The AI Question Every Business Is Asking
Every senior leader is being asked the same question right now: what are we doing about AI? And most are quietly aware that they do not yet have a clear answer.
In the meantime, AI tools for business are already creeping in. People are pasting spreadsheets into ChatGPT to summarise sales figures. Someone in finance has worked out they can ask Copilot to draft commentary on the monthly numbers. A team is uploading customer data to Claude to spot patterns. It is resourceful, it is getting some results, and it is almost certainly happening without much governance, security oversight, or consistency.
The reason this is happening is simple. AI tools have become genuinely useful, and people want to use them on their actual work. The problem is that this kind of ad-hoc AI is hard to trust, hard to scale, and risky from a data governance perspective. You cannot build a business on it. But you also cannot ignore what it tells you. People are clearly hungry to get more from their data, and AI is the tool they are reaching for.
The good news is there is a better way. And if you have already invested in BI services, you are closer to it than you think.
You Already Have the Foundation
AI is only as good as the data it is working from. Ad-hoc tools struggle because they are starting from raw, ungoverned data: spreadsheets that may or may not be current, definitions that vary between teams, numbers without context. The output looks confident, but the inputs cannot be trusted.
If your organisation has already invested in BI, especially in a properly built Power BI Semantic Model, you have exactly what AI needs to be useful. You have clean, structured, governed data. Core metrics like revenue, utilisation, or wins and losses are defined consistently. Business entities like customer, product, and location are classified the same way across every report. The hard work has already been done.
This is the foundation that turns AI from a parlour trick into a genuine business capability. And it is what makes a data agent the natural next step. It does not happen when the BI genuinely makes their job easier.
Data Agents Are the Practical Entry Point
A data agent is an AI assistant that sits on top of your existing data and BI environment and lets people interact with it in natural language. Microsoft Fabric Data Agent and Databricks Genie are two of the most easily accessible options, and both connect directly to your semantic model and data platform.
What this means in practice is that anyone in your business can ask questions of your data in plain English and get answers drawn from the same trusted source your dashboards are built on. No more pasting figures into Claude. No more waiting for someone in the data team to run a query. No more wondering whether the answer is right. This shift changes the dynamic of AI and BI working together.
The dashboard tells you what is happening. The data agent helps you understand why, and what to do about it.
A typical scenario looks like this. A sales leader is reviewing the weekly dashboard and notices that conversions in one region have dropped. In the old world, they would ask the BI team to dig in and come back next week. With a data agent, they can ask the question directly: “Why are conversions in the South region down this week compared to last quarter?” The agent reaches into the same governed dataset the dashboard is built on, runs the analysis, and returns an answer in seconds, with the figures and trends to back it up.
This is not AI vs BI. It is AI extending it, picking up the questions that dashboards were never designed to answer in advance.
Setup Is Easy. Getting It Right Takes Work.
One thing worth being clear about. Technically, setting up a data agent is a few clicks. The tools are mature and the integration with Fabric or Databricks is straightforward. This is part of why they are such a good entry point.
But a working agent and a useful agent are not the same thing. Out of the box, the agent will give you answers, but they will not always be the right answers, or the right kind of answers, for your business. To make it genuinely useful, it needs shaping. That means training it on the language your business uses, configuring it to handle ambiguous questions sensibly, defining what it should and should not try to answer, and validating its outputs against real business scenarios.
This is where most “we tried AI” stories quietly fall apart. People click the buttons, see some impressive demos, then notice the agent confidently making things up, misinterpreting questions, or pulling figures from the wrong place. The reaction is usually to abandon it. The fix is to invest in the shaping, through AI integration services for businesses. Done properly, you end up with an agent that your teams genuinely trust and rely on.
This Is Not a Throwaway Step
One of the questions we get asked is whether starting with a data agent is just a stepping stone, something you outgrow as your AI ambitions develop. The answer is no.
As your AI capabilities scale, whether that is more sophisticated agents, machine learning models, custom workflows, or a wider AI experience embedded across your business, the work you have done here is not thrown away. The data agents you build now plug directly into that wider AI experience. They become components in a larger ecosystem, not legacy tools to be replaced.
This matters because it means your first move on AI is also your foundation. You are not making a tactical decision that needs unpicking later. You are starting to build the AI layer of your business, in a way that is grounded, governed, and extensible from day one.
Where to Start
If you are looking for the most practical first step on AI for business, the answer is rarely a big AI strategy document or a custom build. It is taking the BI and data platform investment you already have, and putting a properly shaped data agent on top of it.
Done well, this gives your teams an AI capability they can use today, grounded in data they can trust, with no governance risk and no shadow AI to worry about. It scales naturally as your ambitions grow. And it answers the leadership question about AI with something tangible rather than another PowerPoint showing the possibilities.
Frequently Asked Questions
The best AI isn’t a standalone application; it is a data agent (such as Microsoft Fabric Data Agent or Databricks Genie) that sits directly on top of your existing Business Intelligence (BI) environment. By using your governed, structured data rather than raw spreadsheets, the AI delivers statistics that are accurate, consistent, and trustworthy.
AI business intelligence is the integration of artificial intelligence into traditional BI platforms. While standard dashboards tell you what happened, AI BI allows business users to ask plain-English questions to instantly uncover why it happened and what to do next, extending how capable your reporting is.
Integrating AI directly with your data platform ensures that the AI operates on clean, governed, and structured data. This gets rid of “hallucinations,” enforces your existing security and access permissions, and guarantees that the insights generated are based on the same numbers your business already relies on.
