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.

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.

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.

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.