These days, many companies no longer want to just view their data in dashboards. They want to ask questions. What is the true state of our pipeline? Which projects are running over budget? Which customers have upsell potential? Which teams are overloaded?
With Microsoft Fabric Data Agents and Copilot, this way of working becomes a more realistic possibility. Business users can interact with data sources using natural language, rather than having to click their way through reports, tables, and model structures. Microsoft describes Fabric Data Agents as a generally available feature that enables organizations to build conversational Q&A systems based on data in OneLake, lakehouses, data warehouses, Power BI Semantic Models, and KQL databases.
That sounds like a major step forward. And it is. But only on one condition: The data must not only be accessible; it must also be understood from a business perspective.
Why Dashboards Are No Longer Enough
Dashboards usually answer questions that someone has defined in advance. Data agents change this logic. They are designed to answer spontaneous questions, explain relationships, and provide decision support.
This is particularly interesting for project-based companies, professional services firms, healthcare organizations, and growing B2B companies. That’s exactly where critical information is often scattered: CRM data, project data, financial data, service histories, time entries, quotes, forecasts, documents, and emails.
A dashboard can indicate that a project is in the red. A data agent should explain why it is in the red, which cost items are affected, which resources are missing, and what customer communications were relevant to this situation.
For that, AI needs more than just access to tables. It needs meaning.
The real problem: Every department uses a different data language
Many companies don't have a problem with the sheer volume of data. They have a problem with its meaning.
Sales talks about the pipeline. Finance talks about the forecast. Operations talks about capacity utilization. Project management talks about remaining work. Management talks about margins. Everyone seems to use familiar terms. But often, these terms aren’t defined consistently.
When is a project considered out of budget? Do planned, booked, or billed hours count? What is a realistic pipeline? Based on opportunity stage, likelihood of closing, or commit forecast? What does customer value mean? Revenue, contribution margin, contract duration, or strategic potential?
As long as these terms are used differently depending on the department, a data agent may be able to provide an answer. However, that answer is not automatically reliable.
| Business Question | What Needs to Be Clarified First |
|---|---|
| Which projects are funded by the budget? | Budget logic, recorded hours, remaining work, forecast, and cost elements |
| Which customers have upsell potential? | CRM data, project status, service history, revenue, and customer segment |
| Which pipeline is realistic? | Sales Stage, Probability, Closing Date, and Forecast Category |
| Which teams are overloaded? | Capacity, Absences, Project Assignment, and Planned Utilization |
| Which processes generate the most costs? | Process data, financial data, time required, and responsibilities |
Without a common data language, there can be no true transparency. All that results in are faster responses based on vague definitions.
Why Semantics Is Becoming Strategic Now
In 2026, Gartner strongly warns against neglecting semantics. According to Gartner, a lack of semantics causes AI agents to become inaccurate and inefficient, waste money, and create additional risks for data and AI governance. Gartner also cites semantics as a key trend in data and analytics because AI agents require reliable meaning, context, and rules.
That's the key point: data agents need more than just access to data. They need a semantic understanding of what metrics mean, how terms are related, and what rules apply.
A semantic model is therefore no longer just a technical issue for BI teams. It becomes the foundation for whether AI can provide reliable answers within the company.
What a Common Data Language Actually Means
Having a common data language does not mean that each department loses its own perspective. It means that key terms, metrics, and relationships are clearly defined.
When Sales, Finance, and Management discuss revenue, it must be clear whether they are referring to recognized revenue, billed revenue, ARR, new business, or forecasts. When project teams discuss capacity utilization, it must be clear whether they are referring to planned capacity, billable time, actual bookings, or availability.
To do this, companies usually need five key elements:
| Basis | Purpose |
| List of Key Metrics | Key definitions for revenue, margin, pipeline, capacity utilization, and project status |
| Data Responsibility | Clear ownership of data quality, definitions, and changes |
| Semantic Models | Consistent logic for reports, agents, and analyses |
| Authorization Policy | Secure use based on role, area, and data class |
| Governance and Monitoring | Verify that responses remain transparent and trustworthy |
These fundamentals may sound less exciting than a new Copilot. But they’ll determine whether Copilot will actually be useful in a business setting.
Microsoft Fabric Data Agents: A New Way to Access Enterprise Data
Microsoft Fabric Data Agents make the next step visible. Data should not only be consumed in reports but also be accessible via natural language. Microsoft describes Data Agents as a conversational analytics component that can be connected to governed data in OneLake, utilizing, among other things, lakehouses, data warehouses, semantic models, and KQL databases. Purview governance policies and permissions also remain in effect when Data Agents are used via Microsoft 365 Copilot.
This is relevant for companies because data work is becoming more integrated into day-to-day operations. A manager doesn’t need to know which report contains the right metric. A project manager doesn’t need to be familiar with every data source. A leader can ask questions and get guidance more quickly.
But that is precisely why the responsibility increases. As data agents become more widely used, data models, definitions, and permissions must be more precise than before. After all, incorrect or inconsistent responses will then also scale more quickly.
Why this is especially important for complex organizations
In simple organizations, data logic is often straightforward. In more complex companies, it is not.
Project-based companies must integrate project status, capacity utilization, budgets, forecasts, and margins. Healthcare organizations work with sensitive data, complex workflows, and stringent governance requirements. B2B companies with scalable processes must integrate CRM, marketing, service, finance, and operations.
In such environments, a single dashboard is rarely enough. The real challenge is making data actionable across functional boundaries.
A data agent can be very useful here. It can help executives identify patterns more quickly. It can reduce the workload on operational teams by minimizing the need for manual analysis. It can democratize data access without giving every user direct access to every table.
But the data agent isn't the starting point. It's the result of a well-prepared database.
How Companies Should Get Started
A good starting point doesn't begin with the question of which AI feature can be enabled. It begins with the most important business questions.
Which decisions are taking too long these days? Where does manual reporting occur? Which metrics are regularly discussed because no one is sure which number is correct? What data does management really need to better manage projects, the pipeline, finances, or customers?
According to this, companies should not connect all data sources right away. It is better to start with a clear use case that offers high business value.
| Step | Goal |
| Select a business issue | for example, project profitability, pipeline quality, or customer segmentation |
| Defining Terms | Establish a Common Logic for Metrics and States |
| Check Data Sources | Identify relevant systems, data quality, and gaps |
| Build a semantic model | Make a uniform business logic available for analysis and agents |
| Clarify Permissions | ensure that users see only relevant data |
| Test the agent | Validate and improve answers with subject matter experts |
| Scale | Add additional data fields and questions step by step |
The result is not a technical experiment, but a data-driven approach with clear benefits.
Conclusion: AI needs more than just access to data
Data agents in the workplace are an important step. They make data more accessible, more intuitive, and more integrated into everyday work. But they do not automatically solve the underlying problem faced by many organizations.
If terms such as revenue, margin, capacity utilization, project status, or customer value are interpreted differently, even the best agent cannot provide a reliable basis for decision-making. It merely highlights the lack of a common data language.
The next competitive advantage, therefore, lies not only in implementing Copilot or Fabric Data Agents. It lies in preparing data in such a way that AI truly understands the business.
Those who establish a common data language gain more than just better reports. They lay the foundation for reliable data agents, faster decision-making, and productive AI within the company.
FAQ
What are data agents in a company?
Data agents are AI-powered systems that allow users to ask questions in natural language about corporate data. They can draw on data sources such as lakehouses, data warehouses, semantic models, or analytical models to provide answers and insights.
Why isn't data access enough for AI?
Data access simply means that a system can read data. To provide reliable answers, AI also needs context, definitions, rules, permissions, and a shared understanding of key business terms.
What does "common data language" mean?
A common data language defines standardized definitions for metrics and terms such as revenue, margin, pipeline, project status, capacity utilization, and customer value. It ensures that departments do not use the same terms with different meanings.
Why is semantics important for AI agents?
Semantics gives data meaning. It helps AI agents understand how metrics, terms, and business logic are related. Gartner warns that a lack of semantics can make AI agents inaccurate and inefficient.
What role does Microsoft Fabric play?
Microsoft Fabric can consolidate data from various sources and make it available for conversational use through Data Agents. Fabric Data Agents can work with OneLake, lakehouses, data warehouses, Power BI Semantic Models, and KQL databases, among other things.
How do companies get started with data agents?
The best way to get started is with a specific business question that offers significant value. Next, terms, data sources, permissions, and semantic models should be clarified. Only then should a data agent be tested in a production environment and expanded.
