# LLM + BI = GenBI? *What do Large Language Models mean for Business Intelligence* By [The Intelligent Enterprise](https://paragraph.com/@intelligententerprise) ยท 2025-09-22 #ai, #businessintelligence, #genbi, #fabric, #llm --- I wasn't planning on talking about the intersection of AI (LLMs) and Business Intelligence now but how can one avoid talking about anything but. Let's take a little detour. Even if we are talking about LLMs, allow me to simply say "AI" throughout this post. AI has its place in the world of BI. I believe there is value in embedding AI capabilities into your BI stack - whether it's an aide to engineers as they develop or an agent that the business can interact with. That is the spectrum as I see it today - the generation of code based on solutions led by engineers and engineering teams and the front-end agents or data agents that serve as the go-between the business and its data. I'm sure a good portion of you reading this have used a few of the models for generating code across various languages. Who can deny that boost in productivity and creativeness (if you ask for creative solutions). That's a gain that, to me, has already been realized across organizations leveraging AI whether they know it or not. As such, it's vital that AI use is understood and spoken about. Building said understanding allows the building of appropriate AI governance which organizations will need to drive innovation and value from AI. When it comes to the front-end "AI agents", I have been pleasantly surprised as what they can do. In testing, we created a table and PowerBI semantic model that contains a massive amount of KPIs and metrics. Releasing an agent on top of that model yielded poor results until a few hundred lines explaining the relationship of the data, anticipating user questions, and translating regular written English to column names provided enough guidance for the agent to start answering questions with increased accuracy. To the point where we were impressed with how well it was responding. Armed with that wealth of information, the agent was not only able to answer questions that a user might usually look at a report or dashboard for, it was also able to generate appropriate graphs and visuals that further helped the user understand the data. And this point right here is where I believe AI has its place in Business Intelligence. Where we get to "GenBI". I imagine a future maturity state of Business Intelligence across an organization that does _not_ involve the use of common BI tools such as Power BI or Looker. In this maturity state, the top level of the stack is simply an agent embedded within the warehouse or lakehouse, provided with guidance, and business users interact solely with the agent in the answering of questions, the generation of reports and dashboards. BI teams are no longer developing reports and dashboards for the user, they are building data models and injecting the model with relational data context in order to give the users the ability to generate their answers and visuals for storytelling. This is the foundational shift in Business Intelligence via AI. I no longer need to grow a BI team to model data and build reports for users. This, as we know, is time-intensive approach. Queue all of the stories we've heard about gathering requirements from users and doing UAT... With a focus on AI, I can have my team ensure the agent has the appropriate guardrails and then letting users run with it. Speed to delivery can be quasi-eliminated as no one needs to wait for the BI team to build a report. Any information can be available as soon as it has been modeled and the agent been given the context. Now I realize I might be getting ahead of myself. No matter how excited I am about piloting an agent-first BI stack - there's plenty of things to watch out for as we move towards GenBI. Governance will be key (again). Organizations will need to spend time logging and documenting their data and the relationship of data across the whole enterprise. Users will need to be trained in interacting with agents. AI teams will most likely need to be stood up in support of managing various agents. I could go on and on but I'm not writing about AI Governance. Not yet anyways. However AI Governance will supersede the need for BI Governance as the former takes over the latter in its importance of giving organizations the insights they need into their operations. Even if we are at the beginning stages of AI in BI, there already is plenty of value to extract from what's been made available. Whether that's through something like Microsoft Fabric data agents or a fully open-sourced solution, the landscape is changing and BI leaders must be ready to incorporate AI into their stack. --- *Originally published on [The Intelligent Enterprise](https://paragraph.com/@intelligententerprise/llm-bi-=-genbi)*