6 Things You Should Know About AI in Microsoft Fabric

Artificial intelligence is transforming how organisations work with data, but adopting AI tools without a clear plan often leads to limited results. Many focus on the technology rather than the value it should deliver. That's why Microsoft promotes a structured, business-led approach through its AI adoption framework, ensuring AI investments support real business goals, follow responsible practices, and are grounded in good governance.

Microsoft Fabric plays an important role in this journey. It's a unified data platform for data engineering, data science, analytics, and business intelligence. With built-in AI capabilities like Copilot in Power BI, Data Factory, and Notebooks, Fabric helps users quickly move from data to insight, even if they're not technical experts.

But using AI effectively in Fabric isn't just about switching on new features. It requires a clear strategy that aligns with business priorities, and the right support structure in place.

In this article, we'll explore six essential things to know about AI in Microsoft Fabric. From getting started with the right framework to the latest integrations with Copilot Studio and Fabric Data Agents, let's explore what you need to know.

1. Align AI Initiatives with the Microsoft AI Adoption Framework

Before diving into any AI features within Microsoft Fabric, it's important to take a step back and ask a simple question: What business problem are we trying to solve? This may sound obvious, but many organisations jump into AI projects without a clear purpose.

That's where the Microsoft AI adoption framework comes in. It's a structured approach that helps you define your goals, understand your readiness, and build a plan for delivering value through AI. It encourages you to start with a business-led strategy, not a technology-led one.

The framework covers several key areas:

• AI strategy: Identify high-impact business use cases and define a clear AI and data strategy from the start.

• AI plan: Assess your team's AI skills, prioritise use cases, and outline a realistic plan with a clear proof of concept.

• AI ready: Build the right environment with a suitable architecture, strong data foundations, and early governance in place.

• Govern AI: Put governance policies in writing and regularly monitor risks across your AI projects.

• Manage AI: Maintain control by managing models, costs, deployments, and operations across teams and departments.

• Secure AI: Protect sensitive data, detect threats early, and ensure compliance with your organisation's security standards.

By starting with a strategic framework, you'll be better positioned to use AI features in Fabric effectively and deliver results that matter. Remember: AI success isn't just about what you build, it's about why you build it.

2. Microsoft Fabric: The Unified Foundation for AI Innovation

Before you can make the most of AI, you need a strong data foundation–and that's exactly what Microsoft Fabric provides. It's a unified platform that brings together data engineering, data science, analytics, and business intelligence into a single, integrated experience.

Instead of juggling multiple tools and data silos, Fabric allows organisations to work from one trusted environment. All workloads, whether they involve pipelines, notebooks, dashboards or machine learning, connect to a centralised data store called OneLake. This means your data is always accessible, governed, and ready to power AI.

Fabric also integrates with Microsoft 365 and Power Platform, making it easier to embed AI directly into everyday business processes. Fabric supports the entire analytics lifecycle from raw data to insights to action. Some key benefits include:

• A single source of truth for all analytics and AI workloads
• Native support for AI services and Copilot experiences
• Built-in governance, compliance, and security tools

3. Generative AI at Work: Copilot in Power BI, Data Factory, and Notebooks

Generative AI is no longer just a concept–it's already improving how people work with data in Microsoft Fabric. Thanks to Microsoft Copilot, AI can now assist with common tasks across Power BI, Data Factory, and Notebooks, helping users move faster from data to decisions.

In Power BI, Copilot makes reporting more intuitive. Users can ask questions about their data in natural language and get visual responses–no advanced knowledge of DAX or data modelling required. It can also help build measures, create summaries, and suggest insights, making self-service BI even more accessible.

In Data Factory, Copilot supports data engineers by suggesting pipeline logic, transformations, and steps based on the data being processed. It reduces manual effort and speeds up data preparation, which is often one of the most time-consuming parts of any analytics project.

In Notebooks, Copilot acts as a coding assistant for data scientists. It can generate Python code, explain existing code, and help troubleshoot errors all within the notebook environment.

Together, these capabilities demonstrate how Generative AI in Fabric helps make complex tasks simpler, improving productivity, and helping teams focus on delivering insights rather than wrestling with tools–and this is just the beginning. Explore the Copilot experiences in Microsoft Fabric to see everything they have to offer.

4. Operationalise AI with AI Services in Microsoft Fabric

Once you've built a strong data foundation, the next step is turning that data into intelligence. Microsoft Fabric makes this possible with a growing set of built-in AI services that help you move from experimentation to real-world application, all within a unified platform.

These services include pre-trained models for tasks like text analytics, language detection, sentiment analysis, and image classification. These can be used straight out of the box with little to no coding, making AI more accessible to teams across the organisation.

For those with more advanced needs, Fabric integrates seamlessly with Azure AI Foundry, enabling teams to build and coordinate powerful AI solutions. This integration makes it simple to operationalise custom models and organise complex AI workflows, all while staying connected to your data within Fabric.

A key advantage of using AI within Fabric is the direct access to OneLake. This means you can serve models directly against governed, real-time and batch data, removing the need for complex data movement or duplication. What's more, AI services in Fabric benefit from the platform's built-in security, compliance, and governance features, helping you meet regulatory and organisational requirements from day one.

And now, with the introduction of AI functions for data engineering, users can easily apply powerful Generative AI capabilities, like summarisation, sentiment analysis, classification, and translation, using a single line of code. These functions seamlessly integrate into workflows with tools like Pandas or Spark, making it easier than ever to enrich and transform data at scale.

5. Fabric Data Agents and Copilot Studio: Custom AI Agents, Smarter Conversations

One of the most exciting recent developments in Microsoft Fabric is the integration between Fabric Data Agents and Copilot Studio, an update that brings conversational AI closer to your data than ever before.

As announced in this Microsoft Fabric blog post, you will soon be able to enrich custom copilots built in Copilot Studio with live insights from your Fabric data. This means business users can interact with their data using natural language and receive contextually relevant responses powered by real-time and batch data models.

At the heart of this integration are Fabric Data Agents. These agents act as connectors between Fabric and the conversational experiences you build in Copilot Studio. They allow your AI assistants to safely and securely access structured data sources like semantic models, Power BI datasets, and Lakehouses stored in OneLake.

This unlocks a new level of productivity where users get answers faster without needing to navigate complex dashboards or build reports. And because these agents respect your organisation's data permissions and governance, they support responsible, scalable use of conversational AI.

6. Responsible AI and Governance: Ensuring Trust at Scale

As AI becomes more embedded in day-to-day decision-making, so too does the need for trust, transparency, and accountability. Microsoft Fabric has been designed with these principles in mind, offering built-in tools to support responsible AI adoption at scale.

At the core of this is Fabric's strong data governance and security model. Every dataset, report, and AI model in Fabric is tied to a central lineage and permissions system, helping organisations control access, maintain compliance, and understand how data is being used. This is especially important as AI capabilities, like Copilot and automated insights, draw directly from your data estate.

Fabric also aligns with Microsoft's broader Responsible AI principles, covering areas such as fairness, privacy, reliability, and inclusiveness. These principles are not just theoretical–they're supported by practical tools and frameworks across the platform. You can monitor how models are performing, detect potential drift or bias, and apply safeguards to prevent misuse and ensure ethical and reliable results.

In addition, Microsoft's Cloud Adoption Framework encourages you to build governance and security into your AI adoption plan from the start. Fabric supports this approach by making it easier to manage data access, monitor usage, and enforce policies at scale.

From Strategy to Insights: Making AI in Microsoft Fabric Work for You

AI is no longer reserved for technical teams or experimental projects. With Microsoft Fabric, it becomes a practical tool that can enhance productivity, speed up decision-making, and unlock new business value when it's implemented with purpose.

As we've explored, success with AI in Fabric doesn't start with the tools. It starts with a clear, business-led strategy, shaped by frameworks like the Microsoft AI adoption framework. By defining your goals, understanding your data landscape, and building governance into your approach, you can lay the foundations for impactful, responsible AI.

The next step? Explore these tools in your own environment. Start small with a proof of concept tied to a clear outcome, and scale from there.