areas of focus to accelerate adoption
and value creation: one is a rapid expansion of modular and
secure enterprise platforms that will serve as the foundation
for developing gen AI applications, and two, a focus on the
reskilling and rewiring of processes required in a business
domain to drive user adoption and capture value.’
– Delphine Nain Zurkiya, senior partner, Boston

Applied AI
As we navigate through 2024, the impact of analytical AI
technologies, including applications of machine learning (ML),
computer vision, and natural-language processing (NLP),
continues to grow across all sectors. Companies are using data
to derive insights to automate processes, transform businesses,
and make better decisions. McKinsey research estimates that
AI applications can potentially unlock an economic value of $11
trillion to $18 trillion annually.1
The excitement around generative
AI (gen AI) has led to increasing awareness of the potential value
of applied AI. In our recent global survey on the state of AI in 2024,
67 percent of respondents say they expect their organizations to
invest more in AI over the next three years.2
The survey highlights
that organizations continue to see returns from AI efforts across
business domains. Regulators and policy makers alike are
also taking note of AI’s increasing impact, with the European
Parliament, for example, passing the unified EU Artificial
Intelligence Act.3
However, the journey to AI adoption is filled with challenges
and learning opportunities, such as transforming organizational
culture to foster collaboration, trust, and adaptation to new
ways of working; acquiring, leveraging, and organizing valuable
sources of large data sets; and interpreting model outputs to build
end-user trust in them. Leaders should anticipate challenges
such as governance conflicts across the entire business—given
the cross-domain nature of AI—and the rapid evolution of the
regulatory and ethical landscape. Despite these challenges,
establishing protocols and guardrails, along with effective change
management, can help mitigate risks and ensure the successful
incorporation of AI into business operations.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.2
0.4
0.6
0.8
News
Talent demand
Research
Searches
1.0
Patents
Equity
investment
Scoring the trend
Applied AI
High innovation and investment scores for applied AI
are commensurate with its large potential impact. Each
year from 2019 to 2023, applied AI has had the highest
innovation scores of all the trends we studied, and its
investment score also ranks in the top five. While
demand for applied AI talent declined 29 percent from
2022 to 2023, perhaps unsurprisingly, in 2023,
demand for talent in applied AI remained among the
highest of all the trends we studied.
sional services; Chemicals; Construction and
building materials; Consumer packaged goods;
Education; Electric power, natural gas, and utilities;
$86 –29%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
2019
1.0
0
2023
1
The economic potential of generative AI: The next productivity frontier, McKinsey, June 14, 2023.
2
“The state of AI in early 2024: Gen AI adoption spikes and starts to generate value,” McKinsey, May 30, 2024.
3
Melissa Heikkilä, “The AI Act is done. Here’s what will (and won’t) change,” MIT Technology Review, March 19, 2024.
20

Latest developments
Recent developments involving applied AI include the
following:
— The emphasis on data-centric AI is growing. Rich, high-
quality data sets are essential assets for capturing value
from AI. The shift toward data-centric AI represents a
significant evolution in the field, as capabilities such
as picking the right model or hyperparameter tuning
become more automated and easier to use. Data-centric
AI use cases are diverse and widespread, but specific
examples include financial institutions using it to detect
and prevent fraudulent activities, healthcare providers
promoting transparency in AI-driven diagnoses, or
manufacturers identifying potential biases in quality
control systems. A company’s unique data can be
used to train AI models to automate and optimize core
processes and unlock new business potential. Having
access to unique data sets can provide a distinct
competitive advantage, which explains why companies
such as OpenAI are actively seeking new data and
purchasing exclusive rights.
As companies build their own private AI environments,
the scope of data governance will expand beyond
privacy to address interconnected threats such as data
poisoning (for example, tampering with the training
data) and model hijacking (for instance, taking control
of an existing model and manipulating it to perform
unauthorized tasks). This transition requires robust data
practices, including maintaining data quality, tracing
data lineage, and employing explainable AI to foster trust
and reduce bias.
— Hardware acceleration has gained significant
momentum in applied AI. The continuous increase
in the scale and complexity of deep learning models
has surpassed the abilities of conventional central
processing units, accelerating hardware development.
To train these large models and operate them in real
time, organizations are shifting toward specialized
hardware such as graphics processing units (GPUs),
field-programmable gate arrays (FPGAs), application-
specific integrated circuits (ASICs), and high-bandwidth
memory (HBM) chips. Originally designed for graphics,
GPUs now provide the parallel processing power
needed for AI tasks. FPGAs offer adaptability for
custom solutions at the edge, while ASICs offer top-
tier performance and efficiency for specific tasks. As
the complexity of AI increases, the search for faster,
more efficient hardware persists. By leveraging the
capabilities of specialized hardware, organizations can
spearhead the forthcoming wave of AI innovation.
— Generative AI (gen AI) opens the door to more applied
AI. Gen AI adoption is not only increasing among
curious individuals but also catalyzing increased
adoption of applied AI. We see organizations getting the
most impact from gen AI when they intertwine it with
applied AI use cases. For instance, a digital-marketing
company is using gen AI to create a variety of unique
and engaging content for its customers. However, the
real magic happens when its applied AI systems analyze
the performance of the generated content, identifying
patterns and trends in user engagement. This data
is then used to inform the gen AI system, generating
insights to produce more effective content in the future.
In this way, gen AI is being informed by real-world
data and feedback. The synergy between gen AI and
applied AI is what truly unlocks the potential of both
technologies.
‘The prominence of generative AI has opened the aperture
for business leaders to explore applied AI, which could
have as much or greater business impact.’
– Michael Chui, partner, Bay Area
21

Demand
Applied AI
Between 2019 and 2022, applied AI saw rapid growth in demand for talent, with job postings more than
tripling. Then, in line with the overall job market, applied AI saw a 29 percent reduction in total job postings
across the most common positions in 2023 compared with 2022. However, applied AI continues to have
among the most job postings per trend, with more than 500,000 job postings in 2023. And with high
investment activity, one could expect the demand for applied AI talent to remain steady.
Skills availability
There is a significant demand for specialized AI-related skill sets, and more people are striving to acquire these
skills, leading to larger numbers of people listing these skills on their profiles. As the level of competency
achieved can vary, companies will need to assess the skills proficiency of potential job applicants.
0
20
40
60
80
120
100
Data scientist
Software engineer
Data engineer
Software developer
Project manager
Scientist
Product manager
2019 2023
58
4.1× 3.7×
6.1×
0.3×
2.9× 2.2×
48
36 24 20
11
Machine
learning
Artificial
intelligence
Python
Data
analysis
Data
science
Deep
learning
Machine
learning
Artificial
intelligence
Python
Data
analysis
Data
science
Deep
learning
22

Applied AI tools are widely adopted across industries and
regions, driven by advancements in AI capabilities and an
increase in use cases. Most companies adopt applied AI
technologies to increase revenue—for example, through
integration with existing offerings or completely new
product and revenue streams.
The technology, media, and telecommunications and
financial-services sectors have emerged as leaders in the
adoption of applied AI tools. Some of these companies are
also the makers and innovators of the technology itself.
Across industries, including financial and professional
services, energy and materials, and consumer goods,
companies also have made significant investments in
applied AI tools.
Adoption dimensions
The adoption trajectory for advanced technologies will
look different for each technology and each use case
within that technology. Advancements along the following
dimensions could enable the next level of adoption for
applied AI:
— improved availability of plug-and-play solutions
to allow seamless integration into existing IT and
cloud infrastructure, combining standardized and
interoperable industrialized ML with gen AI capabilities
for a broader range of industry use cases and clear ROI
— effective change management to foster continuous
learning and knowledge sharing through training,
best-practice dissemination, and role modeling to drive
effective organizational adoption of AI technologies
— robust implementation of ML-operations (MLOps)
and large-language-model-operations (LLMOps)
practices to ensure optimal performance of AI
models in production environments (for more, see the
“Industrializing machine learning” trend), enabling
seamless scalability and sustained performance
from minimum-viable products to enterprise-wide
deployment
— improved data organization, availability, and
governance across organizations to enable AI
use cases
In real life
Real-world examples involving the use of applied AI
include the following:
— Saudi Aramco has built an AI hub to efficiently
analyze more than five billion data points per day
from wellheads in the oil and gas fields, enhancing
the understanding of petro-physical properties and
expediting decision making in exploration and drilling.
The solutions provide real-time alerts to prevent
business disruption, improve reservoir performance,
and save millions of dollars by optimizing field
development plans and well trajectories. AI technology
is also used to predict and prevent drilling challenges,
such as stuck pipes, and to monitor the health of
essential equipment, such as steam traps, using
infrared images.4
4
Victoria Sayce, “The AI Hub at Aramco: The home of our next-generation of digital innovation,” Aramco, October 23, 2022.
‘Applied AI has been transforming the way we work for
some time now. Gen AI takes this to a new level, allowing
organizations to tackle end-to-end workflows that were
previously too complex to go after. This is possible
with gen AI’s powerful off-the-shelf models that are
complemented by data-centric approaches. As we apply
these technologies, organizations need to emphasize the
integral human part of these workflows, ensuring that
these solutions are built for end users, by end users.’
– Stephen Xu, director of product management, QuantumBlack, AI by McKinsey, Toronto
23

— DigitalOwl’s AI-powered platform facilitates the
efficient processing and analysis of extensive medical
records, encompassing both traditional and electronic
health records. Tailored for life insurance underwriters,
this solution simplifies the navigation of complex and
voluminous medical documents by extracting and
organizing critical information.5
— Vistra Corp, the largest competitive power producer
in the United States, committed to a 60 percent
emissions reduction by 2030 and net-zero emissions
by 2050.6
Among several emissions reduction
initiatives, Vistra wanted to understand how AI might
help it run its power plants more efficiently. The
company used a multilayered neural network model,
trained on two years of plant data, to determine
optimal plant operations—for example, set points
in the control room to achieve maximum heat-rate
efficiency for any combination of external factors,
such as temperature and humidity. Once power
engineering experts validated the models, they began
to provide recommendations to operators every 30
minutes to enhance the plant’s heat-rate efficiency,
helping operators meet energy targets and improve
plant reliability. This led to a 30 percent decrease
in duct burner usage, annual fuel savings of about
$175,000, and reduced carbon emissions, ensuring
more efficient, more reliable power. The insights were
incorporated into a solution named the Heat Rate
Optimizer (HRO), which was implemented across the
company’s entire fleet, yielding $23 million in savings.
Vistra has since extended the HRO to 67 additional
power generation units across 26 plants.7
AI comprises several technologies that perform
cognitive-like tasks. For further information on underlying
technology for gen AI, please refer to the gen AI section of
the report. The technologies underlying applied AI include
the following:
— Machine learning. This term refers to models that make
predictions after being trained with data rather than
following programmed rules.
— Computer vision. This type of ML works with visual
data, such as images, videos, and 3D signals.
— Natural-language processing. This type of ML analyzes
and generates language-based data, such as text
and speech.
— Deep reinforcement learning. This type of ML uses
artificial neural networks and training through trial and
error to make predictions.
— Additional hardware tools and technologies. These
are other tools and technologies—such as cloud
computing and domain-specific architectures,
including GPUs—that improve access to high-capacity
compute for AI and ML workflows.
Key uncertainties
The major uncertainties affecting applied AI include the
following:
— Cybersecurity and privacy concerns, notably on data
risks and vulnerabilities, are prevalent: 51 percent of
survey respondents cited cybersecurity as a leading
risk in 2024.8
— Regulation and compliance might affect AI research
and applications.
— Ethical considerations—including data governance,
equity, fairness, and explainability—surround the
responsible and trustworthy use of AI.
— Operational risks may arise from AI failure modes, as
well as potential risks associated with data quality and
integrity, model drift, adversarial attacks, and the need
for ongoing training and education.
questions when moving forward with applied AI:
— How might companies identify the most beneficial
AI applications and strategically use generative and
applied AI together?
— What are the talent and tech stack implications of
adopting applied AI?
— How can companies get ahead of their competitors
and capture the value at scale associated with applied
AI (regarding either revenue or cost benefits)?
— How will companies balance AI’s potential cost savings
while integrating features to make AI trustworthy and
responsible?
— What checks should companies implement to guard
against AI-related risks associated with data privacy
and security, equity, fairness, and compliance?
5
“DigitalOwl revolutionizes medical record analysis and review with the latest release of version 4.0 of their Digital Medical Abstract (DMA),” Business Wire, January
17, 2023; “Nationwide is streamlining life underwriting process with DigitalOwl’s advanced AI technology,” LIFE&Health Advisor, June 3, 2024.
6
“An AI power play: Fueling the next wave of innovation in the energy sector,” McKinsey, May 12, 2022.
7
Ibid.
8
“The state of AI in early 2024,” McKinsey, May 30, 2024.
24

Industrializing machine learning (ML), also known as
machine learning operations (MLOps), is the process of
scaling and maintaining ML applications within enterprises.
As we progress through 2024, MLOps tools are rapidly
evolving, improving in both functionality and interoperability.
These tools are facilitating the transition from pilot projects
to robust business processes, enabling the scaling of
analytics solutions, and enhancing team productivity.
Successful industrialization of ML can help sustain solutions,
reduce the production timeline for ML applications by eight
to ten times, and decrease development resources by
up to 40 percent.1
Initially introduced by a few pioneering
companies, MLOps is becoming more widely adopted
as more companies use AI for a broader spectrum of
applications. The rise of generative AI (gen AI) has reshaped
the AI landscape, demanding a corresponding upgrade in
MLOps capabilities to service its unique demands. This is
the newest field for novel developments in the industrializing
ML trend. MLOps and foundation model operations (FMOps)
are essential for industrializing and scaling gen AI safely
and efficiently.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.1
0.2
0.3
0.4
News
Talent demand
Research
Searches
0.5
Patents
Equity
investment
Scoring the trend
Industrializing
machine learning
Scores across news, searches, publications, and
patents have more than doubled between 2019 and
2023, while demand for talent has more than tripled in
the same time frame. These increases suggest that the
use of methods for industrializing ML could expand in
the years ahead. Equity investment activity in MLOps
has dropped in two consecutive years.
sional services; Chemicals; Consumer packaged
goods; Education; Electric power, natural gas, and
utilities; Financial services; Healthcare systems and
$3 –36%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
2019
1.0
0.5
0
2023
1
Based on observations from MLOps deployment in a series of large-scale analytics transformations supported by McKinsey.
25

Latest developments
Recent developments involving industrializing ML include
the following:
— Monitoring and orchestration are becoming crucial
components of MLOps. This is particularly evident in
the complex task of upgrading enterprise technology
architecture to integrate and manage models and
orchestrate interaction between ML models and other
applications and data sources. Several integration
patterns are emerging, including those that allow
models to call APIs in response to user queries.
Recent advancements in integration and orchestration
frameworks, such as LangChain and LlamaIndex,
have greatly facilitated these developments. To
effectively integrate these models, it’s essential for
MLOps pipelines to incorporate specific performance
measurement tools. For instance, they need to assess
a model’s ability to retrieve the correct information.
Companies such as Fiddler and Databricks are actively
investing in this field. They offer performance tracking,
validation, and orchestration, enabling companies to
monitor their live operations effectively. This ensures
the smooth facilitation of their ML applications,
further emphasizing the importance of monitoring
and orchestration in the successful implementation
of MLOps.
— The use of prebuilt solutions and APIs is on the rise. In
recent years, there has been a significant surge in the
availability of machine learning APIs and preconfigured
solutions, partly due to the explosive growth of gen
AI. Accessed predominantly through APIs, gen AI
technologies—encompassing advanced tools such
as computer vision libraries and pretrained image
recognition models—have profoundly reshaped the ML
development landscape. As these APIs gain popularity
and continue to evolve, they are progressively assuming
responsibilities that were once the purview of ML
engineers, such as data preprocessing and model
training on predefined data sets. As gen AI continues
to evolve, its impact on the field is expected to expand,
making it a pivotal driver of industrializing
ML technologies.
— MLOps is important to gen AI from the get-go. It is
increasingly recognized that gen AI, including large
language models, should not be viewed as separate from
the ML ecosystem. Instead, there is a call to broaden
the scope of MLOps to incorporate gen AI. MLOps is
crucial in developing, deploying, and maintaining gen
AI solutions, allowing ML algorithms to be dispatched
quickly and effectively. By standardizing processes,
enabling version control and tracking, and integrating
shared metrics and monitoring, MLOps breaks down
organizational silos and fosters close collaboration
between data scientists, ML engineers, and operations
teams and thus is pivotal in the end-to-end life cycle
of gen AI.
‘The past several years have yielded huge advances in the
mathematics of machine learning, but the tasks of making
that math really useful have lagged. MLOps—the way that the
math gets made useful—is finally catching up. The tools and
processes are beginning to mature, but we still need additional
talent and skills to reap the benefits of machine learning.’
– Douglas Merrill, partner, Southern California
26

Demand
Job postings for roles related to industrializing ML decreased by 36 percent compared with 2022 levels,
significantly greater than the 26 percent average decrease across all technology trends. As software evolves,
many tasks will be automated, and many MLOps tasks are expected to become the responsibility of frontline
ML developers. Companies investing in industrializing ML are shifting their focus from rapid application
development to effective scaling and implementation. Monitoring is becoming a crucial component, encom-
passing performance tracking, validation, and orchestration. MLOps offerings are also growing to streamline
industrialization (for example, Databricks).
Skills availability
Companies that are industrializing their ML initiatives require advanced technological skills, and there is talent
available to meet this demand. These skills include expertise in containerization with Docker, orchestration with
Kubernetes, and proficiency in programming languages such as Python.
Software engineer
Software developer
Data scientist
Data engineer
Web developer
Systems administrator
Technical architect
0
20
40
60
80
120
100
2019 2023
1.4× 2.1× 2.9×
1.5× 1.1×
Kubernetes Docker Python Cloud
computing
DevOps
Kubernetes Docker Python Cloud
computing
DevOps
68
45 44 42
32
Machine
learning
Machine
learning
18
4.1×
27

Adoption levels of industrialized ML fall in the middle relative
to other trends, as other advancements have generated
more buzz in recent years. Some of the leading sectors in
adopting industrialized-ML practices include energy and
materials and technology, media, and telecommunications.
Additionally, financial-services companies made significant
investments in ML tools, driven by a focus on enhancing
customer satisfaction and improving decision making.
Adoption dimensions
The adoption trajectory for advanced technologies will
look different for each technology and each use case
within that technology. Advancements along the following
dimensions could enable the next level of adoption for
industrializing ML:
— greater availability of simplified tools for data
management and an increase in data source availability
and robustness in terms of data quality and volume of
data points, potentially through improved data services
— continued standardization and improvements in
underlying technologies across the ML/AI software
development life cycle (for example, model development,
deployment, and monitoring)
— organizational adoption and awareness to improve—
making the technology more broadly accessible and
understood by nontechnical employees
In real life
Real-world examples involving industrializing ML include the
following:
— Meta uses HawkEye internally to gain a comprehensive
understanding of its ML workflows.2
HawkEye
functions as a real-time monitor, anomaly detector, and
analyst for potential issues, from data quality to model
performance. It also ensures end-to-end observability
through tracing of ML pipelines, integration with
explainable AI, and the provision of debugging tools.
— MLflow, an open-source platform aimed at streamlining
ML development, is adding generative AI–centered
capabilities. For example, its prompt engineering
user interface provides an opportunity to try out
multiple large language models (LLMs), parameter
configurations, and prompts.3
2
Partha Kanuparthy, Animesh Dalakoti, and Srikanth Kamath, “AI debugging at Meta with HawkEye,” Engineering at Meta, December 19, 2023.
3
MLflow Blog, “2023 Year in Review,” blog entry by Carly Akerly, January 26, 2024.
‘Solving for gaps in automated monitoring and life cycle
management of deployed AI solutions will ensure the lasting
and scalable impact of AI. That includes continued focus on
gen AI: industrializing bespoke gen AI solutions will require
robust gen AI operational ecosystems, and we see more options
emerging for processing unstructured data, engineering and
operating LLM flows, and automating the gen AI solutions
life cycle. Continued progress on enablement of regulatory
and ethical alignment and explainability will help unlock new
areas of AI impact.’
– Alex Arutyunyants, senior principal data engineer, QuantumBlack, AI by McKinsey, Boston
28

Software solutions enable the various stages of the ML
workflow, which are as follows:
— Data management. Automated data management
software improves data quality, availability, and control
in feeding the ML system.
— Model development. Tooling is used to build and
optimize ML models, engineer features, and standardize
processes.
— Model deployment. Provision tooling helps to test and
validate ML models, brings them into production, and
standardizes processes.
— Live-model operations. With this process, software
maintains or improves the performance of models in
production.
— Model observability. These tools go beyond basic
monitoring and delve deeper into understanding a
model’s behavior. They provide insights into model
performance, identify potential biases, explain model
decisions, and help diagnose issues such as data drift or
concept drift.
Key uncertainties
The major uncertainties affecting industrializing ML include
the following:
— Up-front investment and resources will be required to
establish industrialized ML in organizations.
— Processes and accountability will be crucial for
maintaining ML solutions at an industrial scale.
— A fast-evolving market will require organizations to
balance the efficiency of using their existing vendors’
offerings with realizing value from newer offerings
provided by players outside their existing vendor
ecosystem.
— The potential for misaligned capabilities will need to be
avoided by ensuring that organizations are investing in
the right solutions and at the right levels for their specific
use case needs.
— Continuous monitoring and evaluation will be crucial for
identifying and addressing unwanted bias throughout
the ML life cycle, from initial data selection to ongoing
model performance assessments.
— Technology and talent evolution will be essential, due
to increasing automation of certain roles and the need
for workers who are skilled in building and maintaining
productionized ML systems at scale.
questions when moving forward with industrializing ML:
— With the emergence and acceleration of gen AI, how will
MLOps practices and the technology ecosystem evolve?
— With the proliferation of new technologies in ML, how
should organizations prioritize those along the ML
workflow that are most relevant to their needs?
— How will industrialized ML change organizations, their
operating models, and their engineering roles?
— As industrialized ML proliferates, how can organizations
define accountability roles to ensure the trustworthy and
responsible use of AI/ML?
— How can organizations best integrate their MLOps
efforts across machine learning, deep learning, and gen
AI models?
29

Building the
digital future
30

The landscape of software development is currently experiencing
a transformative shift, driven by an influx of cutting-edge
technologies such as generative AI (gen AI) and cloud-native
architectures. The year 2023 saw a significant rise in AI-powered
tools, building on previous years’ advancements in software
development and DevOps automation (for example, continuous
integration, continuous delivery, infrastructure as code, and
improved integrated development environments). These
innovations are revolutionizing how engineers operate throughout
the entire software development life cycle (SDLC), from planning
and testing to deployment and maintenance. These technological
breakthroughs are not only enhancing the capabilities of
engineers but also opening doors for less technical professionals
to participate in application development as complex tasks are
simplified and accelerated.
While the path to wide-scale adoption may take more time—
because of obstacles such as integration challenges, lack of
clear measurement metrics for developer productivity, and need
for large-scale retraining of developers and test engineers—an
increase in the adoption of AI-powered software development
tools is promising. Early adopters are already experiencing
productivity boosts, laying the groundwork for more widespread
adoption in the near future. This year promises even more
groundbreaking possibilities as maturing technologies like user-
friendly low-code platforms, AI assistants throughout the SDLC,
integration with gen-AI-enabled product management tools, and
scalable cloud architectures converge, leading to democratized
development, hyperefficiency, and exceptional adaptability.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.2
0.4
0.6
0.8
News
Talent demand
Research
Searches
1.0
Patents
Equity
investment
Scoring the trend
BUILDING THE DIGITAL FUTURE Score by vector (0 = lower; 1 = higher)
Next-generation
software development
A large uptick in searches, publications, patents, and
talent demand between 2019 and 2023 clearly signals
that both institutions and enterprises are seeing
long-term potential in the evolution of next-generation
software development tools. The investment climate for
this technology has seen peaks and valleys over the
past five years (the peaks reflecting a few mega deals
in some years).
Industries affected: Advanced industries;
Business, legal, and professional services;
Consumer packaged goods; Financial services;
Healthcare systems and services; Information
technology and electronics; Manufacturing; Media
and entertainment; Telecommunications
$17 –37%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
2019 2023
1.0
0
31

Latest developments
Recent developments involving next-generation software
development include the following:
— New versions of AI-powered development tools are
transitioning from proof of concept to wide-scale
application. The software development industry
has witnessed a significant turning point in the past
year with the release of new versions of advanced
AI-powered tools that are transforming the landscape.
Unlike their static and off-the-shelf predecessors,
these new versions have moved beyond the proof-
of-concept phase and now offer a higher degree of
adaptability and customization, catering to the specific
needs of individual projects. This shift is resulting in a
wider application of these tools. For instance, Tabnine,
an AI-powered auto-completion tool, has improved its
ability to understand the context of developers’ code,
leading to more accurate and relevant code completions.
Developers can now create and share custom code
templates within Tabnine, allowing them to automate
repetitive tasks specific to their projects or coding style,
thereby increasing the tool’s applicability on a larger
scale.1
— There is a growing trend toward more integrated
development platforms. Companies are moving
away from a multitude of disparate tools and, instead,
adopting a smaller number of robust or better-integrated
solutions that offer a wide range of functionalities
throughout the development life cycle. This shift
provides several advantages, including streamlined
workflows that lead to improved collaboration, reduced
context switching, and enhanced data visibility.
However, catering to diverse use cases within the
organization requires a careful selection of tools with
robust capabilities and flexibility.
— The talent landscape will undergo changes. The
availability of advanced underlying technologies, such as
gen AI, is enabling software engineers to reallocate their
time from tasks such as pure code generation to tasks
such as architecture design and problem solving. This
change is not only causing a strong mindset shift among
engineers but also influencing how companies approach
talent selection, upskilling, and onboarding. The focus
is no longer solely on the coding skills of potential
candidates. Instead, companies are investing in defining
a differentiated upskilling strategy to retain and develop
talent and are now also assessing how effectively
candidates can utilize and adapt to these advanced tools
in their day-to-day tasks.
— The focus on compliance and trust is increasing. The
software development industry is experiencing a
significant shift toward compliance and trust in response
to growing concerns about legal and security risks
associated with software tools. This past year has seen
growing attention to compliance-focused tools like
SonarQube, which provide features like code tagging,
labeling, and detection to improve transparency and
accountability. Developers are also choosing tools with
guaranteed indemnity to mitigate potential legal risks
associated with code generated or analyzed by the tool.
By prioritizing compliance and safety, the industry can
improve the quality and reliability of their software while
also reducing the risk of legal and security issues.
1
“Tabnine + Pieces for Developers is a win-win for your workflow,” Pieces for Developers, May 3, 2023.
‘These new-generation tools are now guaranteeing indemnity
for use, with the ability to detect, tag, and label code. This
metadata will make generated code easier to track and
manage. The future of tooling is likely to see consolidation
over time, with companies opting for several comprehensive
tools or tool chains instead of numerous specialized ones.’
– Martin Harrysson, senior partner, Bay Area
32

Demand
The number of job postings for next-generation software development peaked in 2022 and showed the most
job demand of all the technology trends in that year. Unsurprisingly, 2023 saw a decline, but even with a 37
percent decrease in job postings, next-generation software development still scores the highest in job demand
among the tech trends, with over 800,000 job postings.
Postings across the board have declined compared with 2022, which is in line with the layoffs seen predomi-
nantly in the technology industry. In the near future, it will be interesting to see what the impact of gen AI will
be on both the types of roles and demand for roles in next-generation software development.
Skills availability
Key talent areas for next-generation software development are focused on DevOps, continuous integration,
and cloud computing. Some skills are more plentiful (for example, DevOps and cloud computing), while others
are harder to find (for example, continuous integration).
1.5×
0.3×
1.1× 0.4×
DevOps Continuous
integration
Cloud
computing
Software
engineering
Information
technology
DevOps Continuous
integration
Cloud
computing
Software
engineering
Information
technology
Python
Python
0.4×
59 43 41 37 34
2.9×
30
Software engineer
Software developer
Data engineer
Web developer
Project manager
Systems administrator
Solution architect
Technical architect
0
250
200
150
100
50
2019 2023
33

The financial-services and technology, media, and
telecommunications sectors have emerged as leaders in
the adoption of next-generation software development.
Investments are driven by the changing compliance
landscape and the availability of more customizable tools.
Adoption dimensions
From nascent to mainstream, the adoption trajectory will
look different for each technology and even each use
case within that technology. As AI-generated code—the
most recent innovation within next-generation software
development—becomes a standard way of working in the
software development cycle, it provides an interesting
example of how the adoption trajectory could develop.
the next level of adoption:
— clear and measurable ROI of AI-generated code tools
(for example, an increase of more than 25 percent in
developer productivity for high-complexity tasks)2
— a legal framework for liability of AI-generated code
outcomes to create transparency on who bears the risk
in case of malfunctions
— an increase in the applicability of AI-generated tools that
can provide sufficient performance for most common
software development use cases
— implementation of AI-generated code as part of the
core curriculum and upskilling programs for software
developers
In real life
Real-world examples involving the use of next-generation
software development include the following:
— Citi leverages the Harness Continuous Delivery platform
to provide an integrated experience across all stages
of software delivery, with a user base of over 20,000
engineers. The platform brings together all the tools and
services involved in software delivery with the aim of
improving performance, consistency, and maintenance
across the enterprise while operating in the unique
regulatory and risk management environment that
comes with financial services. The platform helps
streamline software delivery, automating deployment,
testing, and change management after code approval.
It facilitates faster rollbacks and integrates with
observability tools for proactive issue detection and
auto-rollback if needed. This translates into increased
developer and operations control, reduced manual
effort, and enhanced security.3
— Goldman Sachs is exploring the use of gen AI tools to
assist its software developers in writing and testing
code. The tools can automatically generate lines of code,
freeing developers from repetitive tasks and allowing
them to focus on core functionalities and client needs.
2
“Unleashing developer productivity with generative AI,” McKinsey, June 27, 2023.
3
“Citi improves software delivery performance, reduces toil with Harness CD,” Harness, accessed on April 22, 2024.
‘Next-generation software development tools are
fundamentally changing the role of developers, freeing up
capacity for improved experiences and architectures, and
ultimately, greater value creation.’
– Santiago Comella-Dorda, partner, Boston
34

The technologies that power next-generation software
— AI-generated code. AI applications can go beyond code
suggestions and recommendations and also enable
users to generate entire functions, optimize existing
code, create boilerplate code, and adapt to different
programming languages.
— Low- and no-code platforms. Software development
systems, such as Microsoft Power Apps and Google
AppSheet, make it easier for nondevelopers to build
applications more quickly.
— Infrastructure as code. This is the process of configuring
infrastructure, such as a data center, with machine-
readable code, which enables rapid reconfiguration and
version control. The cloud, for example, is based on the
concept of infrastructure that is fully abstracted as code.
— Microservices and APIs. These are self-contained,
independently deployable pieces of code that can be
coupled to form larger applications.
— AI-based testing. Next-generation software can use AI
to automate unit and performance testing to reduce the
amount of time developers spend on this task.
— Automated code review. These applications use AI
or predefined rules that enable users to check
source code.
Key uncertainties
The major uncertainties affecting next-generation software
— Relying on automated testing and reviews without
having humans check the work can lead to increased
errors in software and erosion of user trust.
— The growth in the use of low- and no-code tools by
nondevelopers could be limited because experienced
developers are needed to monitor and debug
applications.
— Comprehensive monitoring and version control could
become more difficult due to uncoordinated changes
and upgrades from multiple vendors.
— Quality and security remain concerns with code
generated by AI pair programmers, particularly if they
are not regularly updated with the latest standards or are
not trained on clean, fast code.
— Addressing intellectual property, legal liability, and
potential regulations surrounding gen-AI-generated
code is essential for responsible development and
deployment.
— APIs add an extra layer of potential security
vulnerabilities that can be exploited, and their
customization can be a challenging task requiring
substantial time and effort.
questions when moving forward with next-generation
software development:
— To what extent will the development of AI-generated
code affect the day-to-day tasks and responsibilities, as
well as number, of software engineers?
— To what extent might no-code tech used by amateur
developers reduce the demand for fully trained software
development professionals?
— From a cultural standpoint, will teams—both developers
and nondevelopers—embrace or resist changes in ways
of working?
— What intellectual property issues might affect
AI-generated code?
— To what extent will business units take responsibility for
the health of applications, or will accountability continue
to rest with a shared IT function?
— Will organizations invest in the retraining needed
to enable their software teams to adapt to the fast-
changing domain?
— How do organizations upskill engineers to know what
good outputs from AI-enabled tools look like?
35

Digital trust and cybersecurity enable organizations to manage
technology and data risks, accelerate innovation, and protect
assets. Moreover, building trust in data and technology
governance can enhance organizational performance and improve
customer relationships. In this trend, we include technologies that
enhance trust (for instance, digital identity and privacy-enhancing
technologies), cybersecurity capabilities (such as identity and
access management), and Web3 (such as blockchain).
The importance of digital trust and cybersecurity is increasing
as organizations adopt emerging and maturing technologies
within their enterprises (for example, cloud and edge computing,
applied AI, and next-generation software development).1
While
the adoption of these emerging technologies comes with exciting
new benefits, it also exposes organizations to cybersecurity and
other risks, increasing the need for digital-trust technologies. The
adoption of digital trust and cybersecurity, however, has been
affected by a range of factors, including integration challenges,
organizational silos, talent shortages, and its limited consideration
as a critical component of value propositions. Capturing the full
benefit of digital trust and cybersecurity will require top-down
leadership and deliberate changes to multiple spheres of activity,
from strategy and technology to enterprise capabilities.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.1
0.2
0.3
0.4
News
Talent demand
Research
Searches
0.5
Patents
Equity
investment
Scoring the trend
BUILDING THE DIGITAL FUTURE Score by vector (0 = lower; 1 = higher)
Digital trust
and cybersecurity
The digital trust and cybersecurity market has
experienced high growth over the recent years: the
cybersecurity market growth rate in 2021 was 12.4
percent.¹ But, as with other trends affected by the
macroeconomic slowdown, the digital trust and
cybersecurity trend took a hit in 2023, compared with
2022, across dimensions such as investment and
talent demand. That said, the five-year view (2019–23)
shows robust growth across all dimensions, and as the
digitization of enterprises continues, this trend is likely
to keep gaining traction.
Aviation, travel, and logistics; Consumer packaged
goods; Education; Electric power, natural gas, and
utilities; Financial services; Healthcare systems and
Media and entertainment; Pharmaceutical and
medical products; Public and social sectors; Retail;
Telecommunications
$34 –34%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
2019 2023
1.0
0.5
0
¹Bharath Aiyer, Jeffrey Caso, Peter Russell, and Marc Sorel, “New survey reveals $2 trillion market
opportunity for cybersecurity technology and service providers,” McKinsey, October 27, 2022.
1
“The cyber clock is ticking: Derisking emerging technologies in financial services,” McKinsey, March 11, 2024.
36

Latest developments
Recent developments involving digital trust and
cybersecurity include the following:
— Managing generative AI risk and readiness has become a
key focus. The rise of generative AI (gen AI) has sparked
innovation across industries while also heightening the
focus on managing its associated risks. Key concerns
include fairness and bias, as gen AI can perpetuate
existing biases embedded in training data. To counter
these concerns, companies like IBM are creating
fairness tool kits to identify and remove bias within AI
models during the development process. Privacy issues
arise as a result of gen AI’s ability to create deepfakes,
prompting research into watermarking AI-generated
content. The potential misuse of gen AI for cyberattacks
underscores the importance of robust AI security
frameworks. Intellectual property (IP) rights over gen
AI’s creative output remain unclear, and challenges
around the explainability of gen AI’s outputs hinder trust.
President Biden’s executive order on gen AI, calling
for research into these risks and the development of
trustworthy AI standards, and the recently adopted
EU AI Act create pressure for responsible deployment
and will likely lead to the adoption of new tools, such as
those coming from emerging players like Credo AI
and Holistic AI.
— Cybercriminals and threats are evolving at a rapid rate.
Threat actors, including cybercriminals and state-
sponsored groups, are becoming more sophisticated.
Their attacks exploit new vulnerabilities (for example,
intricate ransomware that is debilitating power grids)
and aim for maximum disruption (for example, targeting
industrial control systems). Unfortunately, current
security systems and company readiness are often
not at the level needed to deal with this increased
cybersecurity risk.
— New buyers are emerging outside of the CISO role.
Responsibility for cybersecurity is expanding beyond the
office of the chief information security officer (CISO), with
cyber spend now increasingly coming from nonsecurity
business functions such as product and engineering.2
Consequently, cybersecurity providers must adapt
their strategies to use cases with a wide range of
stakeholders, including and stretching beyond the CISO
office. Improving cybersecurity maturity, increasing
efficiency, and possibly increasing the use of AI-enabled
automation remain key growth drivers.
— The ongoing debate between cybersecurity platforms
and best-of-breed solutions is evolving. Cybersecurity
platforms offer a unified environment, simplifying
management but potentially compromising on
functionality. Conversely, best-of-breed solutions offer
specialized tools but can struggle with data integration
and user experience. We see a shift as the lines
between platforms and best-of-breed solutions are
blurring, with platforms becoming more modular and
integrating best-in-class security tools. The market is at
an inflection point in the “best of breed” versus “best of
suite” debate: customers have not reached a consensus
on a preference in any segment. Smaller companies
might favor the simplicity of platforms, while larger ones
may value the customization offered by best-of-breed
solutions. The best path lies in balancing comprehensive
security with manageable complexity while considering
the company’s security maturity, IT staff skills, and
growth prospects.
— Bitcoin and Ethereum ETFs are sparking mainstream
interest. After a period marked by regulatory challenges
for crypto exchanges, multiple Bitcoin exchange-
traded funds (ETFs) have been approved. This has
effectively lowered the entry barrier, opening up the
world of cryptocurrencies to a wider audience. In
addition to Bitcoin, Ethereum ETFs are also gaining
traction. Several Ethereum ETFs are currently awaiting
approval, indicating a growing interest in diversifying
cryptocurrency investments. While these developments
have significantly influenced the digital-asset market, it
remains volatile.
— Blockchain companies are moving from piloting to
at-scale deployment of tokenized financial assets.
Tokenization, the process of creating a unique digital
representation of an asset on a blockchain network,
has started to scale after many years of promise
and experimentation. The benefits—including
programmability, composability, and enhanced
transparency—can empower financial institutions to
capture operational efficiencies, increase liquidity, and
create new revenue opportunities through innovative
use cases. However, as infrastructure players pivot away
from proofs of concept to robust at-scale solutions,
many opportunities and challenges remain to reimagine
the future of financial services.
2
A recent McKinsey survey on cyber market customers (n = 200) asked respondents, “In your best estimation, how much of your cybersecurity spend comes
from outside of your CISO organization? Where does that non-CISO cyber spend come from?”
37

Security analyst
Software engineer
Security engineer
Software developer
Data engineer
Project manager
Network engineer
0
10
20
30
40
5
15
25
35
45
2019 2023
Demand
Job postings for digital trust and cybersecurity decreased by 34 percent between 2022 and 2023. But in the
longer-term view, we saw an increase of 123 percent between 2019 and 2023. Security analyst remains the
highest-demand job for digital trust and cybersecurity, followed by software and security engineers.
Skills availability
Companies expanding their digital trust and cybersecurity initiatives have a strong demand for skills associated
with security, compliance, and risk analysis. Despite the short-term decrease, the demand for relevant skills
still generally outpaces supply (except for blockchain), and the talent gap is significant.
0.1×
0.4×
0.4× 0.4× 0.2×
4.8×
Information
technology
Computer
security
Risk
analysis
Stakeholder
management
Blockchain Identity
theft
Regulatory
compliance
Information
technology
Computer
security
Risk
analysis
Stakeholder
management
Blockchain Identity
theft
Regulatory
compliance
0.1×
38 31 23 19 17 15 14
38

The digital trust and cybersecurity trend has seen
high adoption levels among our trends, with some
subcomponents achieving widespread use, while others
remain at the forefront of innovation, such as emerging
Web3 applications. About 30 percent of survey respondents
reported that they had either fully scaled or were scaling
digital trust and cybersecurity, and more than 60 percent
mentioned they had invested in it. Financial-services
companies, in particular, have adopted this trend, driven by
a need to combat an increasing range of threats and meet
regulatory requirements.
Telecommunications, media, and technology companies
are also at the forefront of adopting digital trust and
cybersecurity. This is likely because they are leading the way
in enhancing security measures, particularly in the realm of
AI, and developing effective tools to address the constantly
evolving threat landscape.
Companies of any size need to consider how to optimize
their defenses as cyberthreats and regulatory and customer
pressures increase.
Adoption dimensions
The adoption trajectory for advanced technologies
varies for each technology and each use case within that
technology. Advancements along the following dimensions
could enable the next level of adoption for digital-trust and
cybersecurity technologies:
— new digital identity systems integrated and scaled into
existing personal-identification processes
— enhanced integration of advanced technologies into
existing cybersecurity frameworks, including upgrading
midmarket companies’ defenses
— strong protection mechanisms to ensure user privacy
and control of personal data
— improved government and public perception of the
benefits and risks of digital identities
— security capabilities to meet varying regulatory
requirements, ensuring compliance and fostering trust
— innovative applications with tangible real-world
implications for Web3 to continue expansion beyond
decentralized finance as practical applications emerge
across various sectors—for example, the decentralized
physical infrastructure network (DePIN), still in its early
stages, which aims to enable cell phones to function on a
decentralized network
In real life
Real-world examples involving the use of digital trust and
— Salesforce built its Einstein Trust Layer specifically to
address security concerns about using large language
models (LLMs) within the Salesforce platform. This
innovative system acts as a secure intermediary for
Salesforce users interacting with LLMs. The Einstein
Trust Layer ensures data confidentiality and privacy by
masking personally identifiable information (PII) before
it is used as input for the LLM and by adhering to a zero-
retention architecture, meaning none of the Salesforce
data is stored outside the platform or used to train
the LLM itself. Additionally, the Trust Layer monitors
outputs for inappropriate content and streamlines
communication between the user and the LLM.
— Cisco created a customer-facing trust portal called
the Cisco Trust Portal. This self-service tool provides
customers with on-demand access to a wide range of
documents related to security, trust, data protection, and
privacy compliance. The purpose of the Trust Portal is to
assist customers in gaining a deeper understanding of
Cisco’s security measures and evaluating the security of
its offerings.
‘Digital-trust technologies are the cornerstone of value
creation in the age of gen AI. By embedding robust
security, privacy, and ethical frameworks into AI systems,
organizations not only protect their assets but also build
a foundation of trust that drives innovation, enhances
customer loyalty, and unlocks new opportunities for
sustainable growth.’
– Liz Grennan, partner, Stamford
39

— Skyflow offers a platform called the Skyflow Data
Privacy Vault, designed to help companies manage,
protect, and utilize sensitive data while ensuring
compliance and privacy.3
It acts as a secure central hub
for sensitive data, isolating it from other systems and
encrypting it with advanced techniques. Despite strong
security, Skyflow’s secure APIs still allow users to utilize
this data for workflows, sharing, or analysis—all without
ever decrypting the original information.
— Franklin Templeton launched the Franklin OnChain
U.S. Government Money Fund (FOBXX), the first
US-registered mutual fund to use a public blockchain
to process transactions and record share ownership.
Toward the end of March 2024, it had exceeded
$330 million in assets under management. The fund
is primarily issued on the Stellar public blockchain
and Polygon. Franklin Templeton has also announced
plans to issue tokens on other blockchains, including
Avalanche and Aptos.4
— French banking giant Société Générale completed its
first tokenized green-bond issuance on the Ethereum
network, reflecting the growing interest in real-
world-asset tokenization among traditional financial
institutions. The bank’s digital-asset-focused division,
SG-FORGE, registered the issuance of digital green-
bond tokens valued at €10 million ($10.8 million).
These security tokens were purchased by two major
institutional investors, AXA Investment Managers and
Generali Investments, through a private placement.
— Citibank has developed a token service using blockchain
technology to offer digital-asset solutions for its
institutional clients.5
The new service, called Citi Token
Services, converts clients’ deposits into digital tokens,
facilitating immediate cross-border payments, liquidity,
and automated trade finance solutions around the
clock at virtually no cost. As an integral part of the
bank’s Treasury & Trade Solutions, Citi Token Services
aims to integrate these tokenized deposits into Citi’s
global network, thereby strengthening its core cash
management and trade finance functions.
Digital-trust and cybersecurity technologies include the
following:
— Zero-trust architecture. This IT security design concept
assumes an organization’s network is compromised by
default and, therefore, enforces access decisions for
every interaction with every entity.
— Digital identity. An identity consists of all the digital
information that characterizes and distinguishes an
individual or an entity. With self-sovereign identity,
users control which identifying information to share
and with whom. Passwordless identity allows users to
verify and authenticate themselves not with traditional
alphanumeric passwords but with alternatives such as
biometrics, devices and applications, and documents.
Businesses are developing “converged identity”
solutions, which bring together different dimensions of
identity into a single platform, enabling, for example,
continuity as a person shifts from employee to business
partner to customer.
— Privacy engineering. This practice governs the
implementation, operations, and maintenance of privacy
by design. It focuses on the strategic reduction of
privacy risks, enabling purposeful decision making about
resource allocation and effective implementation of
privacy controls in information systems.
— Explainable AI. This AI model covers methods and
approaches that increase the transparency and
interpretability of the inputs, weightings, and reasoning
of machine learning algorithms, thus enhancing trust and
confidence in them.
— Technology resilience. Technology resilience is the sum
of practices and technical foundations necessary to
architect, deploy, and operate technology safely across
an enterprise environment. It includes components such
as immutable backup and self-healing networks. Such
capabilities help organizations identify and overcome
challenges such as latency, outages, or compromise
and have the dual goal of reducing the likelihood of
technology risk events and enabling faster recovery if a
technology risk event does occur.
3
Manish Ahluwalia, “What is a data privacy vault?,” Skyflow, June 23, 2022.
4
“What is Web3?,” McKinsey, October 10, 2023.
5
“Citi develops new digital asset capabilities for institutional clients,” Citibank press release, September 18, 2023.
40

— Blockchain. This is a digitally distributed, decentralized
ledger that exists across a computer network and
facilitates the recording of transactions.
— Smart contracts. Established in immutable code on a
blockchain, these software programs are automatically
executed when specified conditions (such as terms
agreed on by a buyer and seller) are met.
— Digital assets and tokens. These digitally native
intangible items include native cryptocurrencies,
governance tokens, stablecoins, nonfungible tokens
(NFTs), and tokenized real-world and financial assets,
including cash.
— Decentralized applications. These applications operate
on peer-to-peer networks, removing dependence
on centralized servers. They leverage blockchain
technology for data storage and security, often utilizing
cryptocurrencies for transactions and user engagement.
Key uncertainties
The major uncertainties affecting digital trust and
— Implementation complexity is significant, given resource
requirements, talent scarcity, inadequate funding, lack
of shared taxonomies and aligned risk frameworks,
coordination challenges across multiple parties, and
required shifts in organizational norms and practices
needed to achieve effective deployments.
— Compatibility challenges will be encountered when
updating or migrating technologies and integrating them
with legacy systems or with an abundance of fragmented
point solutions.
— Lack of standardization and widely accepted best
practices for how or when to use trust architecture
techniques across industries will continue to be
a challenge. Additionally, differences in national
cybersecurity regulations necessitate changes to local
company policies.
— Tensions between privacy and fairness or privacy and
safety can arise. An example might be tension between
the avoidance of an excessive collection of demographic
data and the need for that data to assess and mitigate
bias or spot harms against minors.
— Geopolitical tensions may lead to increased cyber
risk. Organizations should adopt comprehensive and
adaptive cybersecurity strategies to mitigate these
risks and ensure resilience when navigating geopolitical
uncertainty.
— Regulatory landscapes for blockchain and tokenization
remain fragmented and under development across
various jurisdictions, posing significant challenges for
compliance. While some regions have begun to establish
comprehensive frameworks, such as the EU’s Markets in
Crypto-Assets (MiCA) regulations, the United States and
other countries are still navigating through legislative
processes with bills like the Blockchain Regulatory
Certainty Act. Therefore, it is wise to continuously
monitor these evolving regulations to adapt and ensure
compliance.6
— The path to explainability is unclear. There is no one-
size-fits-all approach to open up the black box of
large AI models to provide meaningful explanation for
outputs. These need to be tailored to context and data.
However, more efficient tooling and new approaches to
explainability create hope for future improvement.
— Businesses have doubts about data usage. Many
companies are worried about their confidential data
being used to train LLMs, leading to data and IP leakage.
This can cause them to default to more expensive
solutions requiring in-house training. Meanwhile, to
alleviate these fears, vendors are offering stronger
commitments to data protection, and some are offering
various forms of indemnity against IP claims.
6
“Tokenization: A digital-asset déjà vu,” McKinsey, August 15, 2023.
‘In a world where information is digital, connected,
and widely accessible, cybersecurity forms the very
bedrock of trust through which competitive advantage
can be accelerated. But it must be designed, built, and
implemented in the right way to realize the benefits.’
– Justin Greis, partner, Chicago
41

— Many executive leaders are beginning to recognize the
importance of integrating digital-trust measures (such
as security, resiliency, explainability, and privacy) as core
product functionality that should be considered from
the start of a product life cycle. This lack of prioritization
at the inception of AI-powered products can be driven
by the perception of ROI on these measures, with
doubts raised by concern that measures could lower
value creation (or increase the erosion of value). On the
other hand, some leaders are finding that investment in
trust accelerates adoption and value capture and, thus,
increases ROI.
— The value proposition and user experience of Web3
compared with incumbent systems (which are also
continuing to evolve) are often not fully understood. Even
as platforms such as Reddit and Discord are beginning
to experiment with Web3 solutions, the benefits remain
unclear to many consumers and enterprises.7
— Consumer protection is increasingly becoming a focal
point for regulators, especially amid recent failures
of several nascent Web3 projects and fraud at major
cryptocurrency exchanges.
questions when moving forward with digital trust and
cybersecurity:
— How do organizations manage higher customer,
employee, and community expectations for security,
experience (for example, frictionless log-in), and privacy
by design?
— How will regulators reconcile past standards governing
data privacy, data permanency, and other issues with the
capabilities and requirements of new trust technologies?
How can regulators be increasingly proactive in a rapidly
evolving threat and technology landscape filled with
complexity?
— How can companies manage the costs of reporting with
regulators increasing the expectations for proactive
cybersecurity risk management?
— What are the most critical systems and data types,
and where are organizations typically exposed to risk?
How can organizations be comfortable that they are
sufficiently protected in line with the organization’s risk
appetite, especially as the attack surface is expanding,
data is flowing out to many cloud surfaces, and the use
of contract workers is becoming more prevalent?
— How can organizations embed leading concepts such as
“zero trust” into all developments in their digital-portfolio
architecture to future-proof security?
— Which Web3 business models and value chains
will emerge as technically reliable, scalable, and
commercially viable? What will unlock mainstream
adoption?
— How will Web3 ecosystems coexist and interconnect
with today’s enterprise system architectures and
hyperscaled Web2 platforms?
7
“What is Web3?,” McKinsey, October 10, 2023.
‘Tokenization enhances transparency, composability, and
programmability, enabling financial institutions to improve
operational efficiencies, increase market liquidity, and create
new revenue opportunities. Tokenized financial assets issued
on blockchain are advancing from pilots to live, at-scale
deployments, emphasizing the need for companies to advance
their capabilities to stay ahead. Rising user awareness and
investor demand will further accelerate this trend.’
– Matt Higginson, partner, Boston
42

Compute and
connectivity
frontiers
43

Advanced-connectivity technologies can potentially
revolutionize the experiences of consumers and industries
such as mobility, manufacturing, and agriculture.
Organizations have been widely adopting proven
technologies to enhance their connectivity infrastructure,
but they have been more hesitant to invest in some of the
latest connectivity technologies because of unclear ROI.
However, with cutting-edge technology—such as the latest
generation of satellite connectivity, private 5G networks,
and eventually 6G—progressing rapidly, telcos and other
enterprises must prepare to reap the full benefits of these
innovations. An increasingly connected world will require
businesses to think through their strategies, investments,
and business models to identify and unlock new growth
opportunities.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.4
0.6
0.8
News
Talent demand
Research
Searches
1.0
Patents
Equity
investment
Score by vector (0 = lower; 1 = higher)
2019 2023
Scoring the trend
COMPUTE AND CONNECTIVITY FRONTIERS
Momentum for advanced connectivity highlights the
significant innovation and growth in the sector, driven
by the substantial investment made during the early
2020s. Although investments declined this year,
underlying drivers, including steady growth in interest
and innovation, highlight the continued excitement
about advanced connectivity’s potential.
Agriculture; Automotive and assembly; Aviation, travel,
and logistics; Construction and building materials;
Electric power, natural gas, and utilities; Financial
services; Healthcare systems and services; Information
technology and electronics; Media and entertainment;
Manufacturing; Metals and mining; Oil and gas; Retail;
Telecommunications
$29 –24%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
1.0
0
44

Latest developments
Recent developments involving advanced connectivity
— Telecommunications industry struggles continue. The
telecom industry faces ongoing pressure to invest
in 5G despite seeing limited gains in incremental
revenues from the technology.1
These players continue
to forge partnerships with next-generation technology
companies, such as satellite connectivity providers, in
hopes of driving innovation within their industry and
overcoming their financial challenges.
— Adoption of private networks progresses. Private-
network adoption, specifically with 5G, has increased,
with industries such as manufacturing, oil and gas,
and mining seeking out the potential latency and
security benefits. While the pace of adoption is modest,
enterprises are beginning to pilot and subsequently
implement this technology, with telecom players, OEMs,
and cloud providers also building out the supporting
infrastructure.
— 6G continues to develop, but some questions remain.
Progress is being made through research with public–
private partnerships, regulatory preparation, and
standardization, but there are outstanding questions
about the road to monetization and adoption. It is still
unclear whether the value added by 6G to certain
applications will outweigh the associated costs and
whether telecom companies will be able to successfully
monetize this new innovation. In any case, securing
external investments, critical infrastructure, and the
right talent mix will be essential to navigating toward a
6G future. Another source of uncertainty is that adoption
speed will largely depend on spectrum cost and sharing
regulation.
— Adoption of xRAN in mobile networks is emerging. In
2023, large partnerships formed to build out radio-
access-networks (xRAN) infrastructure. The technology
makes use of the development of xRAN—that is, open
interfaces (oRAN), virtualization of network functions
(vRAN), and centralization of control (cRAN)—to
enhance the flexibility and interoperability in the design
and operation of wireless networks.3
Both vRAN
and oRAN enable a potential shift away from tightly
integrated hardware and software components from
single vendors to open interfaces and standardization
of components, increasing flexibility to select services
from a wider range of vendors.
— Deployment of LEO satellite constellations is advancing.
Low-Earth orbit (LEO) is the orbit range for satellites
that is closest to Earth. LEO satellite constellations offer
wide-area coverage with significantly reduced latency
compared with existing satellite offerings. They can be
used to provide coverage in remote areas, as well as to
provide connectivity for mission-critical applications.
SpaceX-owned Starlink currently has more than 6,000
satellites and aims to expand their fleet to tens of
thousands of satellites.4
‘Transforming the technology architecture of networks to
unlock cloud-like scalability, enable [generative] AI and its
business impact, and drive platform capabilities will be crucial
for connectivity players. On that journey, new ways to monetize
will emerge—for example, through 5G stand-alone and network
APIs—which will be important to pave the way for the next
generation of RAN and broadband.’
– Martin Wrulich, senior partner, Vienna
1
Shamik Bandyopadhyay, Pallav Jain, Jeremy Leing, and Stefan Prisacaru, “Navigating the three horizons of 5G business building,” McKinsey, February 22, 2023.
2
Zina Cole, Tomás Lajous, Fabian Queder, and Martin Wrulich, “Shaping the future of 6G,” McKinsey, February 28, 2024.
3
Gerardo de Geest, Gustav Grundin, Ole Jørgen Vetvik, and Nemanja Vucevic, “Telecom networks: Tracking the coming xRAN revolution,” McKinsey, February 24, 2023.
4
Elizabeth Howell and Tereza Pultarova, “Starlink satellites: Facts, tracking and impact on astronomy,” Space.com, May 30, 2024.
45

Electronics technician
Software engineer
Network engineer
Software developer
Project manager
Fiber technician
Field technician
2019 2023
0
2
4
6
8
12
10
2.0×
0.7×
0.4×
2.5×
0.6×
Telecom-
munications
Information
technology
Internet
of Things
Electronics Construction
Telecom-
munications
Information
technology
Internet
of Things
Electronics Construction
0.7×
29 23 18 14 12 3
Network
engineering
Network
engineering
Demand
To leverage advanced connectivity at scale, companies require technical talent, including specialized
engineers. In the context of advanced-connectivity-related job postings, the rising demand for technicians and
declining job postings for roles like software and network engineers highlight the industry’s focus on maintain-
ing existing infrastructure and thoughtfully expanding their digital capabilities.
Skills availability
Advancements in connectivity require skills such as network engineers for both wireline and wireless
technology. Skills such as telecommunications and network engineering currently have sufficient supply
relative to demand. This could potentially result from the expansion of networks into the greater technology
ecosystem, allowing telecom players to tap into a larger talent pool for network engineers shared with other
traditional tech companies.
46

Advanced connectivity is one of the top five most adopted
trends and is driven by the growth of technologies such
as the Internet of Things (IoT), which rely on advanced-
connectivity capabilities.
Outside of the technology, media, and telecommunications
sector, financial-services and energy and materials
companies have emerged as leaders in the adoption of
advanced-connectivity tools, with many companies in
these industries reporting that they are scaling or fully
implementing the technology.
Adoption dimensions
The adoption trajectory of advanced technologies, such as
5G private networks, varies for each technology and each
use case within that technology. Advancements along the
following dimensions could enable reaching the next level
of adoption:
— additional software applications to promote
interoperability with existing enterprise infrastructure
(for example, digital twins leveraging 5G capabilities)
— clarification of spectrum licensing regulation to advance
adoption for bands reserved for private networks in
high-traffic areas
— reduction in installation costs and proliferation of 5G
end points to allow small and medium-size enterprises to
implement the technology feasibly
— enterprise-level holistic vision of all the use cases that
require 5G over other connectivity technologies (such
as Wi-Fi), along with investment in scaling the use cases
with demonstrated proof of concept
In real life
Real-world examples involving advanced connectivity
— In January 2024, AT&T, Google, and Vodafone were
involved in a strategic $155 million investment in AST
SpaceMobile. The investment is intended to fund a
“direct-to-smartphone connectivity constellation” that
will allow wireless users to stay connected outside of cell
tower coverage.5
— Verizon partnered with Allegiant Stadium, host of
2024’s Super Bowl, to provide private 5G networks for
coach-to-coach communications and 5G nodes for
spectators. The company estimated that about half
of the approximately 61,000 spectators were Verizon
customers. They used 52 terabytes of data, up
10 percent from the prior year.6
— AT&T announced that it will begin deploying oRAN in
collaboration with Ericsson, aiming at a commercial-
scale deployment of the technology to increase the
interoperability of its infrastructure. AT&T plans to spend
approximately $14 billion over the five-year contract with
Ericsson, with 70 percent of its wireless network traffic
to pass over open platforms by 2026.7
The noteworthy technologies in advanced connectivity
— Optical fiber. Physical strands of glass provide the most
reliable high-throughput, low-latency connectivity.
— Low-power wide area networks. These wireless
networks (for example, narrowband IoT, LoRa, and
Sigfox) can cover large areas more efficiently while being
energy efficient at the end points, focused particularly
on providing connectivity for Internet of Things.
— Wi-Fi 6 and 7. Next-generation Wi-Fi offers higher
throughput, more controllable quality of service, and a
cellular-like level of security.
— 5G and 6G cellular. These next-generation cellular
technologies provide high-bandwidth, low-latency
connectivity services with access to higher-spectrum
frequency bands capable of handling a massive
amount of connected end points, as well as low-power
connectivity suitable for IoT.
— High-altitude platform systems (HAPS). These are radio
stations located at a fixed point 20 to 50 kilometers
above Earth. HAPS can be deployed on lightweight
aircraft to provide flexible capacity and access in
remote locations.
— Direct-to-handset satellite connectivity. Partnerships
between telecom companies and satellite players allow
direct access from phone to satellite, expanding network
coverage beyond the reach of traditional cellular towers.8
5
Jason Rainbow, “Google and AT&T join $155 million AST SpaceMobile investment,” SpaceNews, January 19, 2024.
6
“Verizon customers used 52.34 TB of data in and around Allegiant Stadium for Super Bowl LVIII,” Verizon press release, February 12, 2024.
7
“AT&T to accelerate open and interoperable radio access networks (RAN) in the United States through new collaboration with Ericsson,” AT&T press release,
December 4, 2023.
8
Ivan Suarez and Calil Queiroz, “The coming era of satellite direct-to-handset connectivity,” Via Satellite, November 28, 2022.
47

— Internet of Things. This is a collective network of
connected physical devices with sensors and processing
capabilities to digitally monitor or control the physical
objects.
— Low-Earth-orbit satellites. A constellation of satellites
in orbits at relatively low altitudes above Earth’s surface
can enable connecting remote or inaccessible locations
with high-speed internet, in addition to other use cases,
such as satellite imaging.
Key uncertainties
Key uncertainties of advanced-connectivity adoption vary
by technology:
— Telco profitability is being strained as a result of
competitive pricing, the commoditization of connectivity,
challenges in monetizing better network quality, and the
increasing traffic and deployment costs, all of which have
led to challenging ROI. While advanced connectivity
undoubtedly creates value, the connectivity layer does
not currently capture enough to sustain investment.
— The availability of mature use cases, such as 5G-enabled
robotics and gaming on the go, caters to both industrial
verticals and consumers requiring higher-service-level
agreements, such as high throughput or low latency.
— Ecosystem maturity plays a critical role in the adoption
of IoT, whose uptake has been slower than expected as a
result of a highly fragmented market, security concerns,
interoperability, complex deployments involving a vast
assortment of players, and a lack of standardization.
For 5G and 6G, telecom operators’ monetization
struggles might affect their ability to build the necessary
infrastructure for at-scale rollouts globally.
— Government involvement is still unfolding and will
play a role in regulations and funding for 5G and next-
generation digital infrastructure. Currently, many
governments are involved in the supply factors, while
developing demand-side use cases remains the
exception.
questions when moving forward with advanced connectivity:
— What fundamental shifts does the telecom industry need
to make to improve profitability?
— How will changes in the fiber market (for example,
financing and delayering) affect network options for
customers? Will 5G be sufficiently monetized?
— What are the expectations from various stakeholders for
next-generation wireless technologies?
— What will 6G look like? What needs to happen
technologically and financially for network equipment
players, telecom companies, enterprises, and chip
manufacturers to invest in and monetize 6G?
— Will private-network adoption take off? What do
industrial verticals need to know about it to avoid
missing out on its benefits?
— Will there be an oversupply of satellites as providers
ramp up LEO launches and benefit from technological
advancements?9
‘Advanced connectivity enabled an array of industrial use
cases and transformed the day-to-day lives of people across the
globe. Creating value for investors is something the telecom
industry was struggling with quite a bit, as a large share of
extra profits is going to the tech players sitting on top of the
advanced telecom networks.’
– Zina Cole, partner, New York
9
Chris Daehnick, John Gang, and Ilan Rozenkopf, “Space launch: Are we heading for oversupply or a shortfall?,” McKinsey, April 17, 2023.
48

Immersive-reality technologies allow users to experience
an augmented form of our world or virtual worlds while
uncovering a series of new use cases for consumers and
enterprises alike. These technologies simulate the addition
of objects to real-world settings and enable interactions
in virtual worlds by using spatial computing to render
the physical space around users. Industry players have
taken different approaches that range the spectrum from
augmented reality (AR) to mixed reality (MR) to virtual
reality (VR). The year 2023 saw tenuous investment and
consumer demand, with start-up funding decreasing by
roughly 50 percent1
and sales of VR headsets down by 40
percent from 2022.2
Some notable highlights, such as the
launch of Apple’s Vision Pro headset and continued interest
from enterprises in digital-twin technology,3
demonstrate
resilience despite financial and market hurdles.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.2
0.1
0.3
News
Talent demand
Research
Searches
0.4
Patents
Equity
investment
Scoring the trend
Immersive-reality
technologies
Scores across news, searches, publications, and
patents saw increases between 2019 and 2023. These
increases suggest that companies perceive long-term
potential in the development of immersive-reality
technologies. Continued increases in innovation and
interest indicate technological progress and explora-
tion of a broader set of use cases for the technology,
such as for consumer engagement and digital twins.
Automotive and assembly; Aviation, travel, and
logistics; Construction and building materials;
Consumer packaged goods; Education; Electric
power, natural gas, and utilities; Financial services;
Healthcare systems and services; Information
technology and electronics; Media and entertain-
ment; Real estate; Retail
$6 –36%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
2019 2023
1.0
0.4
0
1
Joanna Glasner, “Startup investors have fled the metaverse,” Crunchbase, January 16, 2024.
2
Jonathan Vanian, “VR market keeps shrinking even as Meta pours billions of dollars a quarter into metaverse,” CNBC, December 19, 2023.
3
Digital twins can be used in non-AR or non-VR contexts, but the use cases for digital twins in this publication relate to AR and VR. For additional reading, see
Roberto Argolini, Federico Bonalumi, Johannes Deichmann, and Stefania Pellegrinelli, “Digital twins: The key to smart product development,” McKinsey,
July 31, 2023.
49

Latest developments
Recent developments involving immersive-reality
technologies include the following:
— The headset market is experiencing selective growth.
Over the past year, we have seen standout headset
launches from Apple and Meta,4
with the Vision Pro and
Quest 3, respectively. While a significant number of
units of the Vision Pro were sold at launch, several other
companies are postponing the release of their headsets
and deferring investments in hardware development.
However, despite some delays, hardware development
is expected to continue. The demand seems to indicate
the proportion of consumers that are willing to accept
the size and comfort of today’s headsets for immersive-
reality experiences at current price points.
— Virtual worlds are expanding beyond their gaming
roots. Virtual worlds such as Roblox5
and Fortnite6
are
increasingly offering digital events, such as concerts,
that allow users to engage with the programs more as
digital experiences than games. This has led many third
parties to form partnerships or acquire stakes in these
platforms, such as LEGO’s partnership with Fortnite,
which gives consumers a new way to engage with LEGO
products and increases their brand recognition.7
— Enterprise adoption persists, but scaling is taking longer
than expected. VR use cases persist in enterprise
adoption, though at-scale adoption is taking longer than
expected. For AR, additional advances are required to
see significant enterprise and consumer adoption. One
area that has seen increased implementation is digital
twins (a digital representation of a physical object,
person, or process, contextualized in a digital version
of its environment to simulate real situations and their
outcomes). Digital twins have a projected CAGR of
approximately 30 percent over the next four years and
have been used in combination with AR for use cases
including manufacturing and training.8
4
“Meta Quest 3 coming this fall + lower prices for Quest 2,” Meta, June 1, 2023.
5
“How Roblox is setting the stage for more and more concerts,” Forbes India, July 9, 2021.
6
Gene Park, “The future of events is uncertain. ‘Fortnite’ is forging ahead anyway,” Washington Post, September 11, 2020.
7
“Build, play, survive: The LEGO Group and Epic Games unveil LEGO Fortnite,” LEGO, December 2, 2023.
8
Mohammad Hasan, “Digital twin market: Analyzing growth and emerging trends,” IoT Analytics, November 15, 2023.
‘Virtual-reality and augmented-reality experiences are
poised to reshape our lives in the coming decade, with
innovation driving advancements across both enterprise
and consumer use cases. Recently, we have seen growth in
B2B applications of immersive-reality technologies such
as spatial computing—specifically digital twins gaining
traction for training, testing, and design in industrial
sectors such as aerospace and defense. On the consumer
side, virtual- and augmented-reality experiences are
reshaping consumer engagement by offering immersive
brand interactions.’
– Eric Hazan, senior partner, Paris
50

Demand
Immersive-reality job postings have doubled since 2020, but a decline in talent demand in 2023 indicates that
the job market is recalibrating itself as the use cases and support structure for this area evolve.
The field represents the nexus of technology, art, and business management, with high job demand for graphic
designers, project managers, and mechanical, software, and design engineers.
Skills availability
For the most part, talent with the skills required is available, with the exception of software engineering talent,
which is less plentiful for immersive reality.
Mechanical engineer
Software engineer
Software developer
Design engineer
Web developer
Project manager
Electrical engineer
Graphic designer
2019 2023
0
2
4
6
8
12
10
Virtual
reality
Augmented
reality
Mechanical
engineering
Software
engineering
Product
design
Graphic
design
Virtual
reality
Augmented
reality
Mechanical
engineering
Software
engineering
Product
design
Graphic
design
19 18 15 12 11 5
1.1× 1.1× 1.0× 0.4× 1.0×
8.4×
51

Immersive reality falls in the middle for adoption level
relative to other trends. Most companies that are adopting
immersive-reality technologies are using them to increase
the value of existing offerings through integration with the
technologies.
The technology, media, and telecommunications sector
has emerged as a leader in adopting immersive-reality
technologies, with the highest percentage of companies in
the industry scaling or fully scaling the technology. Capital
investment has slowed, in line with the macro environment
and partially as a result of companies narrowing the
technologies they highly fund.
Adoption dimensions
The adoption trajectory for advanced technologies varies for
For example, consider the potential adoption trajectory for
AR headsets. Advancements along the following dimensions
could enable the next level of adoption:
— decreases in battery size (that is, battery density of more
than approximately 400 watt-hours per kilogram to
construct a small enough battery), headset weight, and
heat management
— a larger optical field of view (that is, more than 90
degrees) to enhance immersive experiences
— increased computing power to enable seamless
rendering and complex workloads either on the AR
device or through external sources
— increased immersive display brightness (that is, more
than 3,000 nits9
) to allow for outdoor usage
— customer ecosystems to develop a library of use cases
for integrating the technology into consumer lifestyle or
business operations
In real life
Real-world examples involving immersive-reality
— Disney acquired a $1.5 billion stake in Epic Games
and announced a collaboration between the two
companies to build a new virtual entertainment universe.
Consumers in the virtual world will be able to engage
with characters and stories in the Disney universe not
only through games but also through shopping and
media experiences.10
— Porsche announced a partnership with Meta to use its
Quest 3 MR headsets to facilitate event presentations
and vehicle walk-throughs. This will allow multiple people
to move around and interact in a shared VR environment
during product showcases.11
— In September 2023, Mercedes-Benz became one of the
first automakers to implement digital-twin technology on
Nvidia’s Omniverse platform. Digital twins of its factories
and assembly lines in more than 30 locations will allow
factory planners to optimize and streamline production
line layouts.12
— Following Apple’s launch of its new AR headset, Apple
Vision Pro, in February 2024, several companies have
begun leveraging the technology to give consumers a
more immersive product experience. Lowe’s recently
launched Lowe’s Studio Style, an app that allows
customers to design kitchen renovations inside 3D
AR kitchen models, while the PGA launched PGA Tour
Vision, which lets golf fans follow play and virtually walk
the course on some of the tour’s most iconic events.13
Immersive-reality technologies include the following:
— Augmented reality. AR enables partial immersion by
adding information to real-world settings.
— Virtual reality. VR immerses users in entirely virtual
settings.
— Mixed reality. MR enables a level of immersion between
AR and VR, adding virtual elements to the real world so
that users can interact with both.
— Spatial computing. This type of computing uses the
perceived 3D physical space around the user as a canvas
for a user interface.
— On-body and off-body sensors. These sensors—
embedded in handheld or wearable devices or
mounted around users—detect objects and bodies for
representation in virtual settings.
9
A nit is the unit for the amount of light that passes through the area of a solid angle.
10
“Disney and Epic Games to create expansive and open games and entertainment universe connected to Fortnite,” Disney, February 7, 2024.
11
“Porsche partners with Meta to demonstrate metaverse potential,” XR Today, January 22, 2024.
12
NVIDIA Blog, ” Virtually incredible: Mercedes-Benz prepares its digital production system for next-gen platform with NVIDIA Omniverse, MB.OS and generative AI,”
blog entry by Mike Geyer, September 20, 2023.
13
Cathy Hackl, “How early-adopter companies are thinking about Apple Vision Pro,” Harvard Business Review, February 9, 2024.
52

— Haptics. These feedback devices convey sensations to
users, usually as vibrations.
— Location-mapping software. This software integrates
real-time user physical location and surroundings into
AR to provide an overlay of the surrounding physical
environment in the virtual environment.
Key uncertainties
The major uncertainties affecting immersive-reality
— Hardware and software improvements, particularly for
AR devices, are needed to enable miniaturization and
weight reduction, make devices more durable, improve
sensor precision, increase user comfort, enhance heat
management, and extend battery life.
— The pace and level of cost reductions remain uncertain
but will be needed to make applications more consumer-
friendly and scalable.
— Growth in the breadth of user needs is still in question.
While a version of immersive reality exists today, a
true tipping point where demand grows from targeted
niche needs to broader mass-market customer usage
is likely a few years away. Certain business-focused
considerations, including how end-consumer price
points evolve, will also affect the pace of adoption.
— Mitigating security and privacy concerns related to
tracking user behavior will be critical to building trust.
— Safety concerns need to be addressed when
considering the usage of vision-limiting AR and VR
platforms outside of highly controlled environments.
— Proliferation of form factors. End-user devices take
multiple forms depending on intended usage, from
independent AR and VR platforms to peripheral AR
accessories for mobile phones. The proliferation of
multiple form factors is creating uncertainty in terms of
what specific use cases each one is most appropriate for.
questions when moving forward with immersive-reality
technologies:
— What is the potential impact of use cases in various
settings (for example, home, workplace, commuting)?
— How and how quickly will device hardware evolve?
— How will immersive reality shift the new wave of remote
and hybrid work and the human–machine interface?
— How will enterprises effectively manage the tech
infrastructure required for new and evolving use cases?
— What regulatory frameworks are needed to ensure the
safety, security, and ethical use of VR technologies,
including content moderation, data privacy, and
cybersecurity?
‘As widely expected, the near frenzy around immersive-
reality technologies has mostly settled. While many global
start-ups may feel the funding crunch, the committed
investors and builders that remain will continue on their
concrete product development and commercialization road
maps over a more measured five-to-ten-year horizon.’
– Hamza Khan, partner, London
53

Enterprises are in the process of transitioning away from
traditional on-site storage and management toward
distribution across multiple infrastructure points that
range from remote hyperscale data centers to on-site
servers at the edge of the business. The public cloud
allows enterprises to host workloads remotely and scale
the consumption of computing and storage resources on
demand, resulting in better economies at scale, flexibility,
and speed of deployment of applications. With edge
computing, organizations can process data much closer
to where the data originate, providing lower latency,
lower data-transfer costs, and increased data privacy
compared with the cloud (while adhering to data residency
laws as well). Cloud and edge computing has amplified AI
capabilities for both training and inferencing on foundational
models and will continue to be a major driver for the
adoption of these technologies. Balancing workloads across
cloud and edge (and the locations in between) will allow
enterprises to optimize resourcing, latency, data privacy, and
security at scale and, in turn, unlock business value.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.2
0.1
0.3
News
Talent demand
Research
Searches
0.4
Patents
Equity
investment
Scoring the trend
Cloud and edge
computing
Investment scores have increased since 2019, with
significant funding during the peak years of 2020–22.
Overall innovation scores (patents and publications)
have trended upward since 2019. For interest scores,
the data reveal that searches are growing while news
searches stayed steady. These factors speak to a trend
of leveraging early 2020s investments to rapidly deploy
new innovations.
logistics; Business, legal, and professional services;
Chemicals; Electric power, natural gas, and utilities;
Manufacturing; Media and entertainment;
Pharmaceuticals and medical products; Retail;
$54 –38%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
2019 2023
1.0
0.4
0
54

Latest developments
Recent developments involving cloud and edge computing
— The use of cloud and edge computing has grown
substantially due to additional AI demand. The rise of
AI in 2023 resulted in a massive increase in cloud and
edge usage, with a CAGR of approximately 30.9 percent
expected in the cloud AI market between 2023 and
2030 and an estimated increase in cloud spend of about
20.0 percent,1
as companies train and fine-tune models
and perform inferences. The need for extremely large
amounts of compute for AI model training has forced
businesses that have not yet transitioned to the cloud to
commit to it for AI endeavors, since most on-premises
data centers cannot meet the compute requirements
of AI workloads. Start-ups such as Lambda Labs2
and
CoreWeave3
also took advantage of the AI boom to
compete with hyperscalers on providing graphics-
processing-unit (GPU) services (“GPU as a service”) for
enterprises that do not have access to GPU compute on
their own.
— Priority shifts to on-premises edge solutions. Some
organizations are shifting focus from operating at
the network and operator edge (computing locations
situated at sites that are owned by a telecommunications
operator, such as a central data office in a mobile
network) to on-premises edge solutions that are closer
to the end user (such as an on-site data center) to
minimize latency and data transmit times, while demand
for private network connectivity has driven customer
adoption of edge-enabled use cases. A variety of
enterprise locations are poised to take advantage of
on-premises edge; they include manufacturing plants,
restaurants, retail stores, and hospitals.
— For some use cases, the shift from cloud to edge
computing marks the next evolution of AI models. While
2023 was mostly focused on training foundation models
for AI, companies are expected to begin performing
inference at scale on their models in 2024. With low
latency taking priority when performing inference for
some use cases, some workloads will likely shift to
the edge as companies begin to put their models into
commercial use.
— Companies diversify their GPU supply base. Nvidia’s
well-documented success in the GPU market throughout
2023 has improved GPU access for customers,
from hyperscalers to start-ups such as CoreWeave.
Companies of all sizes are considering additional
options for sourcing or building GPUs. For example,
hyperscalers are exploring and actively working on
a collection of sources for their compute needs and
have started designing in-house hardware and chips.
Other alternatives to Nvidia chips include chips from
Advanced Micro Devices (AMD) and Intel.4
However, the
ability to interchange GPU chips is also affected by the
software that facilitates their utilization. For instance,
Nvidia’s CUDA platform presents more challenges in
chip swapping than other, more standardized software
solutions.
1
“Gartner forecasts worldwide public cloud end-user spending to reach nearly $600 billion in 2023,” Gartner press release, April 19, 2023; Cloud AI market, Fortune
Business Insights, May 6, 2024.
2
Matt Kimball, “Analyzing the Lambda Labs partnership with VAST Data,” Forbes, November 1, 2023.
3
Chris Mellor, “CoreWeave GPU-as-a-service cloud farm using VAST storage,” Blocks & Files, September 26, 2023.
4
Leo Sun, “Could AMD become the next Nvidia?,” Motley Fool, March 16, 2024.
‘With the increasing growth of data volumes, particularly
with the widespread deployment of AI and generative AI use
cases, edge and cloud will continue to work in tandem. Edge
infrastructure will play a crucial role in enabling real-time
inference much closer to the source of data generation. Edge
will help enterprises maintain their edge.’
– Bhargs Srivathsan, partner, Bay Area
55

Demand
Cloud and edge job postings saw reductions across the board but remained high for software engineers. Data
engineers and software developers saw a larger relative decrease in job postings than other technical roles.
Since the growth of AI heavily influences the growth of cloud and edge computing, we also noted postings for
roles such as machine learning (ML) engineers (for more on this, please see the “Industrializing machine
learning” trend in this report).
Skills availability
There is a shortage in the supply of people with experience working in data centers. This shortfall indicates a
need for the field to continue developing more specialized professionals in cloud-computing roles.
Software engineer
Network engineer
Solution architect
System engineer
Technical architect
Project manager
Data engineer
Software developer
2019 2023
0
10
20
30
40
60
50
1.5×
0.2× 0.4× 0.4×
0.3×
55 44 36 27 18
Data
centers
Cloud
computing
Information
technology
Automation
Data
centers
Cloud
computing
Information
technology
Automation
Software
engineering
Software
engineering
56

Cloud and edge computing is the most widely adopted
trend across industries and regions, boosted by the growth
of AI. Most companies adopt cloud and edge computing
technologies to increase the value of existing offerings,
lower costs, better serve their customers, and optimize
the use of computer and storage resources. Companies
in regions like Africa show a strong interest in adopting
cloud but struggle with a lack of local data centers from
hyperscalers, legacy infrastructure, and connectivity
issues.5
Cloud adoption in the Middle East is growing fast,
with significant investment in regional data centers.6
Some of the leading industries in adopting cloud
and edge computing include technology, media, and
telecommunications; energy and materials; and financial
services.
Adoption dimensions
Enterprises are likely to seek improvements in latency, cost,
and security, spurring the next level of adoption of edge
computing technologies. Advancements along the following
dimensions could enable the next level of adoption:
— Scaled adoption of low-latency use cases (such as self-
driving cars and virtual reality headsets) or an increase
in AI inferencing needs could lead to a shift from cloud
to edge computing to improve latency for consumer and
enterprise use cases and to process data much closer to
where the data originate.
— Enterprises could move computation from the cloud
to the edge, potentially as a result, for example, of
increased data security requirements.
— A reduction in the cost of edge connectivity makes it
more feasible for small and medium-size enterprises to
migrate relevant, expensive cloud workstreams to the
edge.
In real life
Real-world examples involving cloud and edge computing
— McDonald’s and Google Cloud announced a multiyear
global partnership to use edge computing for the
restaurant’s mobile app, self-service kiosks, and other
machinery. They will use a combination of Google’s cloud
and edge capabilities and McDonald’s own software to
draw insights on equipment performance and reduce
complexity for staff.7
— In early 2024, the International Space Station (ISS)
installed a Kioxia (previously Toshiba Memory) solid-
state drive (SSD) for edge computing and AI tasks. This
upgrades the HPE Spaceborne Computer, the first
commercial edge-computing and AI-enabled system
on the ISS, originally installed to reduce dependency on
mission control for data processing.8
— Amazon, Google, and Microsoft all released proprietary
in-house AI chips.9
5
Sven Blumberg, Jean-Claude Gelle, and Isabelle Tamburro, “Africa’s leap ahead into cloud: Opportunities and barriers,” McKinsey, January 18, 2024.
6
“The Middle East public cloud: A multibillion-dollar prize waiting to be captured,” McKinsey, January 30, 2024.
7
“McDonald’s and Google Cloud announce strategic partnership to connect latest cloud technology and apply generative AI solutions across its restaurants
worldwide,” McDonald’s press release, December 6, 2023.
8
Roshan Ashraf Shaikh, “International Space Station gets Kioxia SSD upgrade for edge computing and AI workloads – HPE Spaceborne Computer-2 now packs
310TB,” Tom’s Hardware, February 5, 2024.
9
Cade Metz, Karen Weise, and Mike Isaac, “Nvidia’s Big Tech rivals put their own AI chips on the table,” New York Times, January 29, 2024.
‘In 2023, we saw an acceleration of large cloud
partnerships with CSPs [cloud service providers] in the
context of scaling generative AI adoption. It will be very
interesting to see how these will unfold in 2024 and 2025
as line of sight for real profit-and-loss impact at scale is
still emerging.’
– Andrea Del Miglio, senior partner, Milan
57

We see edge being deployed in various formats, depending
on proximity to the user or data generated and the scale of
resources involved.
— Internet of Things (IoT) or device edge. IoT devices, such
as sensors and video cameras, are used to collect and
process data. These devices often come with basic
computing and storage capabilities.
— On-premise or “close to the action” edge. These are
computing and storage resources deployed within the
premises or a remote or mobile location where data are
being generated.
— Operator, network, and mobile edge computing
(MEC). These are private or public computing and
storage resources deployed at the edge of a mobile or
converged-services provider’s network, typically one
network hop away from enterprise premises.
— Metro edge. Data centers with smaller footprints (about
three megawatts) located in large metro areas augment
the public cloud with near-premises computing power
and storage to provide lower latency and greater
availability.
Key uncertainties
The major uncertainties affecting cloud and edge computing
— Scaling hurdles could arise as the number of edge nodes
and devices grows, because edge computing does not
benefit from the same economies of scale as traditional
cloud computing.
— There is limited availability of talent and management
buy-in. Companies scaling cloud computing often face a
shortage of in-house talent to implement cloud solutions
effectively. This shortage poses challenges in identifying
new use cases tailored to the local context, such as
those specific to a retail store. Additionally, it hinders
the scaling of cloud infrastructure. This challenge
is further exacerbated if there is a lack of local
management buy-in.
— Technical challenges make it difficult to maintain and
scale cloud computing. The complexity of ML/AI models
and the absence of readily deployable solutions pose
significant challenges for companies seeking to build
cloud-computing capabilities. Additionally, maintaining
and managing edge hardware at scale can be tedious.
Furthermore, current 5G MEC coverage is not yet
extensive enough to support the scaling of use cases.
— Other challenges include limited ROI visibility, an overall
longer path to returns for edge development, a lack
of customer understanding of value-add use cases,
large investment requirements for scaling from pilot
to at-scale implementations, a complicated technical
stack requirement (especially due to integration with the
existing tech landscape at most companies), and a lack
of ready-to-deploy solutions.
— Privacy in the cloud is still a concern for many
enterprises. Some organizations are subject to strict
data privacy laws and are generally hesitant to make
a full migration to the cloud in the event of a breach or
cyberattack.
questions when moving forward with cloud and edge
computing:
— Will flexibility and positioning in a business and
regulatory sweet spot make edge more disruptive than
cloud? Or will inhibitors such as lack of interoperability
and commonality of standards in networking prevent
edge from reaching its full potential?
— Will hyperscale cloud providers be leaders in edge
computing? And how will telecommunications
companies with 5G-enabled MEC contend or partner
with hyperscalers?
— How will rapidly evolving AI technology and, importantly,
accompanying regulatory changes alter cloud and edge
provider business models?
— How will specialized chips deployed both in data
centers and at the edge, such as AI inference or always-
on sensors, modify the competitive cloud and edge
landscape?
— Will the increase in the number of storage and
processing units lead to security vulnerabilities?
— How will the transition to green infrastructure facilitate
the continued evolution of cloud and edge technology?
— As sensor costs drop and their performance increases,
how will edge and cloud resources cope with growing
demand for data movement and AI-enabled analytics?
— Will reduced connectivity costs drive more edge
adoption?
58

Quantum technologies encompass three pillars: quantum
computing, which not only will provide a speedup over
current computing systems for certain problems but also
could enable applications that are impossible to implement
on classical computers; quantum communication, which will
be critical for secure communication in the era of quantum
computers; and quantum sensing, which provides higher
sensitivity in more modalities than conventional sensors for
specific applications. The estimated full potential economic
impact of these technologies could be upward of roughly
$0.9 trillion. While the actual quantum advantage for useful
applications is still outstanding, we see promising research
and experimentation within pioneering enterprises across
industries, including chemicals, pharmaceuticals, finance,
automotive, and aerospace. In 2023, we saw steady
progress on both the hardware and software fronts while
organizations took more practical steps to ensure that their
infrastructure and security are ready for the technology.
Quantum technology must overcome a series of technical
hurdles to unlock its proposed benefits, which requires both
private and public sector efforts. It’s strategically wise for
companies to invest intelligently now to capitalize on future
advancements.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.02
0.01
0.03
News
Talent demand
Research
Searches
0.04
Patents
Equity
investment
2019 2023
1.0
0.04
0
Scoring the trend
Despite continued interest and innovation in the past
few years, quantum technologies saw a slowdown in
private investments. The 2022–23 period marked a
shift in investment toward more established companies,
with 62 percent of funding directed to companies
founded more than five years ago, reflecting a focus on
scaling promising ventures. However, public sector
investment in this field increased in 2023, underscoring
a sustained commitment to advancing quantum
technologies. While there is a long road ahead before
companies can achieve large-scale fault-tolerant
quantum computing, the groundbreaking potential for
quantum technology to be leveraged in appropriate use
cases could allow early innovators to extract significant
value once key performance milestones are met.
logistics; Chemicals; Financial services; Information
technology and electronics; Pharmaceuticals and
medical products; Telecommunications
$1 –17%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
59

Latest developments
Recent developments involving quantum technologies
— Major steps forward in error correction have occurred.
In collaboration with QuEra, MIT, and the National
Institute of Standards and Technology (NIST), Harvard
researchers demonstrated large-scale algorithms on 48
logical units, with an error rate below 0.5 percent. This
breakthrough was followed recently by a collaboration
between Microsoft and Quantinuum that produced four
reliable qubits with an error rate below 0.01 percent.1
Teams at IBM2
and Google’s Quantum AI3
also made
advances throughout the year to push the boundaries
of logical qubit storage, error rate per cycle, and error
frequency. These developments could serve as a
promising stepping stone to necessary advancements
on the long road from focusing on record qubit numbers
to achieving higher-quality, scalable qubits capable of
delivering meaningful results. Open hardware questions
about the efficacy of several qubit technologies are
currently being explored, as each has its own benefits,
challenges, and optimal use cases.
— Additional emphasis is being placed on building out
the full stack, including software and the integration
of quantum into classical computing infrastructure.
Quantum computers will be useful only for a small
but impactful set of problems; therefore, it will be
important to think about which parts are calculated on a
quantum-processing unit (QPU) versus other computer
architectures (for example, central processing units and
graphics processing units). Moreover, while there are
many technological hurdles to overcome, there has been
additional emphasis on building out the rest of the stack,
from software development kits (such as IBM’s Qiskit)
to testing and simulation of quantum algorithms (such
as NVIDIA’s Quantum Cloud that was built on its open-
source CUDA platform in March 2024).
— Strides have been made in information security
because of the progress in quantum computing. Major
tech players are ramping up their information security
measures to reduce the risk of newer threats emerging
from advancements in quantum computing. These are
termed “harvest now, decrypt later” attacks, where
data expected to have a long shelf life are captured
and stored so they can be decrypted once quantum
computers are powerful enough to crack today’s
classical public key encryption such as RSA or elliptic-
curve cryptography. For instance, various organizations
are enhancing these classical public key encryptions
with postquantum-cryptography algorithms this year.
This allows these organizations to proactively mitigate
the risks of harvest now, decrypt later attacks with
currently available classical tools ahead of potential
additional quantum-level protection—such as quantum
key distribution (QKD)—maturing and becoming
available. In December 2022, the Biden administration
signed into law the Quantum Computing Cybersecurity
Preparedness Act, asking government agencies to
“adopt technology that will protect against quantum
computing attacks.”4
Various intergovernmental
organizations have also published policies and strategies
to develop quantum technologies and prepare for
potential quantum cyberattacks.
— Start-up partnerships with conventional enterprises
continue. Quantum computing start-ups and
conventional enterprises announced more partnerships
in 2023. These continue to occur as start-ups attempt to
get closer to the proposed use cases of their computers
and conventional enterprises try to gain a future
competitive edge. For example, Rolls-Royce partnered
with quantum start-up Riverlane to develop algorithms
and computational tools to accelerate complex material
discovery for jet engines and other components in
hostile environments.5
1
Stephen Nellis, “Microsoft, Quantinuum claim breakthrough in quantum computing,” Reuters, April 3, 2024.
2
Sergey Bravyi et al., “High-threshold and low-overhead fault-tolerant quantum memory,” Nature, 2024, Volume 627.
3
“Suppressing quantum errors by scaling a surface code logical qubit,” Nature, 2023, Volume 614.
4
John Hewitt Jones, “Biden signs quantum computing cybersecurity bill into law,” FedScoop, December 21, 2022.
5
Matt Swayne, “NQCC, Rolls-Royce and Riverlane partner to accelerate materials discovery,” Quantum Insider, December 18, 2023.
60

Software engineer
Scientist
Data scientist
Software developer
Project manager
Security analyst
System engineer
Research technician
0
0.2
0.4
0.6
0.8
1.0
2019 2023
Quantum
computing
Physics Artificial
intelligence
Python Cloud
computing
Algorithms Machine
learning
Machine
learning
Quantum
computing
Physics Artificial
intelligence
Python Cloud
computing
Algorithms
42
23 21 17 14 14 12
4.1×
2.3×
1.5×
2.9×
0.3×
0.5×
2.9×
Demand
While quantum technology has a small labor market, talent demand has more than doubled since 2019. Given
the nascency of the technology, the number of graduates from quantum-specific programs is low. As a result,
talent is sourced from the broader fields of physics, mathematics, electrical engineering, chemistry, biochemis-
try, and chemical engineering. All told, approximately 367,000 people graduated in 2023 with degrees relevant
to quantum technology, with the number of universities offering programs and master’s degrees in quantum
technology increasing by 8.3 percent and 10.0 percent, respectively, over the past year.¹
While quantum-technology talent demand saw a slight decrease in 2023, in line with the broader talent
market, the hiring mix of applied roles, such as data scientists, and more specialized roles, such as software
engineers, lines up with the path followed by more mature technologies, such as AI. As quantum technology
continues to develop, this shift toward more specialized hiring will likely strengthen.
Skills availability
Although the total demand for jobs in quantum technologies remains low, growth in the supply of skilled
talent—especially for skills such as quantum and cloud computing, machine learning, risk management, and
Python programming—indicates a strong dynamic for further industry acceleration.
¹“Steady progress in approaching the quantum advantage,” McKinsey, April 24, 2024.
61

Given quantum technology’s nascency, it is unsurprising
that it is one of the least widely adopted technologies
across our set of analyzed trends. However, the potential
for future quantum applications to be disruptive in select
sectors and use cases has inspired several companies to
engage in experimentation and pilots of different quantum
technologies. These companies most commonly see
the ultimate goal of adopting quantum technologies as
increasing the value of existing offerings through better
outcomes or products (for example, optimized portfolios
and routing).
The finance, pharmaceuticals, technology, energy and
materials (including chemicals), and telecommunications
sectors have emerged as leaders in the adoption of
quantum technologies, with more than roughly 40 percent
of companies in these industries reporting that they are
conducting experiments and pilots. These industries
cover both shapers of quantum technologies (primarily
telecommunications and technology) and eventual end
users, with significant use cases across chemicals,
finance, and pharmaceuticals. Investments in quantum
communications are driven by improvements in quantum-
resistant cryptography and QKD to protect against future
decryption attacks by quantum computers, indicating a
forward-looking approach to managing risk.
Adoption dimensions
The adoption trajectory of advanced technologies varies for
If or when certain technical challenges are overcome, the
dawn of quantum computers that are powerful enough
to jeopardize current encryption provides a potentially
disruptive use case for quantum technologies.
The next level of adoption likely will involve companies
leveraging quantum computers to significantly speed up
the solution to a set of valuable computational problems.
Additionally, companies will be motivated to enhance their
encryption methods to include postquantum cryptography,
ensuring the protection of mission-critical and long-lived
digital data.
the next level of adoption for quantum technologies:
— to achieve further milestones suitable for large-scale
fault-tolerant computing, an increased number of
physical qubits (that is, greater than 1,000) with lower
error rates (that is, 10–6
or less) across several qubit
platforms (for example, superconducting and spin), along
with a road map to scalably interconnect qubits and
possibly chips of qubits
— to increase reliability, an increased number of logical,
error-corrected qubits (more than 50 to 100)
— to secure data that retains its value over several years,
implementation of quantum-resistant cryptographic
algorithms (such as the four algorithms selected in 2022
by NIST: CRYSTALS-Kyber for key establishment and
CRYSTALS-Dilithium, Falcon, and SPHINCS for digital
signatures)
‘While the technology still faces challenges, significant
advancements have been made this year, especially in the
field of error correction. In addition, major enterprises are
actively working to improve encryption by incorporating
postquantum-cryptographic algorithms. This highlights the
importance of taking proactive measures now to minimize
the impact of potential “harvest now, decrypt later” attacks.’
– Mena Issler, associate partner, Bay Area
62

In real life
Real-world examples involving the use of quantum
— Banks are partnering with quantum businesses to
build their capabilities. HSBC, for example, announced
a partnership with quantum computing start-up
Quantinuum. The companies will work together to
explore the benefits of quantum machine learning
and quantum natural-language processing to analyze
customer data more accurately and prevent fraud.6
— Apple upgraded its iMessage encryption with a new
protocol known as PQ3 that utilizes postquantum
cryptography to protect messages against future
encryption breaches from quantum computers.7
The
company joins messaging app Signal in using the NIST-
selected Kyber algorithm.8
— The Biden administration, through the US Department
of Commerce’s Economic Development Administration
(EDA), designated 31 tech hubs across the United States,
including the Bloch Tech Hub (Bloch), a consortium
led by the Chicago Quantum Exchange,9
and Elevate
Quantum Colorado (led by Elevate Quantum). Bloch
will make use of Chicago’s universities, national labs,
private quantum companies, investors, accelerators, and
other partners to increase access to quantum facilities
and meet industry demand. Elevate Quantum Colorado
encompasses the Denver–Aurora region of Colorado
and has proposed initiatives focused on infrastructure,
entrepreneurship, workforce development, and
stakeholder engagement throughout the quantum
value chain.10
— Several successful quantum-communications
milestones occurred throughout 2023. New York
University and Qunnect performed a successful test
of a ten-mile quantum network between Brooklyn and
Manhattan, with 99 percent uptime.11
Amazon Web
Services (AWS) researchers conducted a successful
test of point-to-point QKD between two devices three
miles apart in Singapore.12
A team of scientists from
Russia and China established an encrypted quantum-
communication link using secure keys over 3,800
kilometers, leveraging China’s quantum satellite, Mozi.13
Noteworthy quantum technologies include the following:
— Quantum computing. Quantum processors use the
principles of quantum mechanics to perform simulations
and process information. They can provide exponential
performance improvements over classical computers for
some applications.
— Quantum communication. This is the secure transfer
of quantum information across space. It could ensure
security of communications, enabled by quantum
cryptography, even in the face of unlimited (quantum)
computing power.
— Quantum key distribution. QKD is the use of quantum
technology to secure communications against possible
attacks by quantum computers.
— Quantum sensing. Quantum sensors could provide
measurements of various physical quantities at a
sensitivity that exceeds those of classical sensors by
orders of magnitude.
6
“HSBC and Quantinuum explore real world use cases of quantum computing in financial services,” Quantinuum, May 30, 2023.
7
Apple Security Research Blog, “iMessage with PQ3: The new state of the art in quantum-secure messaging at scale,” Apple, February 21, 2024.
8
Signal, “Quantum resistance and the Signal Protocol,” blog entry by Ehren Kret, September 19, 2023.
9
“Biden–Harris administration designates tech hub in Illinois to drive innovation in quantum computing and communications,” US Economic Development
Administration press release, October 2023.
10
“Biden–Harris administration designates tech hub in Denver–Aurora to drive innovation in quantum information technology,” US Economic Development
Administration press release, October 2023.
11
“NYU takes quantum step in establishing cutting-edge tech hub in Lower Manhattan,” New York University press release, September 13, 2023.
12
John Russell, “AWS partners report successful quantum key distribution trial in Singapore,” HPCwire, March 6, 2023.
13
Matt Swayne, “China and Russia test quantum communication link,” Quantum Insider, January 2, 2024.
63

64
Key uncertainties
The major uncertainties affecting quantum technologies
— Technical challenges include the ability to manage a
sufficient quantity and quality of qubits over enough
time to derive meaningful computational results while
navigating potential barriers to adoption (for example,
regulatory, technological, and financial) that are not yet
apparent.
— Cost-effectiveness may take time. Traditional
supercomputers can perform most calculations that
businesses require reasonably well and at a much
lower cost; this is expected to change once quantum
advantage is achieved and general-purpose quantum
computers take center stage.
— Ecosystems are nascent. Limited awareness and
adoption of quantum technologies (such as differing
levels of technology maturity and applicability
for different industries), the need for increased
interdisciplinary coordination required to bring
technologies to market (for example, between academia
and industry), and quantum companies’ continued
work to access talent (talent includes theory, hardware,
and software development) hinder development and
innovation outside quantum hubs.
questions when moving forward with quantum technologies:
— On what timeline over the next decade will quantum
technology advance and reach major milestones (for
example, full error correction, quantum advantage, and
vulnerability of current RSA encryption)?
— What benefits could arise from the combination of
quantum and AI?
— How and when should companies start to prepare for
quantum technology, particularly the security threats
posed by quantum computers?
— Will talent supply catch up to demand? What levers
are available, and how can organizations help fill the
talent gap?
‘Recent advancements in quantum computing show that
we are moving away from research toward real application.
It’s interesting to see that these advancements happen
currently across different technologies. So the race for the
best technology or combination of technologies—and how
best to use them—is still on.’
– Henning Soller, partner, Frankfurt

Cutting-edge
engineering
65

Future of robotics
Advanced robotic systems are characterized by their high
sophistication in automating a variety of physical tasks.
The range of use cases—from consumer-level services
to enterprise-level assembly—has proliferated in recent
years because of both macroeconomic conditions and
technological advances. In terms of macroeconomics, the
world has seen rising labor costs, aging populations, and
additional complexity regarding offshoring labor, leading to
tight labor markets in many countries.1
From a technological
perspective, AI has led to many innovations that have
increased the capabilities and accelerated the training of
physical robots. While there are technological and social
hurdles to overcome, widescale adoption can be key to
unlocking productivity, shifting the economy to incorporate
new ways of working that are fundamentally different from
current human-centric jobs.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.04
0.02
0.06
News
Talent demand
Research
Searches
0.08
Patents
Equity
investment
2019 2023
1.0
0.08
0
CUTTING-EDGE ENGINEERING
Scoring the trend
Future of robotics
We see an uptick in research, patents, and investment
in 2023 for these technologies.
Industries affected: Agriculture; Automotive and
assembly; Chemicals; Consumer packaged
goods; Information technology and electronics;
Manufacturing; Metals and mining; Oil and gas;
Pharmaceutical and medical products; Retail;
$6 –20%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
66
1
“Help wanted: Charting the challenge of tight labor markets in advanced economies,” McKinsey Global Institute, June 26, 2024.

Latest developments
Recent developments involving the future of robotics
— A proliferation of sectors are adopting robots. With
increasing capabilities and decreasing costs, robots
are branching out from assembly and manufacturing to
new sectors such as life sciences and agriculture. In life
sciences, for example, we have seen a surge in interest
in automated liquid handling—robots that assist with
pipetting, handling, and transferring liquid chemicals
for drug development—with a range of off-the-shelf
and custom robot options for buyers and an estimated
market value of approximately $3 billion in 2023.2
In
agriculture, restaurant chain Chipotle announced at
the end of 2023 that it was investing in GreenField, a
company that builds autonomous, lightweight robots
that can cut weeds without damaging crops.3
— The types of robots are expanding. Robot types
are expanding beyond the typical industrial robots.
Companies in the electronics industry are using newer,
small-scale collaborative robots (cobots) that can be
placed on desktops to aid in material handling and
assembly. Service robots have also seen steady growth,
with a forecasted CAGR of approximately 18 percent
over the next five years4
and expansion to mostly
commercial-sector operations in the form of cleaning
and kitchen robots. In 2023, Miso Robotics and Cali
Group opened an automated restaurant in Pasadena,
California—equipped with sensors, cameras, and
algorithms, Miso’s smart commercial kitchen robot can
cook a variety of food items and is not limited to a single
type of cuisine or dish.5
— Humanoid and general-purpose robots have surged in
interest. Although most robots are still used for specific
tasks, AI has led to considerable interest in humanoid
robots, which have the potential of being deployed in
environments that will require minimal retrofitting. While
early demonstrations still show limited functionality,
humanoid robots are already being tested in commercial
settings and garnering investor interest. Humanoid
robot start-ups Figure and 1X Technologies closed $675
million and $100 million funding rounds, respectively,
with major investors including Microsoft, Nvidia, and
OpenAI.
— AI continues to boost progress toward more
autonomous robots. AI has been crucial to the
development of robotics, through both the construction
of new algorithms and techniques as well as the
refinement of older ones. New generative AI
approaches, such as Covariant’s robotics foundation
models or Toyota Research Institute’s Large Behavioral
Models (LBMs), train robots to react to unexpected
situations and build generalizable skills. Toyota’s
LBMs, for example, allow robots to learn a series
of fundamental skills, such as pouring liquids and
using tools, by observing humans. These skills are
generalizable to many more tasks, with the company
aiming to improve its current skill count from 60 to 1,000
by the end of 2024.6
2
“Automated liquid handling industry report 2023-2035: Patent surge reflects thriving innovation,” PR Newswire, August 16, 2023.
3
“Chipotle invests in autonomous agricultural robots and climate-smart fertilizer to improve the future of farming,” PR Newswire, December 13, 2023.
4
“Service robotics market size, share & COVID-19 impact analysis,” Fortune Business Insights, April 22, 2024.
5
Brianna Wessling, “Miso Robotics and Cali Group open automated restaurant,” The Robot Report, December 11, 2023.
6
“Toyota Research Institute unveils breakthrough in teaching robots new behaviors,” Toyota, September 19, 2023.
67
‘We stand on the brink of revolutionary advances in robotics,
with more autonomous, more dexterous, and more mobile
machines emerging [at scale]. These advances promise a
future where robots enhance our capabilities and expand
the operational domains of automation, from intricate tasks
on manufacturing floors to dynamic service environments.
Thoughtful adoption of these technologies could unlock
productivity while elevating the nature of labor.’
– Ani Kelkar, partner, Boston

Software developer
Data scientist
Software engineer
Technician
Project manager
Mechanical engineer
Business analyst
Automation engineer
2019 2023
0
1
2
3
0.3× 0.1× 0.8× 0.3×
2.9×
0.4×
3.7×
Automation Mechatronics Manufacturing Artificial
intelligence
Python Data
analysis
Software
engineering
Automation Mechatronics Manufacturing Artificial
intelligence
Python Data
analysis
Software
engineering
41 34
21 20 19 18 17
Demand
Future of robotics
Data scientists, technicians, and automation engineers saw smaller decreases in job postings relative to other
top jobs, which could point to an increasing focus on using AI models to train robots.
Skills availability
Skills in automation, mechatronics, and manufacturing are all in high demand for the future of robotics. As
robots’ functionality is improved, more programming and AI skills may be needed.
68

Robotics technologies saw the highest experimentation
rates and have had one of the lowest levels of investment
out of all trends, indicating its emergence as a nascent
trend with significant advancement opportunities. However,
we do see advanced-industries companies making the
largest average initial and run-rate investments in gross
dollars, highlighting the maturity of robotics use cases in the
automotive and manufacturing sectors.
Adoption dimensions
We define the next level of adoption of humanoid or
general-purpose robots as their wide-scale deployment
in factories and commercial pilots at other enterprises.
— increased dexterity so that robots can manipulate
objects at a similar skill level as that of their human
counterparts (for example, for assembly-line tasks)
— improved battery life, allowing robots to operate
untethered for most of a standard working day (that is,
approximately eight hours)
— sufficient autonomy so that robots can operate in
certain edge-case scenarios without requiring human
intervention when difficulties are encountered (for
example, the ability to troubleshoot and decide on the
next action in case of unexpected circumstances)
In real life
Real-world examples involving the future of robotics include
the following:
— BMW and robotics start-up Figure signed a partnership
that would bring the start-up’s humanoid robots to
BMW’s auto manufacturing facilities. After undergoing
training to perform the related functions, the robots will
operate in the body shop, warehouse, and sheet metal
line in the next one to two years.
— Chevron has been using robotics company Boston
Dynamics’ four-legged Spot robot in its oil and gas
operations. The Spot robots are equipped with
many different sensors to help in operations, safety,
inspections, and more. Spot has been considered for
use in environmental and safety monitoring, as well as in
emergency management.
— After acquiring robot kitchen start-up Spyce in 2021,
Sweetgreen opened its first restaurant that uses kitchen
robots in 2023. The company claims that it can reduce
the time needed to make a bowl by 50 percent and that
the location employing the robot had 10 percent higher
average tickets, faster throughput, and improved order
accuracy.7
— In preparation for future deep-space infrastructure
construction, such as solar-power stations,
communications towers, and crew shelters, NASA
developed robots that learned how to build a shelter on
their own in about 100 hours. The test involved three
robots—two builders and one fastener—that were
given plans for the shelter and had to use software and
digital simulations to determine the best approach for
constructing the building.
A future of more autonomous and dexterous robots will
depend on the following technologies:
— Autonomous technologies. Automated systems with
sensors and AI can make independent decisions based
on data they collect.
— Motion and sensor technology. This technology involves
using actuators, motors, and sensors that can enhance
dexterity, movement, and environmental perception and
could expand the set of use cases.
— Connectivity technologies. Technologies such as 5G/6G,
private networks, and the Internet of Things can enable
real-time updates and improved security levels.
— Materials innovation. Using new materials (for example,
carbon fiber and other lightweight materials) and
processes (for example, 3D printing) can improve
efficiency and sustainability.
— Electrification technologies. These solutions allow
robots to operate untethered for longer time frames,
leading to increased versatility.
7
Lisa Jennings, “Sweetgreen’s robotic makelines show 10% sales lift,” Restaurant Business, March 1, 2024.
69

Key uncertainties
The major uncertainties affecting the future of robotics
— Safety, privacy, and accountability concerns could arise
as robots become further integrated with society and
work alongside humans.
— The impact on the labor market and public perception
might initially be negative. Although adoption of the
trend has the potential to automate many work activities,
it can also provide the opportunity to redesign the job
market for new roles. Integration of robots into the
workforce would most likely require training to upskill
human workers for different roles or to work effectively
with their new counterparts.
— Access to sufficient resources, such as batteries
and talent, will remain critical to both the technology
development and supply of future products.
— Cross-border competition can have an outsize effect on
global technology trade flows.
— The potential for regulatory shifts adds significant
uncertainty to the market outlook, as some companies
are concerned that regulation could reshape
technological development and deployment plans,
potentially leading to inconsistent practices and
challenges in ensuring accountability and public trust in
the development and deployment of these technologies.
questions when moving forward with robotics:
— At what rate will companies adopt robots into their
organizations?
— How will integration with robots reshape the workforce
of the future?
— When can we expect general-purpose robots?
— What new business use cases may be created by
advanced robots?
70
‘Labor scarcity, the need for manufacturing flexibility,
and the productivity imperative are expected to remain
key drivers of growth in the robotics industry. Humanoid
robots powered by AI are anticipated to offer significant
flexibility, enabling them to shift from single-purpose robots
to multifunctional machines. This increased flexibility is
expected to further propel growth in the robotics industry,
as manufacturers seek to optimize their production processes
and increase efficiency.’
– Ahsan Saeed, partner, Munich

Future of mobility
Technical advancements, coupled with rising sustainability
concerns, have given rise to a new era of mobility.
Autonomous and electric vehicles (AVs and EVs), urban
air mobility, and ACES (autonomous driving, connectivity,
electrification, and shared/smart mobility) technologies
have become the focus of many organizations trying
to revolutionize the transport of people and goods
while improving accessibility, safety, and sustainability.1
Although the regulatory environment remains nascent,
ACES technology has seen accelerating adoption by both
new industry players and incumbents in the automotive
and aerospace industries. For example, 2023 saw
more major steps on the path to wide-scale adoption of
these technologies, with commercial pilot programs of
autonomous robo-taxis in major cities and flight testing of
urban aircraft. Even with high growth projections and early
signs of success for many of these technologies, innovators
still wrestle with technological, regulatory, and consumer
sentiment issues, which have added volatility to the industry
over the past year.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.4
0.6
0.8
News
Talent demand
Research
Searches
1.0
Patents
Equity
investment
2019 2023
Scoring the trend
Future of mobility
Significant increases in news, research, and patents
indicate increasing interest from the public sphere and
an acceleration of R&D from enterprises and research
institutions. While investment saw a dip, in line with
macroeconomic conditions and a general reduction in
investment activity, talent demand is increasing as
companies extend commercial pilot programs and
testing in aviation and autonomy and manufacturers of
autos and trucks with electric drivetrains scale up their
volumes.
Industries affected: Automotive and assembly;
Aviation, travel, and logistics; Electric power, natural
gas, and utilities; Financial services; Metals and
mining; Oil and gas; Public and social sectors;
Retail
$83 –5%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
1.0
0
1
Kersten Heineke, Nicholas Laverty, Timo Möller, and Felix Ziegler, “The future of mobility,” McKinsey Quarterly, April 19, 2023.
71

Latest developments
Recent developments involving the future of mobility
— EV demand remains high, despite recent slowing growth
in major regions. EVs have been soaring in popularity
for several years and remain in high demand, despite
the recent slowing growth rate in certain regions.2
EVs saw record sales in 2023, but manufacturers
are seeing slowing consumer demand with lower
growth projections for 2024, partially because of high
prices and customer range anxiety. This is leaving
automakers with high EV inventory and forcing some to
cut prices. The industry is now exploring how to make
EVs meaningfully cheaper, particularly in the United
States and Europe. Even China, the world’s largest
EV market, saw a slowdown in growth in 2023 that
coincided with reduced subsidies, touching off a price
war among domestic and foreign manufacturers.3
The
battery industry continues to grow quickly because of
heavy public and private investment in EV development.
For example, approximately 30 battery factories are
currently planned, being constructed, or operational in
the United States alone; 13 of them are expected to open
by 2025.
— Robo-taxis navigate hurdles to achieve more
widespread commercial use. In August 2023, California
regulators granted Waymo and Cruise permission to
operate commercial robo-taxi services in San Francisco.
However, following a series of safety incidents, Cruise
had its license revoked in October. Waymo continues its
commercial operations and has gained initial regulatory
approval to expand within California and operate on
highways. China issued its first regulation on commercial
AVs in December 2023, requiring a robo-taxi to remote-
operator ratio of no more than 3:1.
— Autonomous trucking reaches a pivotal moment as
testing begins. The year 2024 could be a watershed
one for autonomous trucking as companies begin
larger-scale on-the-road pilots. This is especially the
case in the United States, where demand has been
demonstrated, given that the trucking industry has
been experiencing a shortage of drivers. Start-ups are
beginning commercial tests amid a mixed regulatory and
investment environment. Aurora Innovation, a company
specializing in self-driving technology, began an on-the-
road pilot for commercial trucking on public highways
between Houston and Dallas, with the goal of achieving
fully autonomous trips by the end of 2024. Like the
industry for robo-taxis, the autonomous trucking sector
faces regulatory challenges related to safety concerns.
However, interest remains strong.
— Micromobility generally demonstrates resilience amid
market consolidation. The micromobility sector has
shown signs of steady growth and robust progress.
Lime, an e-scooter start-up, reported that for the first
time in 2022, it achieved profitability, which continued
into the first half of 2023. Users of micromobility
are also wide ranging: a National Association of City
Transportation Officials (NACTO) report indicates that
customer segments of micromobility include commuters,
tourists, and recreational users.4
As the micromobility
market progressed in 2023, it saw increased
consolidation in a bid to compete for funding and chase
profitability while also seeing postpandemic demand
levels for e-bikes decline in Europe.
2
Kersten Heineke, Philipp Kampshoff, and Timo Möller, “Spotlight on mobility trends,” McKinsey, March 12, 2024.
3
Selina Cheng, “Even the world’s biggest electric-vehicle market is slowing,” Wall Street Journal, February 18, 2024.
4
Andrew J. Hawkins, “Bird may be bankrupt, but shared micromobility is doing just fine,” Verge, December 21, 2023.
72
‘Batteries require building new supply chains
from raw materials to recycling to enable the
energy transition in mobility.’
– Andreas Breiter, partner, Bay Area

73
— The scale and breadth of drone delivery operations have
increased. In 2023, commercial drone deliveries saw a 14
percent increase from 2022, exceeding one million, and
drone delivery was the only future mobility technology
to see a significant increase in funding.5
This growth is
partially due to favorable regulatory developments, such
as the Federal Aviation Administration (FAA) granting
120 waivers for beyond-visual-line-of-sight (BVLOS)
operations in 2023.6
This includes approvals for drone
delivery players such as UPS Flight Forward, Wing
Aviation, and Zipline, representing a 65 percent increase
in waivers from 2022. BVLOS operations allow drones
to fly farther, with less costly oversight from nearby
pilots or visual observers, making expanded operations
possible and the economics attractive.7
— Funding for eVTOL aircraft experienced a slight
decline, but the potential for certification maintains
momentum. Funding for electric vertical takeoff and
landing (eVTOL) aircraft experienced a slight decline,
with eVTOL companies securing only about 50 percent
of the funding they received in 2022. However, there is a
positive outlook regarding potential certifications from
regulatory bodies, which could reinvigorate the segment.
To support this growth, players are developing additional
infrastructure and manufacturing capabilities, including
charging facilities and landing sites. The approval for
commercial flights for eVTOL operators is expected to
further accelerate the expansion of this infrastructure.
5
Future of Air Mobility Blog, “Clouds or clear skies? Prospects for future air mobility,” blog entry by Axel Esqué, Tore Johnston, and Robin Riedel, McKinsey, January
23, 2024.
6
Other jurisdictions allowed BVLOS operations prior to the FAA waivers.
7
Future of Air Mobility Blog, “Clouds or clear skies?,” McKinsey, 2024.
‘While last year saw both advances and setbacks,
we’ve gotten another step closer to autonomous
vehicles being a reality at scale. The first mass
adoption use cases will be in autonomous trucks,
robo-taxis, and robo-shuttles, with further advances
in autonomy levels for personal vehicles at the same
time. Remote operating is another interesting use case
that we expect to grow sizably over the next years.’
– Kersten Heineke, partner, Frankfurt

Demand
Future of mobility
Software engineer was once again the job posting with the highest demand for future mobility technologies in
2023, with only a 5 percent decrease in job postings relative to a 26 percent average decrease across all
technology trends.
While most technical and managerial roles, such as system engineers and project managers, saw decreases,
the demand for jobs such as drivers remained fairly constant or saw slight upticks.
Skills availability
There was a decline in talent demand in 2023 alongside a shortage in talent availability.
Software engineer
Project manager
Software developer
Electrical engineer
System engineer
Program manager
Fleet manager
2019 2023
0
4
8
12
16
20
0.5× 0.4× 0.2×
Electric
vehicles
Automotive
industry
Information
technology
Vehicle fleet
management
Transportation
management
Electric
vehicles
Automotive
industry
Information
technology
Vehicle fleet
management
Transportation
management
12 11 11 10 9
<0.1× <0.1×
74

75
Among the trends observed this year, the future of mobility
trend ranks in the bottom three for initial investment,
adoption, and optimism. These rankings can be seen as
indicators of the significant innovation, regulatory, and
behavioral hurdles companies will have to overcome to
reach next-level adoption of future mobility technologies.
Adoption dimensions
The adoption trajectory varies for technologies and use
cases within the future of mobility trend. For example, robo-
taxi and robo-shuttle ridesharing illustrate the potential
adoption trajectory for consumer use cases of future
mobility technology.
As an extension of public transit and an alternative to
traditional ridesharing and private vehicles, the next level of
adoption for robo-taxis and robo-shuttles could be at-scale
deployment of fleets in significant metropolitan areas and
across all road types.
— safety and technological reliability to be demonstrated
for both highway and city operations to fully cover
metropolitan areas (currently, human drivers have
approximately five incidents per million miles)8
— robo-taxis and robo-shuttles to become comparable to
traditional ridesharing options in terms of cost
— regulatory changes to lay the groundwork for scalability
of robo-taxis in major metropolitan areas (currently,
there are around half a dozen cities in the United States
and China piloting the technology)9
In real life
Real-world examples involving the future of mobility include
the following:
— After performing more than 700,000 robo-taxi trips
in 2023,10
Waymo gained approval to expand its
commercial robo-taxi services to additional parts of the
San Francisco Bay Area, Los Angeles, and Phoenix. This
expansion allows the company to operate on highways
for the first time in designated areas. Meanwhile, Baidu’s
Apollo Go gained approval to offer 24/7 services of its
robo-taxis and to begin operating on highways to Beijing
Daxing International Airport, making Beijing the first
capital city to offer airport robo-taxi rides.11
— Uber had its first profitable year as a public company
in 2023, with a net income of about $4 billion. This was
driven by a combination of robust user growth and more
efficient cost management practices. This milestone
could be a potential inflection point for the company
as it shifts from a growth-focused start-up to a more
mature profit-oriented company and serves as a positive
indicator of the viability of the mobility model.
— Joby Aviation signed a six-year contract with Dubai’s
Road and Transport Authority to conduct air taxi
services in the city by early 2026, with Joby aiming to
commence operations as early as 2025. The company’s
aircraft are built to carry a driver, a pilot, and four
passengers at speeds of up to 200 miles per hour. In
February 2024, Joby became the first developer of
eVTOL aircraft to complete the third of five stages of the
FAA type certification process.12
— E-scooter start-ups Tier Mobility and Dott merged
to form Europe’s largest e-scooter company, with an
additional $66 million fusion into the newly formed
business. This merger will allow for operations spanning
20 countries in cities such as Berlin, London, Paris, and
Rome, with a combined annual revenue of about $250
million.
— Many EV automakers—including Ford and General
Motors—announced that they would adopt Tesla’s North
American Charging Standard (NACS) port in an effort to
gain access to the company’s extensive supercharging
network amid consumer concerns about unreliable
third-party chargers.
8
Waypoint: The Official Waymo Blog, “Waymo significantly outperforms comparable human benchmarks over 7+ million miles of rider-only driving,” Waymo,
December 20, 2023.
9
Off the Kuff, “The state of robotaxis in 2024,” blog entry by Charles Kuffner, February 29, 2024.
10
Waypoint: The official Waymo Blog, “Dear Waymo community: Reflections from this year together,” blog entry by Dmitri Dolgov and Tekedra Mawakana, Waymo,
December 21, 2023.
11
“Baidu launches China’s first 24/7 robotaxi service,” PR Newswire, March 8, 2024.
12
“Joby completes third stage of FAA certification process,” Joby Aviation press release, February 21, 2024.

76
A future of efficient, sustainable mobility will be defined by
ACES and adjacent technologies, such as the following:
— Autonomous technologies. Automated systems with
sensors and AI can make independent mobility decisions
based on data they collect.
— Connected-vehicle technologies. Equipment,
applications, and systems use vehicle-to-everything
communications to improve safety and efficiency.
— Electrification technologies. These solutions replace
vehicle components that operate on a conventional
energy source with those that operate on electricity.
— Shared-mobility solutions. Hardware and advanced
digital solutions, as well as new business models and
social adoption, enable the use of alternative shared
modes of transportation in addition to—or instead of—
privately owned vehicles.
— Materials innovation. The use of new materials (for
example, carbon fiber and other lightweight materials)
and processes (such as engine downsizing) can improve
efficiency and sustainability.
— Value chain decarbonization. In addition to
electrification, technical levers (such as green primary
materials) can abate emissions from materials’
production and increase recycled materials’ use.
Key uncertainties
The major uncertainties affecting the future of mobility
— The global energy supply expansion that is required
to meet EV demand remains uncertain. Demand for
lithium-ion batteries is surging as EV production
expands, necessitating more and larger battery
factories. At the same time, critical upgrades are
required to EV-charging infrastructure. Europe, for
instance, may need to invest upward of €240 billion
to complete extensive utility grid updates, increase
renewable-energy production capacity, and provide the
estimated 3.4 million public charging points required by
2030 (up from 375,000 in 2021).13
— Safety and accountability concerns surround uncrewed
and autonomous-mobility technologies.
— Technology uncertainties about batteries with sufficient
range to support more applications (such as air mobility)
may hinder greater adoption.
— Customer perceptions of noise and visual impact
remain in play (for example, noise pollution from delivery
drones).
— Equipment and infrastructure costs are factors for
new modes of transportation (for instance, building
EV-charging networks).
— Regulation shifts will occur as mainstream certification
frameworks are developed (for example, controlling
expanded air traffic).
— Privacy and security concerns for underlying AI
algorithms and workflows that rely on consumer data
should be addressed.
— Access to sufficient resources (such as raw materials
for battery production and software developers for
autonomous-driving software) will be required to scale
these technologies.
questions when moving forward with ACES technologies:
— How will the future of mobility trends shape cities?
— What regulatory enablers and barriers need to be
addressed to enable widespread adoption?
— What share of vehicle sales will autonomous vehicles
account for, and what business models will predominate?
— What achievements need to be made to win over
consumer trust for autonomous vehicles and urban air
mobility?
— What scale will advanced air mobility achieve in the next
decade?
— What needs to be in place for advancements in
shared mobility to deliver on anticipated financial and
environmental impact?
13
Kersten Heineke and Timo Möller, “Future mobility 2022: Hype transitions into reality,” McKinsey, March 10, 2023.

The combination of biological and computing advancements
has led to a range of innovations in products and services
for industries such as healthcare, food and agriculture,
consumer products, sustainability, and energy and materials.
With the possibility of more than $2 trillion of potential
economic impact in the next decade,1
as well as hundreds
of use cases, bioengineering technologies such as gene
therapy have the potential to improve human health and
longevity, and technologies such as alternative-protein
production could contribute to sustainability. Although
the science underlying many of these use cases has
been demonstrated today, the technologies must also
achieve commercial viability and overcome social and
regulatory challenges.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.4
0.6
0.8
News
Talent demand
Research
Searches
1.0
Patents
Equity
investment
2019 2023
Scoring the trend
Although bioengineering ranked second among
emerging trends in publications and research in 2023,
with a noticeable uptick in both areas, news coverage
and searches of the trend have remained constant
since 2019. There has also been a decline in
investment, which is in line with the overall market in
2023, as well as a slight decline in patents (patent
scores are based on patents granted and, therefore,
subject to a 12-to-18-month lag). Nonetheless, talent
demand has nearly doubled since 2019.
Industries affected: Agriculture; Chemicals;
Consumer packaged goods; Healthcare systems
and services; Pharmaceuticals and medical
products
$62 –23%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
1.0
0
1
“What is bioengineering?,” McKinsey, June 23, 2023.
77

Latest developments
Recent developments involving the future of bioengineering
— CRISPR-based gene therapies are taking a significant
step forward. The FDA and the European Commission
granted regulatory approval for Vertex Pharmaceuticals’
Casgevy, the first gene therapy that uses the renowned
CRISPR-Cas9 technology. The therapy treats rare
blood disorders such as sickle cell disease and beta
thalassemia, marking a significant moment for the
technology.2
— Researchers continue to discover new uses for AI in
bioengineering. Advancements in AI led to additional
successes in bioengineering in 2023. Although
generative AI has been used in the industry for research
and trials, many of the recent advancements in protein
engineering and drug repurposing have used other
proprietary AI algorithms. For example, researchers
at the University of Pennsylvania used an AI ranking
algorithm to reveal a previously unknown use for an
existing drug to treat a man with idiopathic multicentric
Castleman disease (iMCD). While using the drug for this
purpose has not yet been tested or approved at scale, its
utilization in this case shows promising potential.
— Advances in alternative-protein production continue
despite regulatory constraints. There has been renewed
interest in using technologies such as precision
fermentation for producing alternative proteins. Having
already shown its viability, the technology, which is more
sustainable than other forms of alternative-protein
production, was granted safety approval in 2023, with
New Culture becoming the first company to achieve
a generally-recognized-as-safe (GRAS) grading for
its animal-free protein. Cultivated meat, another form
of alternative protein, has seen a mixed regulatory
environment, however. Italy banned the production of
cultivated meat in a bid to protect farmers, while the
Netherlands became the first EU government to allow
cultivated-meat tastings.
2
Julianna LeMieux, “The first CRISPR drug: Vertex Pharmaceuticals’ Casgevy wins U.K. approval for sickle cell disease,” Genetic Engineering & Biotechnology
News, November 16, 2023.
‘We may look back at the past year as the point
in time when gene editing became ‘everyday.’
We now have approved cures for a well-known
and widespread disease like sickle cell anemia
and also have more consumer-oriented products,
including purple tomatoes and glow-in-the-dark
plants. Genetic modification has been around and
commercialized for years but never before in such a
tangible way for the average person.’
– Tom Brennan, partner, Philadelphia
78

Scientist
Research technician
Research assistant
Data scientist
Clinical research coordinator
Medical engineer
Project manager
Process engineer
2019 2023
0
4
8
12
16
20
Biology Biomedical
engineering
Molecular
biology
Pharmaceuticals Bio-
technology
Biology Biomedical
engineering
Molecular
biology
Data
analysis
Data
analysis
Pharmaceuticals Bio-
technology
Gene
therapy
37
19 18 17 16 16
1.0×
8.0×
3.7×
0.1×
3.7×
0.3×
Gene
therapy
13
0.6×
Demand
The future of bioengineering has seen an overall decline in job postings from 2022 to 2023, yet a few roles
have seen growth in 2023. Job postings for research technicians, clinical-research coordinators, and medical
engineers have remained largely consistent with prior years’ postings. However, the scientist role has experi-
enced the most notable decline, possibly due to a surge in demand during the COVID-19 pandemic, along with
a slowdown in investment in 2023.
Skills availability
While the supply of talent in molecular biology is high relative to demand, the supply of talent for more special-
ized areas—such as gene therapy and pharmaceuticals—is low.
79

The adoption of bioengineering technologies is low relative
to other trends, as they are specific to certain industries and
have higher up-front capital investment needs as they reach
commercialization. The energy and materials industries
have seen a relatively high percentage of respondents
demonstrating that they have at least started experimenting
with bioengineering. This likely arises from its potential in
both well-established and nascent use cases—for example,
improved fermentation processes, bio routes to develop
novel materials with entirely new properties (for instance,
alternatives to traditional leather), and broadening biofuels
production to new feedstock sources.
Adoption dimensions
— continued investments in scientific research and
development to support bioengineering innovation
— an increase in regulatory approvals of bioengineering
innovations across most major economies (currently,
medicine developed through the use of CRISPR is
approved in the European Union, the United Kingdom,
and the United States)
— improved public perception and broader social
acceptance of the technologies
— a complex infrastructure to support advanced
bioengineering activities and the ability to scale
operations and production to meet market demands
In real life
The following are real-world examples involving the future of
bioengineering:
— After acquiring Elixirgen Scientific in 2022, Ricoh, a
Japanese digital-services company, sought to leverage
its expertise in digital technologies and AI to create
more reliable disease models, leading to shorter drug
development timelines and increased success rates.
Ricoh accordingly entered into a CRISPR/CRISPR-Cas9
license agreement with ERS Genomics for access to
gene editing technology patents. The initiative aimed
to accelerate personalized medicine, drug discovery
research, and regenerative medicine.
— Japan became the first country to approve a self-
amplifying mRNA vaccine that instructs the body on how
to make more mRNA, as trial results have shown signs
of an increased antibody response relative to traditional
mRNA boosters. This advance could potentially allow
for lower effective dosages and a more resilient immune
response, leading to fewer side effects and longer-
lasting vaccines.
— Tropic, an agricultural biotech company based in the
United Kingdom, used CRISPR to genetically modify
bananas so they stay fresh longer. Conventional
techniques for creating a genetically engineered
organism involve the introduction of foreign DNA
from other organisms, whereas CRISPR offers a
more targeted and precise approach to gene editing.
The Philippines Department of Agriculture has since
classified these bananas as non-GMO and approved
their production.
‘The momentum of progress in bioengineering
remains strong, driven by breakthroughs in generative
AI technologies that have unlocked new pathways
for innovation. The excitement surrounding these
advancements has already led to notable developments,
particularly in areas like protein engineering and drug
repurposing. We can only expect more, and faster,
adoption of these technologies as biopharma and other
industries continue advancing their use of generative AI.’
– Erika Stanzl, partner, Zurich
80

— Insilico Medicine is evaluating its potentially first-in-class
antifibrotic small-molecule inhibitor on lung function in
patients with idiopathic pulmonary fibrosis (IPF). The
cause of IPF, a progressive lung disease characterized
by the formation of scar tissue in the lungs, is unknown,
which makes it challenging to treat effectively.
Researchers at Insilico Medicine used a combination
of machine learning and generative AI to identify a
new therapeutic molecule to create a compound that
demonstrated antifibrotic properties. While this process
usually takes five to eight years, the compound was able
to progress to human trials in only 18 months.
— Unilever announced that it will launch a version of
its Breyers ice cream that consists of whey protein
produced by precision fermentation. The company has
partnered with Perfect Day, a food-tech start-up, to
produce the whey protein to meet its sustainability goals.
— Norfolk Plant Sciences released a genetically altered
purple tomato with high levels of anthocyanins, a type
of antioxidant shown to have anti-cancer and anti-
inflammatory effects. The company began selling the
seeds to farmers and gardeners alike, allowing regular
consumers to “grow biotech” in their own backyard.
Advancements in the following technologies will define the
future of bioengineering:
— Omics. Biological sciences ending in the suffix “-omics,”
such as genomics and proteomics, focus on a different
class of molecule and its functions. Omics are central to
the development of bioengineering applications such as
viral-vector gene therapy (which uses modified viruses
to permanently replace poorly functioning genes that
cause genetic diseases) and mRNA therapy (which uses
messenger RNA to trigger the synthesis of proteins that
can help prevent or fight disease).
— Gene editing. A subset of genomics, gene editing
comprises techniques for modifying an organism’s DNA,
usually using tools such as CRISPR-Cas9.
— Tissue engineering. This technology enables the
modification of cells, tissues, and organs. Tissue
engineering supports various human applications, such
as development of transplantable biomaterials and the
generation of human tissue replicas for drug studies.
Cultivated meat is an example of a product produced
via tissue engineering methods. It is made by taking
a sample of animal cells and growing it in a controlled
environment to produce tissue that is similar to meat
from whole animals.
— Biomaterials. Materials made using bioengineering
technology are known as biomaterials. They fall
into several different categories: bio-based drop-in
chemicals (which can replace chemicals traditionally
made from petrochemicals without changing
surrounding operations), bioreplacements (new materials
made from bio-based chemicals that provide similar
quality and cost but better environmental performance
than traditional chemicals), and biobetter materials
(completely new materials produced via biochemical
synthesis).
Key uncertainties
The major uncertainties affecting the future of
bioengineering include the following:
— Regulation of bioengineering technology and products
will play a part in governing the pace of advancements.
— Public perceptions and ethical concerns regarding the
safety, cost, and quality of bioengineered products
could determine how quickly markets develop. Concerns
about modifying living organisms could also challenge
advancements.
— Unintended consequences could occur, as biological
systems are self-replicating, self-sustaining, and highly
interconnected, and changes to one part of a system
can have negative cascading effects across an entire
ecosystem or species.
Companies and leaders may want to consider the following
questions when moving forward with bioengineering
technologies:
— How will society, in light of its diverse values and
principles, determine an appropriate extent for genome
editing?
— In conjunction with business adoption, how will the public
perceive and adopt bioengineering? For example, how
does cultivated meat fit within existing diets?
— How long will it take for a variety of CRISPR-based gene
therapies to come to fruition and become more socially
accepted for a range of ailments?
81

Rapidly decreasing technology costs over the past decade
have given rise to an increase in the viability and relevance
of space technologies. Lower costs, attributable to
reductions in the size, weight, and power needs of satellites
and launch vehicles, have led to a growing number of
launches and applications for space technologies. We have
seen the rise of wide-scale satellite internet connectivity—
pioneered by SpaceX-owned Starlink, with more than 5,000
low-Earth-orbit (LEO) satellites—and increased private-
market involvement and innovation around launch vehicles.
The growing number of use cases has also attracted
the attention and investment,of non-space-technology
companies that see a series of opportunities within the
realms of remote connectivity, Earth observation across
a spectrum of frequencies, and more. Revenues of the
industry’s “backbone”—that is, space hardware and service
providers—could potentially grow to more than $750 billion
by 2035, but adoption of different space technologies varies
widely.1
While some technologies are deployed and scaling
rapidly, many activities of the future space industry, such as
space mining and on-orbit manufacturing, are still nascent
and will have to navigate an array of technological and
geopolitical hurdles in the coming years.
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.2
0.1
News
Talent demand
Research
Searches
0.3
Patents
Equity
investment
2019 2023
1.0
0.3
0
Scoring the trend
Future of space
technologies
Space technology momentum scores have remained
modest—but shown steady increases—across all
dimensions since 2019.
Agriculture; Aviation, travel, and logistics;
Telecommunications
$9 –9%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
1
“Space: The $1.8 trillion opportunity for global economic growth,” McKinsey, April 8, 2024.
82

Latest developments
Recent developments involving space technologies include
the following:
— LEO satellite communications constellations see
continued growth. LEO satellite constellations are in
various stages of planning and deployment, with multiple
companies launching and deploying constellations for
commercial and government use. After only a few years
of commercial operation, Starlink saw rapid growth in
2023, reaching more than 2.3 million subscribers in over
60 countries and launching new satellites into orbit every
few days.2
Potential competitors, such as Amazon’s
Project Kuiper, are testing their products in hopes of
introducing commercial service soon.
— Interest and expected expansion of direct-to-device
connection continues. Following Apple’s release of its
direct-to-device (D2D) emergency connection on its
iPhone, companies have increased their focus on the
technology to broaden their coverage to remote areas.
SpaceX has completed successful tests in partnership
with T-Mobile. Viasat and Skylo announced the world’s
first global D2D network with industries such as
agriculture, mining, and logistics as target customers.
— Global launch activity increased. Excitement and
attention about launches continued in 2023, with an
estimated 223 attempted launches versus 186 in 2022,
a 20 percent increase.3
Most of these launches are from
US-based companies, primarily SpaceX. However, there
was a significant uptick in activity from other countries,
such as France and India, as they are starting to invest
more in space exploration and involvement. The debut
of Vulcan Centaur—a new methane-fueled rocket from
United Launch Alliance—in early 2024 marked the
beginning of two certification missions, adding a new
launch alternative to the market.
— Lunar activity continued within the private and public
sectors. In January 2024, Japan successfully completed
the country’s first and the world’s most precise moon
landing ever. This event represents the expanded
interest in lunar landings across geographies between
the private and public spheres. A series of private
companies, such as Astrobotic Technology and Intuitive
Machines, have focused heavily on constructing lunar
landers, with Intuitive Machines’ Odysseus managing
to land the first US spacecraft on the moon since 1972.
NASA has recently selected Intuitive Machines, along
with Lunar Outpost and Venturi Astrolab, to advance
capabilities for the lunar terrain vehicle for Artemis
astronauts, enabling them to conduct scientific research
on the moon and prepare for future Mars missions.4
— Integration of offerings into end-to-end solutions is
driven by increased interest from non-space-technology
sectors. Space technology companies are increasingly
focusing on providing end-to-end (E2E) solutions as the
market matures and customers, particularly enterprises,
demand seamless integration with their existing
infrastructure and less complexity with implementation.
We see this through single partnerships between space
tech companies (such as those offering D2D technology
or remote sensing analytics) and enterprises in industries
such as mining, agriculture, and sustainability that are
seeking both hardware and software solutions. For
example, Planet Labs has contracts with Swiss Re and
other insurers to use their satellites and software to both
observe and provide analytics to support parametric
agriculture insurance.5
‘Space continues to accelerate in the public
consciousness, yet adoption is uneven, and funding
is increasingly flowing to “winners.” Real progress is
being made to deliver on the promises of 2020–21.’
– Jesse Klempner, partner, Washington, DC
2
Magdalena Petrova, “Starlink’s rapid growth and influence has made it an indispensable part of Elon Musk’s SpaceX,” CNBC, November 11, 2023.
3
Jonathan McDowell, “Space activities in 2023,” January 15, 2024.
4
“NASA selects companies to advance moon mobility for Artemis missions,” NASA press release, April 3, 2024.
5
“How satellite data is changing agriculture insurance,” Planet Labs, December 6, 2023.
83

Demand
In 2023, the prolonged growth streak of the space technology labor market tapered off. Previously, from 2019
to 2022, the market had consistently seen double-digit growth. Notably, job postings surged during the height
of private-capital investment in 2021, peaked in 2022, and experienced a slight decline in 2023.
Postings for electrical and mechanical engineers have seen growth, while all other major segments have
declined since 2022. Currently, the space industry job market is witnessing a decline in job postings, though
the expectation for long-term growth persists, with established and emerging space disruptors contributing to
the growth.
Skills availability
Job listings within the space technology sector feature numerous technical positions, notably in aerospace
engineering and manufacturing. Additionally, as the use of space technology expands, there is a growing need
for expertise in engineering and data-related fields.
Software engineer
System engineer
Electrical engineer
Mechanical engineer
Project manager
Software developer
Space engineer
Program manager
2019 2023
0
1.0
2.0
3.0
4.0
0.5
1.5
2.5
3.5
4.5
Aerospace
engineering
Manufacturing Systems
engineering
Python Physics Remote
sensing
Space
exploration
Aerospace
engineering
Manufacturing Systems
engineering
Python Physics Remote
sensing
Space
exploration
40
23 16 16 15 15 13
0.8× 1.4×
2.9×
0.5× 0.1×
7.7×
0.7×
84

Space technology tools are more specific to some
industries and, therefore, experience relatively low levels
of reported adoption across the broader market. Perhaps
unsurprisingly, respondents from energy and materials and
telecommunications, media, and technology companies
self-reported that they are scaling or have fully scaled more
than other industries, owing to how central connectivity and
remote sensing are to these sectors.
Adoption dimensions
The adoption trajectory for advanced technologies varies for
— Technological evolution facilitates easier access and
harmonization of space data and creates opportunities for
new revenue streams to emerge. Improved accessibility
and usability enable non-space-technology commercial
players to embrace space data, breaking down technical
barriers and fostering innovative use cases.
— Demand increase—for instance, through regulatory
requirements for observation of key metrics—could
be accomplished by space-based remote sensing in a
broader range of verticals. Substantial legislation where
third-party verification of emissions is either required or
beneficial, such as the EU Deforestation Regulation, can
create incentives for enterprises to use space-based
technologies to monitor their environmental impact.
Growth in demand is also enabled by an increasingly
connected and mobile world, generating demand for
satellite internet, positioning, and navigation services,
and AI- and machine learning–powered insights for
various applications, including disaster response and
early trendspotting.
— Considerable decrease of implementation costs for
companies, aided by further E2E integration of data,
hardware, software, and science-based methods would
enable more enterprises to access the technology,
integrating not only satellite data but also other relevant
insights. For example, it can help in the sustainability
field, with certifiable Scope 3 emissions calculations or
certification needs.
In real life
Real-world examples involving the use of space
— John Deere formed a commercial partnership with
SpaceX’s Starlink to bring D2D connectivity to its
agricultural machinery. This will allow new features
on new and existing machines, such as real-time data
sharing, enhanced self-repair options (for example,
connected support and software updates), and
machine-to-machine communication for farmers in
remote locations.6
— Qatar Airways announced that it will begin installing
Starlink on select planes for passenger Wi-Fi. The
company claims that customers will be able to achieve
speeds of up to 350 megabits per second, which is
faster than in many homes in North America.7
Other
airlines incorporating Starlink include JSX, Hawaiian
Airlines, airBaltic, and ZIPAIR.
— India successfully landed its Chandrayaan-3 lunar lander
on the moon in August 2023. This makes India the fourth
nation to successfully land a spacecraft on the moon8
and the first to land on the unexplored south side.9
— With the International Space Station currently slated
to be retired around late 2030, several commercial
companies are vying to build and operate LEO space
stations.
— The Australian government enlisted the geospatial-
analytics company HawkEye 360 to use its remote-
sensing satellites and radio-frequency data analytics
on a pilot program to detect and prevent illegal and
unregulated fishing activity in the Pacific Islands.
HawkEye 360 operates a constellation of 21 satellites,
with plans to expand to 60 satellites by 2025.10
— Nanosatellite start-up Fleet Space Technologies
purchased equity in mineral exploration company Thor
Energy after raising $33 million in its Series C round
in mid-2023. Together, the companies will perform
mineral exploration tests using Fleet Space’s ExoSphere
technology, which uses their satellites and seismic
array technologies to create 3D models of mineral
exploration sites.
6
“John Deere announces strategic partnership with SpaceX to expand rural connectivity to farmers through satellite communications,” John Deere press release,
January 16, 2024.
7
“Qatar Airways selects Starlink to enhance in-flight experience with complimentary high-speed internet connectivity,” Qatar Airways press release,
October 13, 2023.
8
Nivedita Bhattacharjee, “Chandrayaan-3 spacecraft lands on the moon in ‘victory cry of a new India,’” Reuters, August 23, 2023.
9
Jeffrey Kluger, “How India became the first country to reach the moon’s south pole,” Time, August 23, 2023.
10
“HawkEye 360 working with the Pacific Islands Forum Fisheries Agency for greater maritime visibility in the Pacific Islands,” HawkEye 360 press release,
July 6, 2023.
85

Foundational space technologies include the following:
— Small satellites. Modular small satellites can be custom
built—by using CubeSat architectures and standard-
size building blocks—to perform a widening variety of
missions.
— Remote sensing. Full-spectrum imaging and monitoring
are used to observe Earth’s features, such as
oceanography, weather, and geology.
— SWaP-C advancements. Reductions in the size, weight,
power, and cost (SWaP-C) of satellites and launch
vehicles have increased the cost-effectiveness of space
technology and associated use cases.
— Launch technology advancements. Technology
advancements (for example, computer-aided design
and material sciences), the reuse of booster structures
and engines, the advent of new lower-cost heavy launch
vehicles, and the increases in launch rates are opening
access to space. We see potential for more advanced
launch technologies, such as nuclear propulsion.
— Advanced-connectivity technologies. These
technologies, including laser communications,
electronically scanned antennas, and automated
satellite operations, are expected to progress in the
coming years.
Key uncertainties
The major uncertainties affecting the future of space
— Cost-effectiveness of space technologies is required to
enable further scalability.
— Governance mechanisms need to better define
the allocation of spectrum and orbit usage rights
to accommodate the increasing number of players,
satellites, and applications.
— Cyber risks, including data breaches, malware, and other
cyberattacks, are growing in number and complexity
because of the proliferation of commercial players.
questions when moving forward with space technologies:
— How can leaders define ownership and access rights to
space and space technologies?
— How can the industry build governance structures
around key domains (for example, reducing unintentional
interference, promoting safe operations, protecting
property rights and usage, determining liability, and
encouraging equitable data sharing)?
— How can stakeholders coordinate to manage space
debris and traffic effectively?
— What will future satellite distribution look like (for
example, balance across orbits)?
— How will the market evolve, given a variety of factors
(macroeconomic, the push for E2E solutions, et cetera)?
— How will competition evolve, within the private-launch
market?
— With increasing competition and the risk of interference
and gridlock in spectrum usage, could the current
spectrum allocation system endure?
‘For so long, space has been fascinating yet far from
reality. But now, it is one of the biggest influences
on our daily lives—from guiding us on our daily
commutes to facilitating disaster relief operations.
Space technologies enable impact on Earth.’
– Giacomo Gatto, partner, London
86

A sustainable world
87

Electrification and renewable-energy technologies are crucial
for reducing global carbon emissions in accordance with the
Paris Agreement. Achieving the agreement’s goals requires a
45 percent reduction in global emissions by 2030 and net-zero
emissions by 2050.1
Fortunately, many of the technologies required to achieve these
reductions already exist today and encompass the entire value
chain of energy production, storage, and distribution. These
increasingly important solutions include renewable sources
such as solar and wind power; clean firm-energy sources such
as nuclear and hydrogen, sustainable fuels and bioenergy, and
energy storage; and distribution solutions such as long-duration
battery systems and smart grids.
The shift to clean energy will have far-reaching effects on both
energy-producing and energy-intensive sectors, and it will require
substantial investments in physical assets for energy and land-
use systems. So far, the total investment in physical assets for
energy and land-use systems is still well below the $9.2 trillion
annual investment required to reach net zero by 2050.2
While
capacity, reliability constraints, and rising interest rates could
slow the uptake of clean energy, growing capital spending can
help accelerate adoption. Increased government support on
infrastructure and permitting could likely accelerate adoption as
well. As these technologies become more widespread, closing the
talent gap will also be critical: McKinsey research estimates that
climate technology value chains will require approximately 200
million skilled workers globally by 2050.3
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.4
0.2
0.6
0.8
News
Talent demand
Research
Searches
1.0
Patents
Equity
investment
2019 2023
A SUSTAINABLE WORLD
Electrification
and renewables
1.0
0
Scoring the trend
The electrification and renewables trend had the
highest investment and interest scores among all the
trends we evaluated, with innovation scores close to
the group average.¹ These positions align with the
maturity and scaling of renewable technologies,
particularly photovoltaic-solar and wind power.
Moreover, they reflect the level of investment required
to meet global net-zero pathways.
assembly; Aviation, travel, and logistics; Chemicals;
Construction and building materials; Electric power,
natural gas, and utilities; Metals and mining; Oil and
gas; Real estate
$183 +1%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
¹PitchBook data for closed deals across all investment types, based on keywords
(consistent with the 2022 Global Energy Perspective report).
1
“For a livable climate: Net-zero commitments must be backed by credible action,” United Nations Environment Programme, November 2023.
2
Mekala Krishnan and Lola Woetzel, “Infrastructure for a net-zero economy: Transformation ahead,” McKinsey, April 6, 2022.
3
“What would it take to scale critical climate technologies?,” McKinsey, December 1, 2023.
88

89
Latest developments in 2023
Recent developments involving electrification and
renewables include the following:
— Renewable generation grows amid challenges. Despite
high interest rates and an increased focus on energy
security, particularly in Europe, 2023 was a record
year for renewable-capacity installation. An estimated
50 percent more renewable-generation capacity
(totaling 507 gigawatts) was added globally compared
with 2022.4
This rapid deployment was underscored
at COP28, where countries committed to tripling
renewable-energy capacity and doubling energy
efficiency by 2030.5
Utilities for Net Zero Alliance
(UNEZA), an international platform for cooperation in
power and utilities, confirmed the difficulty of achieving
that goal without grid buildout. Further innovation,
government support, and funding, particularly for
emerging economies, will be necessary to continue
the momentum toward global decarbonization by
2050, especially considering that emissions and gas
consumption were at all-time highs in 2023.
— Public sector support for hydrogen increases, but
implementation still lags. Green hydrogen remains
an important piece of the clean-energy puzzle—for
example, hydrogen is used in processes such as
hydrocracking or hydrotreating at refineries. Recent
incentives such as the US Clean Hydrogen Production
Tax Credit, the EU Important Projects of Common
European Interest, and the UK Low Carbon Hydrogen
Agreement demonstrate growing interest in public
sector support for advancing the green-hydrogen
ecosystem and ultimately allow for economies of
scale despite existing cost barriers.6
Despite the new
incentives, private sector hydrogen adoption remains
relatively small, with only 1.0 percent of global production
(and 0.7 percent of demand in existing applications)
coming from low-emission hydrogen.7
Factors
contributing to slow adoption include the challenge to
balance clean-hydrogen production with the growing
demand for power, the high-interest-rate environment,
and incentives that mainly target new applications
instead of scaling existing uses.
— Global battery storage capacity is scaling rapidly. In
2023, lithium-ion battery pack prices dropped by 14
percent and are expected to decline further in 2024,
with demand for mobile and stationary battery storage
increasing by more than 50 percent year over year.8
McKinsey analysis projects that demand for lithium-ion
batteries will scale up to six times to 4,700 gigawatt-
hours by 2030, with mobility applications encompassing
a vast majority of the market.9
Established policy
incentives, including Europe’s Fit for 55 program, the
US Inflation Reduction Act, the European Union’s 2035
ban on internal-combustion-engine vehicles, and
India’s scheme for faster adoption and manufacture of
hybrid vehicles and electric vehicles (EVs), demonstrate
public sector interest in continued EV adoption.
However, widespread adoption of EVs will likely
hinge on the expansion of charging infrastructure, as
well as improvements in mileage and performance.
Innovations such as solid-state batteries, which promise
significant enhancements, are still years away from
commercialization.
— Policy incentives look to spur stalled heat pump
adoption. In 2023, global heat pump installations
declined (approximately 3 percent) from their peak at
111 gigawatts in 2022,10
with a 17 percent decline in the
United States alone.11
While studies have shown that
heat pumps can drive long-run cost and energy savings
for residential applications, high installation costs and
variable natural gas prices have created a hesitancy in
further consumer adoption.12
However, existing
subsidies for heat pump installation throughout the
European Union13
and new tax credits in 2023–24
through the US Inflation Reduction Act14
could look to
reinvigorate demand.
4
Johnny Wood, “Energy transition: The world added 50% more renewable capacity last year than in 2022,” World Economic Forum, February 8, 2024.
5
A world energy transitions outlook brief: Tracking COP28 outcomes: Tripling renewable power capacity by 2030, International Renewable Energy Agency,
March 2024.
6
“Global Hydrogen Review 2023: Executive summary: Low-emission hydrogen production can grow massively by 2030 but cost challenges are hampering
deployment,” International Energy Agency, 2023.
7
Ibid.
8
“Lithium-ion battery pack prices hit record low of $139/kWh,” BloombergNEF, November 26, 2023.
9
Kersten Heineke, Philipp Kampshoff, and Timo Möller, “Spotlight on mobility trends,” McKinsey, March 12, 2024.
10
“Executive summary: Heating is a fundamental service to society that needs to be decarbonised further” in The future of heat pumps in China, International Energy
Agency, 2024.
11
Casey Crownhart, “This chart shows why heat pumps are still hot in the US: Sales slowed in 2023, but heat pumps are gaining ground on fossil fuels,” MIT
Technology Review, February 12, 2024.
12
“Benefits of heat pumps detailed in new NREL report: Millions of homes can benefit today, but installation costs keep technology out of reach for some,” National
Renewable Energy Laboratory news release, February 12, 2024.
13
Subsidies for residential heat pumps in Europe, European Heat Pump Association, April 2023.
14
“This chart shows why heat pumps are still hot in the US,” MIT Technology Review, 2024.

¹The ratio of talent supply to demand is based on skills listed in McKinsey’s Organization Data Platform (ODP) job postings compared with LinkedIn users’ skills, filtered by a set
of keywords specific to each trend.
Demand
Between 2019 and 2022, electrification and renewable-energy technologies saw tremendous growth, withjob
postings increasing more than 250 percent (37 percent CAGR). The trend also demonstrated a noteworthy lack
of cyclicality, as job postings increased approximately 1 percent between 2022 and 2023, compared with the
average 26 percent reduction across all trends over the same period. This growth coincides with public sector
incentives that have allocated capital toward infrastructure improvements.
Talent availability,¹ ratio of talent to demand
Skills availability
There is a broad shortage of experienced talent throughout electrification and renewable-energy technologies,
with gaps for professionals with deep knowledge of specific renewable technologies (for example, photovolta-
ics and wind power) and installation (for instance, construction). To scale electrification and renewable-energy
technologies to meet global decarbonization timelines, the supply of experienced construction and mainte-
nance professionals will need to increase in line with projected clean-energy capacity demand.
Electrical engineer
Project manager
Mechanical engineer
Software engineer
Engineering manager
Technician
Energy manager
Business development manager
0
14
12
10
8
6
4
2
2019 2023
1.8×
0.3× 0.7× 0.8×
1.8×
0.1×
Renewable
energy
Photovoltaics Construction Sustainability Wind
power
Contract
management
Renewable
energy
Photovoltaics Construction Sustainability Wind
power
Contract
management
50
24 23 12 10 7
90

91
More than 40 percent of survey respondents self-reported
that they already are piloting, are scaling, or have fully scaled
their electrification and renewable-energy technologies.
Energy and materials and technology companies showed
the greatest adoption across industries, indicating the
significant impact electrification and renewables will have
throughout the energy value chain.
Adoption dimensions
— Further adoption of electrification and renewable-
energy technologies depends on reducing costs, which
can be achieved through tech advances and investments
to scale. Material efficiency will then become more
relevant, since the supply of materials such as lithium,
steel, and copper could become constraints if the speed
of low-carbon energy generation increases to the levels
required to keep pace with global net-zero commitments.
— The future energy mix may involve ramping up
infrastructure development for production of emerging
technologies such as green hydrogen, batteries, and
sustainable fuels.
— Accelerating innovation and investment in infrastructure
for power transmission and distribution, battery storage,
EV charging, and smart-grid load management can
facilitate the clean-energy transition. Streamlined
permitting processes can reduce project lead times and
facilitate rapid scale-up.
— Deploying electrification and renewable-energy
technologies at the speed and scale required for global
decarbonization commitments could be achieved
through a step-change increase in the supply of clean-
energy technology professionals.
— Cross-regional collaboration can help harmonize
standards, accelerate the global adoption of renewable-
energy technologies, and align on global energy security
policies.
In real life
Real-world examples involving the use of electrification and
— Aira is a Swedish-founded, UK-based clean-energy
technology company disrupting the European heat
pump market with a vertically integrated, subscription-
based business model. The company overcomes
consumer concerns about navigating complex regulation
and high installation costs by providing end-to-end
customer support through home energy assessments,
grant application assistance, installation, and lifetime
system maintenance while spreading up-front costs
over monthly installments. Aira heat pumps can be fully
controlled using the Aira app and deliver significant
energy and cost savings. Over the next ten years, the
company has set the goal of serving five million homes
with its clean-energy technology solutions.
— EV OEMs are forming strategic partnerships to be at the
forefront of battery technology. Stellantis has made an
investment in Lyten to accelerate the commercialization
of 3D Graphene applications to lithium–sulfur EV battery
technology. The technology produces lithium–sulfur
batteries without using nickel, cobalt, or manganese,
which could potentially result in an estimated 60 percent
lower carbon footprint than current best-in-class
batteries.15
The rate of innovation for lithium–graphene
batteries is spurred by a rapid increase in related patent
filings.16
Adoption potential is significantly boosted by
anticipated decreases in graphene costs as production
scales up, making these advanced batteries more
economically viable for broader markets.
— Cloud hyperscalers are investing heavily in renewable
energy. For example, Amazon directly invested in more
than 100 new renewable-energy projects in 2023,
increasing its total portfolio to over 500 projects
globally, with a total installed generation capacity of
more than 77,000 gigawatt-hours per year.17
15
“Stellantis invests in Lyten’s breakthrough lithium–sulfur EV battery technology,” Stellantis press release, May 25, 2023.
16
Oliver Gordon, “Graphene is set to disrupt the EV battery market,” Energy Monitor, February 5, 2024.
17
“Amazon is the world’s largest corporate purchaser of renewable energy for the fourth year in a row,” Amazon press release, January 16, 2024.

‘The technologies that enable the transition to clean
energy are critical to delivering approximately
50 percent of the required solution for net zero.
Acceleration of these technologies is critically
important. Above all, we need ambition to accelerate
the transition.’
– Mark Patel, senior partner, Bay Area
92
Foundational technologies in electrification and renewables
— Batteries. These devices store chemical energy
and convert it into electricity. They are applicable to
traditional energy sources as well as renewables such as
wind and solar.
— Heat pumps. These devices extract heat from a source
(that is, from air, ground, or water) and transfer that heat
from evaporator to condenser, proving to be 2.0 to 4.5
times more efficient than a traditional furnace or boiler.18
They also function as air conditioners, transferring heat
from internal spaces to outside.
— Energy storage. These technologies, including
batteries, capture energy from various sources, such
as electrochemical, thermal, mechanical, and chemical
systems, to be used later.19
— Nuclear fission. Nuclear fission, the process of splitting
large atoms to create energy, is a proven zero-carbon
power source. Concerns about accidents and radioactive
waste persist. However, the growing demand for clean
energy is reinvigorating efforts to expand nuclear power
capabilities. (There is also growing interest in nuclear
fusion, the process of combining small atoms to produce
energy, but significant technical challenges remain to be
solved.)
— Renewables. These are energy sources produced by
natural power resources. New technologies such as
advanced solar photovoltaics and both onshore and
offshore wind turbines are driving significant growth
from traditional renewable sources.
— Hydrogen. This is a versatile energy carrier that can be
produced with minimal or zero-carbon emissions using
electrochemical energy conversion technologies.
— Sustainable fuels. These are fuel alternatives to
traditional fossil hydrocarbon fuels, including both low-
carbon fuels and fuels derived from natural or alternative
feedstock (for example, biomass, hydrogen, e-ammonia,
and e-methanol-based fuels).
Opportunities to leverage other tech trends with
electrification and renewables technologies include the
following:
— Future of mobility. There are expanded applications
of electrification and renewables in transportation; for
example, innovative battery technologies can transform
micromobility applications.
— Applied AI. Real-time smart-grid monitoring enables
dynamic energy pricing models and more efficient
charging.
— Industrialized machine learning. Predicting green-
hydrogen production potential from organic waste can
enhance efficacy and yield.
— Immersive reality. Blueprints combined with augmented
reality headsets could allow heat pump installers to see
necessary ductwork changes and installation steps.
18
“How a heat pump works,” in The future of heat pumps, International Energy Agency, December 2022.
19
“Energy storage: How it works and its role in an equitable clean energy future,” Union of Concerned Scientists, October 4, 2021.

Key uncertainties affecting the trend
The major uncertainties affecting electrification and
— Concerns exist about the high costs of scaling
renewables, generating clean firm power, and supporting
infrastructure.
— Balancing necessary transmission and distribution
investments with uncertain adoption timelines for EVs,
heat pumps, and other electrification technology could
create challenges for efficient capital deployment.
— Government climate policies and regulation can
dramatically alter the timeline of climate technology
adoption.
— Reskilling and transitioning skilled labor from legacy
industries to electrification and renewables will be a
massive workforce challenge but could also present an
incredible opportunity for inclusive job growth around
the world.
questions when moving forward with electrification and
renewables:
— How will innovations in battery storage technology
influence the adoption of EVs and renewable assets?
— How can public–private stakeholders collaborate to
manage existing and emerging energy systems in
parallel while ensuring energy security and grid stability?
— How will regions and organizations leverage the new
comparative advantages brought on by an electrified
world while increasing energy access and ensuring job
security for employees of legacy industries?
— How will the power sector increase the talent pool
of workers with skills specific to electrification and
renewable-energy technology?
— Will emerging economies choose to supply growing
populations with clean energy despite existing
infrastructure hurdles20
and cost competition from
hydrocarbons?21
20
Gracelin Baskaran and Sophie Coste, “Achieving universal energy access in Africa amid global decarbonization,” Center for Strategic & International Studies,
January 31, 2024.
21
Carl Greenfield, “Energy system: Fossil fuels: Coal,” International Energy Agency, March 26, 2024.
‘Scaling renewables and electrification technologies
requires cost reductions, substantial investments, and
talent. Despite challenges, 2023 marked significant
strides in renewables, battery storage, and hydrogen
support. However, cost competitiveness, raw materials,
manufacturing capacity, labor transition, and
infrastructure remain hurdles to leaping ahead.’
– Sebastian Mayer, partner, Munich
93

Climate technologies beyond electrification
and renewables
cover technologies related to circularity and resources and
carbon capture and removal. The production of sustainable
goods and services can support companies in terms of complying
with emerging regulations, creating growth opportunities, and
attracting talent. While many technologies that mitigate the
environmental impact of consumption are technically viable,
few have become cost-effective enough—or have overcome
other hurdles, such as labor upskilling and funding—to achieve
mass scale. The scope of the challenge is also unprecedented:
according to some estimates, an additional removal capacity of
0.8 to 2.9 metric gigatons of CO2
per year is required by 2030
(to be on the pathway to net-zero emissions by 2050)—three
to ten times more than the volumes currently estimated to be
onstream by that date.1
However, opportunities for innovators to
capture value through scale are apparent, since a carbon removal
market capable of enabling gigaton-scale removals at net-zero
levels could be worth up to $1.2 trillion by 2050.2
To close the
gap between aspirations and commitments, a step change in
investment equal to about 0.1 percent of global annual GDP (about
$120 billion) could be necessary.3
Talent demand Ratio
of skilled people
to job vacancies
Equity investment
Patents Patent
related to trend
News Press reports
featuring trend-
related phrases
Searches Search
engine queries for
terms related to
trend
Research Scientific
0.2
0.1
0.3
News
Talent demand
Research
Searches
0.4
Patents
Equity
investment
Scoring the trend
and renewables
Despite a decline in private investment since 2021, the
investment score for climate technologies beyond
electrification and renewables remains above average
compared with other tech trends, which highlights the
importance of climate solutions in mitigating the
challenges brought on by climate change.¹ Interest and
innovation have maintained their momentum of
increasing scores year over year, with a slower pace of
innovation being indicative of how the capital intensity
of and long development timelines for new projects are
creating challenges for scaling technologies.
assembly; Aviation, travel, and logistics; Chemicals;
Construction and building materials; Electric power,
natural gas, and utilities; Metals and mining; Oil and
gas; Real estate
$68 –11%
1 2 3 4 5
Fully
scaled
Frontier
innovation
Equity investment,
2023,
$ billion
Job postings,
2022–23,
% difference
2019 2023
1.0
0.4
0
A SUSTAINABLE WORLD
¹PitchBook data for closed deals across all investment types, based on keywords
(consistent with 2023 report).
1
Carbon removals: How to scale a new gigaton industry, McKinsey, December 4, 2023.
2
Ibid.
3
“Global Energy Perspective 2023: CCUS outlook,” McKinsey, January 24, 2024.
94

95
Latest developments in 2023
Recent developments involving climate technologies
beyond electrification and renewables include the following:
— Companies are expanding their commitments. This
is evidenced by a moderate increase in targets across
various dimensions and a more notable surge in
corporate climate commitments. Currently, about 80
percent of companies in the Fortune Global 500 have
set carbon reduction targets. Beyond carbon, the
dimensions attracting the most attention include water,
chemicals and plastic, biodiversity, and forests. The year
2023 witnessed a 30 percent year-over-year increase
in the number of companies setting targets in three or
more dimensions of nature (for example, air, water, land).4
Europe has the greatest share of companies with nature
targets, likely galvanized by a set of nature-related
regulations, including the Corporate Sustainability
Reporting Directive, EU Deforestation Regulation, and—
most recently—the EU Nature Restoration Law. Similar
public sector action in other regions could encourage
companies in all sectors to implement nature-positive
levers with opportunities for an annual net benefit of up
to $700 billion.5
— The adoption of agriculture technology (agtech)
solutions for sustainable farming is growing, though
penetration is currently relatively low. While sustainable
agricultural practices that require behavior change
(such as cover crops and afforestation of degraded
cropland) have the highest adoption, practices that
leverage technology and require a product or equipment
change are not adopted at full scale yet, according to
the latest US farmer survey.6
However, we see advances
in these agtech solutions. For example, variable-rate
fertilizer applications (VRA) benefit from advances in
AI to generate high-precision VRA maps to account
for field variability. This can help reduce the amount of
fertilizer applied, which, in turn, reduces greenhouse
gas emissions caused by excess nitrogen. Similarly,
weed control robots are advancing and allow for
the reduction of tillage and herbicide applications,
which benefits carbon sequestration and reduces
water pollution. Satellite-enabled remote-sensing
technologies help to assess reduced tillage, sustainable
practices for cover crops, and plant health. Further
adoption of these technologies requires helping farmers
overcome operational challenges and the risks that
new technologies might entail—for example, through
insurance and the promotion of potential yield, cost, and
revenue benefits (such as carbon credits).
— Public sector support for carbon management initiatives
is increasing. Government agencies across the globe are
leveraging a number of instruments, including grants,
regulations, and tax breaks, to support the development
and adoption of carbon management technologies.
For example, in addition to other investments, the US
Department of Energy recently awarded $13 million
in funding to 23 projects focused on R&D in carbon
capture through the Office of Fossil Energy and
Carbon Management.7
This funding aims to support
innovative solutions and advancements in carbon
capture technologies. Furthermore, the US Department
of Energy has allocated up to $1.2 billion, through the
Office of Clean Energy Demonstrations, to support two
direct air capture (DAC) projects in Louisiana and Texas.8
These DAC hubs could play a crucial role in developing
and demonstrating large-scale carbon removal
technologies. These initiatives from the Department of
Energy may indicate a significant focus on addressing
carbon management challenges and accelerating the
development of sustainable solutions in the fight against
climate change.
— Carbon capture, utilization, and storage (CCUS) has seen
a step change in interest. In the past year, the global
CCUS market has seen a step change in interest. If all
announced projects in the pipeline are realized by 2030,
current capacity could increase by 12 times.9
However,
many of these projects still need confirmed funding and
final decisions on executing the buildout. This projected
increase in interest coincides with a proliferation of
CCUS start-ups, new technologies, and private sector
interest in forward agreements and long-term carbon
offtakes. These agreements allow developers to
finance CCUS infrastructure more easily, since they can
demonstrate proof of an existing customer base prior to
construction. Such market dynamics are encouraging
signs for global net-zero ambitions, considering the
expectation that CCUS capacity needs are expected to
increase by over 100 times by 2050.
4
“Companies are broadening their commitments to nature beyond carbon,” McKinsey, December 8, 2023.
5
Nature in the balance: What companies can do to restore natural capital, McKinsey, December 5, 2022.
6
“Voice of the US farmer 2023–24: Farmers seek path to scale sustainably,” McKinsey, April 9, 2024.
7
“DOE invests over $13 million for projects that capture carbon emissions from industrial facilities, power plants, air, and oceans,” Office of Fossil Energy and Carbon
Management, August 9, 2023.
8
“Biden-Harris administration announces up to $1.2 billion for nation’s first direct air capture demonstrations in Texas and Louisiana,” US Department of Energy,
August 11, 2023.
9
“Global Energy Perspective 2023,” January 24, 2024.

Demand
Between 2019 and 2022, climate technologies beyond electrification and renewables saw significant growth,
with job postings increasing by over 96 percent (25 percent CAGR). Climate technology jobs were also less
affected by macroeconomic conditions, as they declined by only 11 percent from 2022 to 2023, compared with
the average 26 percent reduction across all trends over the same period. This relatively strong labor demand is
supported by significant public sector incentives, such as the Inflation Reduction Act, and initiatives like the
European Union’s Net-Zero Industry Act, providing increased public sector support for the reduction of
greenhouse gas emissions and for sustainable production methods.
Skills availability
Climate technologies beyond electrification and renewables face shortages of workers with industry-specific
and manufacturing or construction skills. To meet global net-zero timelines, the supply of skilled workers with
climate-tech-specific skills will need to scale in line with the overall trend.
2019 2023
Technician
Project manager
General supervisor
Process engineer
Warehouse worker
Energy manager
Energy consultant
0
2
4
6
8
10
0.8×
2.8×
0.7× 0.5× 0.8× 0.2× 0.4×
Sustainability Energy
efficiency
Construction Waste
management
Manufacturing Hazardous
materials
Regulatory
compliance
Sustainability Energy
efficiency
Construction Waste
management
Manufacturing Hazardous
materials
Regulatory
compliance
16 15 13 10 9 6 5
96

97
Adoption levels for climate technologies beyond
electrification and renewables currently sit in the middle of
all our trends, with over 50 percent of companies reporting
they have invested in the trend. As these technologies move
down the cost curve, adoption is expected to increase.
Respondents from energy and materials companies self-
reported the greatest level of progress toward scaling
capabilities, given the high relevance of circular-technology
applications and carbon management to several industries
within the sector.
Adoption dimensions
are diverse, each having unique development timelines
and use cases. The adoption trajectory for advanced
technologies will look different for each technology and
each use case within that technology. Advancements along
the following dimensions could enable the next level of
adoption for these technologies:
— scaling of circular technologies, which require
collaboration across the supply chain to administer
collection, recycling, and reintroduction into the value
chain
— regulatory clarity to promote investor and consumer
confidence in alternative proteins and near-term
innovation to maximize flavor and decrease costs for
the many emerging alternatives that are still at the
precommercial stage
— increased trust in and broader recognition of carbon
abatement and removal schemes, including increased
valuation of the environmental and human health
cobenefits from nature-based carbon removal
— lower unit costs for measurable carbon abatement and
removal projects, as well as for products and materials
with low-carbon equivalents
— accelerated deployment of carbon capture and storage
technologies to be achieved by enhancing funding,
research, and international collaborations to improve
feasibility and scalability
— pursuing net-zero or nature-positive operations over
an extended time horizon, which requires an enduring
ambition
In real life
Real-world examples involving the use of climate
technologies beyond electrification and renewables include
the following:
— The circularity solution led by Schneider Electric, a digital
automation and energy management company, shows
an example of how end-to-end operations can be set up
in a circular approach. Through ecodesign, waste-to-
resources sites, and a global network of refurbishment
centers, this IoT-enabled architecture and platform has
helped its customers to avoid more than 500 million
metric tons of CO2
since 2018 and to use 27 percent
green material content across products.10
— A group of investors, including Equinor and
TotalEnergies, partnered on the first phase of the
Northern Lights project, a first-of-its-kind carbon
capture and storage infrastructure open to third-party
industrial emitters. The program will facilitate the
transportation and permanent storage of liquefied CO2
from the European continent to the reservoirs beneath
the North Sea. Infrastructure completion and initial
deliveries are on track for 2024.11
— Running Tide is an ocean health company that
processes, quantifies, and provides carbon credits
through ocean-based, natural climate solutions.
The company uses two main carbon sequestration
techniques—terrestrial biomass sinking and ocean
alkalinity enhancement—to capture and store oceanic
carbon. It then quantifies carbon removal, using in situ
measurements, lab testing, and sophisticated ocean
models to offer accredited carbon credits with high
confidence in sequestration permanence. To date, the
company has removed over 25,000 metric tons of CO2
equivalent and sold about 22,000 carbon credits.12
— Among other agricultural and food companies, Cargill
expanded its existing sustainable agriculture program,
Cargill RegenConnect, to four European countries
(France, Germany, Poland, and Romania). The program
pays farmers to adopt agricultural techniques that
pull carbon from the atmosphere into the soil—such
as cover cropping and no-till farming—based on the
market rate per ton of carbon sequestered in their soil
and helps them leverage remote sensing and crop and
soil health monitoring. It also helps connect farmers
10
“Circular economy: How ‘lighthouses’ in the built environment can drive value,” World Economic Forum, January 15, 2024.
11
“What we do,” Northern Lights, accessed May 31, 2024.
12
“Carbon credits,” Running Tide, accessed May 31, 2024.

‘Carbon management technologies to reduce and
remove carbon from the atmosphere will be an
essential part of the world’s journey to net zero.
Creating a gigaton-carbon-management industry in a
few short decades to meet the climatic need presents an
enormous challenge and an exciting opportunity for
innovators, investors, and policy makers alike.’
– Emma Parry, partner and global colead of carbon management service line, London
98
to other members of the downstream supply chain
and provides training and mentorship on sustainable-
farming techniques through agronomic experts. Cargill
is committed to supporting farmer-led regenerative
agriculture on ten million acres of farmland by 2030.13
There are varieties of climate technologies beyond
electrification and renewables:
— Circular technologies. Design and production techniques
and engineered materials can increase recycling and
reuse and minimize waste.
— Natural climate solutions. Nature-based projects
can remove carbon or prevent emissions from being
produced. Those can include terrestrial ecosystems
(for example, afforestation), peatland restoration, fire
management, and agricultural management (such as
through optimizing grazing pathways and cover crops).
— Alternative proteins. These proteins can be produced
from natural sources with significantly fewer emissions
than animal proteins. These sources include plants
(for instance, soybeans, wheat), microorganisms (such
as through microbial fermentation), and cultivated
animal cells.
— CCUS. CO2
can be captured—before being emitted
into the atmosphere—typically directly from the point
of production, such as at industrial facilities and power
plants that use fossil fuels. The utilization of CO₂ and its
sale as a product offer a revenue source to offset
the cost of capture. One of the primary uses of CO₂
today is enhanced oil recovery; other uses also are
gaining momentum.14
— Engineered carbon removal. Various technologies can
remove atmospheric CO2
, including direct air capture and
storage, bioenergy carbon capture and storage, biochar
and bio-oil, and enhanced weathering.
Opportunities for the integration of climate technologies
beyond electrification and renewables with other tech
trends include the following:
— Applied AI. AI technologies can be used to increase the
efficacy and efficiency of carbon capture systems.
— Advanced connectivity. Advanced connectivity can
improve real-time crop monitoring and automated
micro-irrigation.
— Future of bioengineering. Bioengineering can help in
the development of genetically modified varieties of
trees, crops, and seaweed that absorb more carbon with
fewer inputs.
— Future of space technologies. More advanced satellites
can monitor CO2
concentrations and soil and ocean
health and validate, quantify, and authenticate carbon
management schemes.
13
“Digging in: Cargill’s regenerative agriculture program brings healthier soil and profits to more European, U.S. farmers,” Cargill, May 23, 2023.
14
“Scaling the CCUS industry to achieve net-zero emissions,” McKinsey, October 28, 2022.

‘Nature is the technology that we have available today
for the next critical decade and can help solve the net-
zero equation. If you want to think about a cost-effective
way of capturing carbon or avoiding carbon loss today,
it is through natural systems. And nature’s import goes
far beyond its role in the climate: let’s not forget we are
dependent on it for our lives and livelihoods.’
– Joshua Katz, partner, Stamford
99
Key uncertainties affecting the trend
The major uncertainties affecting climate technologies
beyond electrification and renewables include the following:
— Public sector incentives such as potential policies and
regulations could be pivotal in shaping investment
decisions, business case viability, and public reactions to
carbon management schemes.
— Natural-capital valuation through different carbon
management incentive structures allows organizations
to apply varying valuations for the cobenefits of natural-
capital solutions, relative to pure CO2
removal.
— Coordination throughout the value chain could be a
challenge, as scaling carbon abatement and removal
infrastructure is time and capital intensive. The need for
coordination between public and private stakeholders at
the local level could be an additional obstacle.
— Standardization of the carbon market could prove
difficult, as it is unclear how recent commitments by
independent carbon credit standards to enhance
transparency and consistency will affect trust in carbon
management schemes to follow through on promises.
questions when moving forward with climate technologies
beyond electrification and renewables:
— How will carbon management schemes overcome
potential bottlenecks (for instance, raw materials, land,
and infrastructure) as R&D, experience, and economies
of scale help propel adoption?
— How will debates about the efficacy of nature-based
carbon removals compared with technology-based
carbon removals affect investment decisions and public
perception?
— Can innovations in CCUS technologies lead to significant
cost reductions and expanded use cases?
— Can independent carbon credit agencies successfully
meet certification criteria and increase scoring
transparency to build trust and reliability in the voluntary
carbon market?
— How will consumers react to the continued innovations in
alternative proteins?

McKinsey & Company
July 2024
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