How do you integrate AI and BI into your workflow?

I have found the Jobs-to-be-done framework an excellent approach for the purpose of integrating AI and BI into workflows. Start with the idea that everything in an organization is doing some kind of "job". Using AI and BI to upgrade how a job is done should lead to greater success and acceptance. In contrast, trying to change job descriptions leads to confusion and much greater need for change management. While such transformations are sometimes needed, start with the low lying fruit.

I'm working with ChatGPT for day to day analysis of data being generated by the sales department , it makes understanding the outcome easier and sometimes gives further insights which I was not aware of

Based on my experience, AI should be able to integrate the goals and objectives of the business to create business intelligence.

There is a well-tested structured approach that I recommend. 1. Identify Stakeholders: Spot business leaders, project managers and product owners who AI can empower. Map AS-IS and TO-BE states with AI streamlining tasks and amplifying productivity, transforming processes and services. 2. Measure Impact: Gauge stakeholders' needs for business impact. Identify groups with aligned goals, laying the foundation for the next steps. 3. Engage Strategically: Conduct insightful problem interviews. Navigate political intricacies and hidden agendas to remove potential blockers. 4. Prioritise Initiatives: Outline and prioritise initiatives with the lowest time-to-value (i. e. the low-hanging fruits) and the most significant business impact.

For day today insights and also for key performance indicators, you can get the results using natural language using Open AI integrating with BI, which acts as a self-serve tool.

We need to start with the outcome and what we want to achieve. Is our data clean rubbish in = rubbish out. Then we need to consider the people and involve them and get them onboard with anything that affects them and their role.

BI acts based on the exact questions and fact business answers. AI predictions can improve using BI data. This will open a new era in field of business decision making with less influence of human biases and more machine made fact based decisions without involvement of human.

The integration of AI and BI into a workflow starts with a comprehensive understanding of organizational objectives. A well-defined goal ensures that the AI and BI solutions are aligned with the organizational vision and mission. Here, collaboration between different departments, such as IT, marketing, and operations, is vital to ensure that the AI and BI implementations meet diverse needs across the organization. Whether it's improving customer experience, optimizing supply chain management, or enhancing predictive analysis, clearly outlining the purpose will streamline the integration process.

AI enhances data processing, uncovering fraud indicators, and provides predictive modelling for risk forecasting. BI facilitates analytical insights for decision making, strategic planning, and alignment with regulatory standards. Automation and operational excellence are achieved through AI's ability to streamline tasks and BI's automated reporting. Customised AI models alongside adaptive BI tools for data needs offer a tailored approach to compliance and risk management. Unified systems create seamless collaboration between AI and BI within detection, streamlining processes, and supporting decision-making. Emphasis on ethical considerations, data privacy, and transparency fortifies commitment to industry best practices and compliance.

Integrating AI and BI enhances decision-making and efficiency. Align strategies with goals by assessing needs. AI automates processes, analyzes data, and reveals insights. Leverage AI-powered analytics for accurate predictions in BI. Embrace machine learning for forecasting and anomaly detection. Use NLP and computer vision for unstructured data. Collaborate for robust pipelines, data quality, and security. Continuously update AI models and refine BI reports based on insights. Combine AI's predictive capabilities with BI's power for faster decisions. The synergy of AI and BI unlocks valuable knowledge, driving innovation and growth. Maximize potential by embracing their transformative integration.

I would not call AI and BI tool as it was referred in the first paragraph " All collaborative articles Engineering Artificial Intelligence (AI) How do you integrate AI and BI into your workflow? Powered by AI and the LinkedIn community Artificial intelligence (AI) and business intelligence (BI) are two powerful tools that can help you improve your workflow" It should be referred as technology or more something you find more approporiate.

Something I've noticed is that data is the fuel that drives AI, and the engine that powers BI. Viewing data as an integral part of AI and BI is key to sustainable development

In my experience, dataset is one of the most important aspect of AI that need to be looked at for proper blending between AI and BI to work efficiently.

If I could give advice, I would like to give it about tools, programming languages, frameworks. Advice: never fall in love with a language, programming tool. Have one as a base but be open to learning others. Don't focus 100% of your energy on a single specialization. Follow the trends and always evaluate.

The right tools and platforms are primarily determined by the identified data needs and gaps, which further determines how well stakeholders understand the direction they want their projects and products to develop. The next step is to catalogue the data that is available: data sources, internal 2nd and 3rd party data, types of data, data volume, real-time or near-real-time processing needs, as well as privacy and cybersecurity concerns. The final step is to attract internal and external technical experts to find the perfect match between available tools and platforms and the business needs. Sometimes internal open-source-based solutions may be needed to develop.

Selecting the right tools and platforms for AI and BI integration is akin to laying the foundation for a building. The selection should not only be based on current requirements but also future scalability and adaptability. Consideration for open standards, community support, and the provider's reputation can be as crucial as cost and complexity. Consulting with a diverse team including data scientists, software developers, and industry experts can provide valuable insights into making the right choice that aligns with your business’s unique requirements.

There are tools that use AI & BI discretely and then there are AI-enabled BI tools. The selection of tools depends on the organization's specific needs, technical capabilities, and budget amongst others. 1. Primarily, need to clearly define the business needs, identify the data sources, and validate the integrations pointers and means of integration. 2. Consider a user-friendly interface that is easily customizable and scalable. 3. Visualization and Reporting of the data are also very important as they will be the final outcome of communication. 4. Data security and compliances are critical considerations. 5. Training, technical and operational support, and the future roadmap of the product are important.

Most AI and BI solutions create problems because of poor data cleaning and analysis. A huge task, data mining and cleaning can save lives

Implementing AI and BI solutions often overlooks the value of recognizing prior successes and relevant use cases. Trace past AI/BI deployments aligned with your challenges. Likely, similar problems were addressed internally or externally. Tap into your network of experts for insights and guidance. Leveraging shared experiences can streamline effective problem-solving, optimizing your outcomes.

The critical component is to get a complete understanding and alignment with the stakeholders on how you define success. How do you know that your AI and BI solutions effectively and efficiently solve problems and deliver business value? A clear and well-defined set of business and technical metrics is vital. Identify a limited number of metrics (usually no more than 3 to 5) to be monitored for each solution. Revise and update these metrics if needed, and always focus on whether your AI and BI solutions are solving your problems. How do you know it?

Actually, I agree with the fact that feedback is very important to get the best from the integration that exist between AI and BI.

It is a must to monitor model performance on an ongoing basis. This ensures the model performs as per requirement out-of-sample. Another important practice is to retrain models at a fixed cadence as and when new data flows in. This ensures the model stays updated.

I totally agree with this. In my experience, feedback is the most important process to build into your AI implementations. This is how you get the best of your AI implementation. It's how you gradually align the company's goals and objectives in the transformation journey.

This is a great area for growth after implementation. When you pay attention to what the feedback and satisfaction is from your users and stakeholders you can see areas where you are succeeding that can help you strengthen your brand as you can leverage it to be your differentiator from competitors. In addition, you can see the areas that need improvement which can lead for your project to continuously evolve and become more innovative.

Implementation is where strategy meets execution. A step-by-step, iterative approach is often most effective, allowing for constant refinement and adaptation. Monitoring should not be an afterthought but an integral part of the implementation process. This includes not only monitoring the technical performance but also how the solutions are received by end-users. Regular audits, user feedback, and continuous alignment with evolving business goals can ensure that AI and BI solutions remain relevant and effective.

with AI tools , I'm constantly building on my master prompt and making changes to it , sometimes the answers I get are not how I was looking at it . A word of caution , sometimes I get stuck in the flow and go the deepend , know the limit you want analysis to be a very macro level does get distracting and divert from original thought

Create a structured lessons learned repository and a PowerPoint deck with success stories that you can share externally and internally. This way, you can build a library of AI/ BI solutions that were successfully or not successfully deployed. You can then use your repository to offer quick solutions and ways to approach business problems, dramatically reducing time-to-value.

I totally agreed with the above submission because for AI and BI to integrate properly, solutions provided must address the needs created by these solutions.

AI and BI are not just tools for automation but catalysts for innovation. Encouraging a culture of learning from the insights generated can lead to groundbreaking improvements in workflow efficiency and decision-making. However, the insights must be made accessible and understandable to various stakeholders, often requiring tailored visualizations or summaries. A collaborative approach that includes not just data experts but decision-makers, frontline staff, and even customers can foster an environment where data truly drives innovation.

Well said. I'd also add inclusiveness to the above considerations on ethical principles for responsible AI. Exclusivity is pejorative to responsible AI and prejudices BI

Based on my experience, ethics is one area that is yet properly incorporated into AI for it work in align with businesses for business intelligence to address the needs of companies, so for AI to help provide the BI, efforts must be taking in right direction to incorporate ethics to address ethical issues and challenges.

Ethical considerations in AI and BI are non-negotiable. From data collection to model training to insights application, every step should be scrutinized for potential biases, privacy violations, or other ethical concerns. Establishing a code of ethics specifically related to AI and BI, and continuous monitoring for compliance, can mitigate risks. Transparency with stakeholders about how data is being used and the logic behind AI-driven decisions fosters trust and alignment with societal values.

You should always be open to take in feedback and use the insights to quickly pivot and continue evolving. If you want to be innovative, become aware of the latest trends, what is available, what your competitors have, and use it as inspiration for your own product. Keep the users of your product in focus, analyze their feedback and think of big ideas of what you could create to solve their problems. Can you think of ideas that haven't been created yet? Prioritize them by importance and feasibility and you'll be not only solving more problems but innovating with AI and BI.

The fields of AI and BI are dynamic, with new techniques, tools, and best practices emerging regularly. Encouraging ongoing learning and experimentation can keep your organization at the forefront of these trends. This can mean formal training, attending industry conferences, or fostering a culture of curiosity and experimentation. Collaboration with academic institutions, industry peers, or specialized AI and BI communities can offer fresh perspectives and drive innovation.

BI and AI complement each other. A BI analyst does not need AI skills. However, in my view a great AI expert needs to have BI skills. Understanding business context and competitive advantages is key to the success of AI projects.

Most important pieces: Onboarding the company, Training & enablement plan for the new solutions, create change management plan -new technologies most of the time require new way of thinking and implementing things- and make sure that the solution adopted within the company...

Automating Routine Tasks: AI can be used to automate routine tasks, such as data entry, data cleaning, and report generation. This reduces manual effort and minimizes the risk of errors. As you know data scientist spend more than 80% of the time in data cleaning and wrangling. AI can assist in ensuring efficient workflows and easily diagnosing systematic issues with data prior to the presentation through BI tools such as Looker, Tableau or PowerBI

Integrating AI and BI into a workflow is a complex but rewarding journey. It’s not just about technology but about aligning an organization's culture, structure, and strategy with a new way of thinking and working. Building a cross-disciplinary team that includes not only technical experts but also business strategists, ethicists, and user experience designers can make the journey smoother and more successful. Finally, recognizing that AI and BI are not a one-time effort but an ongoing process of learning, adapting, and innovating ensures sustained value and relevance.