Business intelligence (BI) is going through one of its most interesting shifts in years. For nearly two decades, the operating model has been relatively consistent:
- Data teams developed pipelines and models.
- Developers created dashboards and reports.
- Business users consumed the information to monitor performance and make decisions.
That model is changing, or more precisely, expanding.
AI is introducing new ways to access, question, interpret, and act on the insights that BI teams have spent years developing. Rather than replacing traditional dashboards, AI is extending their value by making business information more accessible and actionable.
I see this as an expansion of BI rather than a replacement of what already works. The real opportunity is to bring AI closer to the trusted data and business context that organizations have spent years establishing.
One Governed Foundation. Multiple Ways to Access Insights.
1. Conversation Is Becoming Another Front Door to BI
Business users can now ask questions in natural language and receive answers, summaries, and visualizations without navigating multiple reports.
Copilot in Power BI, for example, can help draft report pages, generate DAX queries, and summarize what a dashboard is showing.
The dashboard is still there. It remains an important tool for teams tracking performance every week.
What changes is how users investigate the information.
A business owner who notices an unexpected change in a metric no longer needs to raise a ticket and wait for a data team to prepare another report for every follow-up question. They can ask questions, investigate further, and return to the dashboard with additional context.
AI shortens the distance between seeing a number and understanding what it means.
2. Agentic AI and Governed Semantics
The next step beyond answering questions is agentic analytics: AI that can monitor data, identify changes, investigate potential causes, and bring relevant findings to the right person’s attention.
This moves BI beyond responding to user queries toward more proactive identification and investigation of business performance changes.
Gartner’s 2026 Magic Quadrant for Analytics and Business Intelligence Platforms reflects the growing importance of agentic AI and governed semantics across modern analytics platforms.
These capabilities build directly on what established BI environments already do well: maintaining consistent metrics, trusted data, and governed access to business information.
The important shift is not simply that AI can investigate data on its own. It is whether those investigations remain grounded in the same business definitions and governance that organizations already trust for reporting.
Moving from conversational analytics to agentic investigation also requires an enterprise AI strategy that establishes which business questions AI should address, how its findings should be validated, and where human judgment remains necessary.
3. Insights Are Moving Closer to Actions
Insights are no longer confined to the BI portal.
They are increasingly becoming accessible through chat, email, and productivity assistants, where business discussions and decisions already take place.
This extends the reach of BI to people who may not regularly open dashboards but still depend on timely business information.
Instead of requiring every user to navigate to a reporting environment, relevant insights can be brought into the applications they use throughout the day.
The value is not simply easier access to data. It is making trusted information available closer to the point where decisions are discussed and actions are taken.
This is also where AI-augmented decision intelligence becomes relevant, connecting governed business information with the operational context in which decisions are made.
4. The Semantic Layer Takes Center Stage
AI works best with enterprise data when it can rely on governed business definitions.
Consider a business user asking an AI assistant:
“What was our net collection rate last quarter?”
The quality of the answer depends on whether the assistant understands how the organization defines net collection rate, which adjustments are included, how the reporting period is determined, and who is authorized to access the information.
This knowledge already exists in well-structured BI environments.
Measures, relationships, hierarchies, and security rules are elements that BI teams have refined over years to make reporting consistent and reliable.
The semantic model provides a shared foundation that dashboards, analysts, AI assistants, and agents can use to interpret business information consistently.
One Governed Semantic Model. Four Connected Experiences.
SEMANTIC MODEL Shared business definitions, measures, relationships and security
Without that foundation, different AI interactions may produce answers that appear reasonable but do not follow the same business definitions.
Maintaining those definitions across systems also depends on enterprise data governance and strategy, particularly how critical metrics are defined, owned, accessed, and maintained as analytics environments evolve.
Gartner predicts that 60% of agentic analytics projects relying solely on Model Context Protocol (MCP) will fail without a consistent semantic layer, highlighting the importance of governed business definitions for reliable AI-driven analytics.
I believe this is where years of BI engineering work become even more valuable. The measures, relationships, and governance that once supported consistent reporting now have a much broader role in helping AI interpret enterprise data reliably.
As AI expands how people interact with enterprise data, governed semantics become more important, not less.
Where Power BI Fits: From Dashboards to AI’s Source of Truth
Microsoft is extending how Power BI reports and semantic models can be accessed through AI-powered experiences.
Microsoft 365 Copilot can answer questions using information from supported Power BI reports and semantic models while respecting applicable permissions.
For example, a sales leader could ask a question through a supported Copilot experience and receive an answer grounded in the same governed semantic model used by the finance team.
At FabCon Barcelona, Microsoft announced the general availability of Fabric IQ in Copilot Chat, alongside Power BI agentic experiences and a “Restrict from Copilot” control that allows administrators to manage which content can be used by Copilot.
Power BI MCP servers are also in preview.
Enables AI agents to query Power BI semantic models through the DAX engine while operating within the user’s permissions.
Supports developers in developing and refining semantic models through natural language within their existing development and review workflows.
Taken together, these developments point toward a broader role for Power BI semantic models.
They can support more than dashboards and reports. They can provide a governed data foundation for Copilot, custom AI agents, and other enterprise AI use cases.
For organizations with established Power BI environments, this creates an opportunity to extend existing BI investments into AI-enabled analytics while continuing to rely on familiar business definitions and governance controls.
What This Looks Like in Practice
We see the potential of this approach in healthcare operations.
Consider a hospital operations leader reviewing a capacity dashboard and noticing that emergency department (ED) wait times are increasing at one facility.
Traditionally, understanding the cause might require reviewing several reports or asking an analytics team to investigate.
With AI connected to a governed semantic model, the leader could ask whether the increase is associated with admissions, discharge delays, or staffing gaps.
They could investigate which departments or units are affected and share the findings with clinical leaders for further review and appropriate action.
From Dashboard Signal to Operational Insight
How AI can help hospital leaders investigate rising emergency department wait times
Dashboard Signal
A hospital operations leader notices increasing emergency department (ED) wait times at one facility.
→Ask a Question
The leader asks AI whether the increase is associated with admissions, discharge delays, or staffing gaps.
→Investigate Drivers
Explore the operational factors behind the increase:
Share for Review
Share findings with clinical leaders so they can validate the information and determine appropriate operational action.
The dashboard surfaces the signal. AI helps investigate and explain it.
Both depend on consistent definitions of bed status, length of stay, department hierarchies, and other operational measures.
When these definitions are governed and consistently applied, AI can help users investigate operational questions without introducing a separate interpretation of the underlying data.
The important distinction is that AI supports investigation and decision-making. It does not eliminate the need for operational judgment or human validation.
What BI Teams Should Prioritize as They Introduce AI
For organizations bringing AI into their BI environments, I would put the emphasis on strengthening what makes their existing analytics reliable before expanding AI access.
A few fundamentals deserve attention:
- Strengthen the existing foundations. Establish clear naming conventions, documented measures, and consistent business definitions so AI can interpret data within the correct context.
- Incorporate security from the beginning. Include access controls, permissions, and sensitivity labels as part of the AI rollout rather than addressing them afterward.
- Validate and certify AI-ready content. Start with trusted, validated semantic models and reports before expanding AI access to additional information.
- Keep humans in the loop. Maintain appropriate human review and approval before AI-generated findings lead to decisions or actions with significant business impact.
- Expand the role of BI teams. Alongside their existing development responsibilities, BI professionals will increasingly contribute as semantic model owners, governance stakeholders, and AI enablers.
These priorities reinforce an important point: introducing AI into BI is not only about enabling new interfaces or deploying additional tools. It also requires organizations to maintain the quality, consistency, and governance of the information those tools depend on.
A data strategy roadmap can help organizations sequence those priorities, particularly when existing data quality, semantic modeling, and governance gaps need attention before broader AI adoption.
The Bottom Line
BI gives organizations trusted data. AI makes that data easier to access, investigate, interpret, and act on.
Dashboards will continue to play an important role in monitoring business performance. Conversational AI and agentic analytics extend what users can do with the information those dashboards represent.
But the value of these capabilities still depends on the semantic models, business definitions, and governance practices that make BI reliable in the first place.
My view is that organizations will get the most from AI and BI when they stop treating them as two separate initiatives. BI provides the trusted foundation, while AI extends how people access, investigate, and act on that information. Bringing both together is where I see the real opportunity for enterprise analytics.
Build on trusted data. Extend what your teams can do with AI.
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