---
title: "What Context Does Conversational Analytics on Power BI Depend On?"
url: "https://atlan.com/know/ai-agent/microsoft/power-bi-conversational-analytics-context/"
description: "See the verified F2+ capacity floor, the RLS-as-accuracy argument, the Q&A-to-Copilot dependency, and why one certified Power BI model isn't the whole estate."
author: "Emily Winks"
author_role: "Data Governance Expert"
published: "2026-10-07"
updated: "2026-10-07"
---

> Atlan is hosting Context Conference, bringing together the leaders and builders at the frontier of giving AI the context it needs to understand their business. It runs online on October 28, 2026, from 11:00 AM to 2:00 PM ET. Atlan co-founder Prukalpa Sankar opens and closes the day. Leaders from AstraZeneca, BNY and Verizon share why they invest in context and what they get from it. Registrants get early access to The AI Context Gap, a new study from MIT Technology Review Insights. Register: https://atlan.com/context-conference/

---

Conversational analytics on Power BI runs through Copilot, which needs Fabric capacity F2 or Power BI Premium P1 or higher; trial capacities don't qualify. Copilot inherits your existing row-level security, and Microsoft's Approved for Copilot certification, plus a 200-character description limit, get one semantic model ready, which is where Atlan, as a context layer, picks up rather than competes. When the same metric is also defined in a Snowflake view or a Databricks model, Copilot's certified answer in Power BI is only half the picture; keeping that metric consistent everywhere it lives, without replacing Microsoft's own readiness tooling, is the job Atlan does for a Microsoft-first estate.

### Build Your Semantic Layer Shortlist

Give it the semantic layer tools you're considering and everything that has to read a metric. It returns an evaluation matrix scored on consumer coverage, bypass resistance, and change control. [Read the skill](/skills/semantic-layer-tool-shortlist.md).

*Paste into a new chat*

```
Use the skill at https://atlan.com/skills/semantic-layer-tool-shortlist.md to evaluate semantic layer tools for this estate. Ask me for whatever it needs.
```

*Run once in a terminal*

```
curl -fsSL --create-dirs \
  -o ~/.agents/skills/semantic-layer-tool-shortlist/SKILL.md \
  https://atlan.com/skills/semantic-layer-tool-shortlist.md
```

*For an agent*

```
curl -fsSL https://atlan.com/skills/semantic-layer-tool-shortlist.md
```


    Human
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As of October 2026, those facts are current, not folklore: the capacity minimum corrects a stale "F64 or higher" claim still circulating in community threads, and the Q&A dependency below is a nuance most competing guides skip. A certified single model, meanwhile, doesn't guarantee a consistent answer once the same business concept exists somewhere else in the estate.

| Fact | Detail |
|---|---|
| What it is | Conversational, natural-language analysis of Power BI data, delivered today primarily through Copilot |
| Capacity required | Paid Fabric capacity F2 or higher, or Power BI Premium P1 or higher; trial and free SKUs don't qualify |
| Key constraint | Descriptions on measures, tables, columns, and calculation groups are cut at 200 characters for Copilot's use |
| Certification flag | Approved for Copilot marks one semantic model as ready; there is no report- or dashboard-level equivalent |
| Retirement timeline | Power BI Q&A retires February 2027; Copilot's current data-question answering still depends on it being enabled |

---

## What counts as conversational analytics on Power BI?

Conversational analytics on Power BI means asking a business question in plain language and getting an answer grounded in a [Power BI semantic model](https://atlan.com/know/ai-agent/semantic-layer/power-bi-semantic-model/), most commonly through Copilot. Power BI Q&A, the older natural-language feature, is being retired, covered in full below. For the broader feature tour, see [Microsoft's own introduction](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-introduction); what follows starts further in, at what has to be true about your data first.

The same question shows up across every BI platform, not just Power BI. See [conversational analytics](https://atlan.com/know/conversational-analytics/) for the industry-wide version, and [conversational search for enterprise data](https://atlan.com/know/conversational-search-enterprise-data/) for the adjacent question of searching data rather than analyzing it. What's specific to Power BI is the dependency chain underneath: a [semantic layer](https://atlan.com/know/semantic-layer/) defining the business terms Copilot grounds itself in, the difference between that [semantic layer and a context layer](https://atlan.com/know/context-layer-vs-semantic-layer/) above it, a licensed Fabric or Premium capacity, and, for now, Power BI Q&A switched on.

Conversational analytics on Power BI is also a different product family from [a Fabric data agent](https://atlan.com/know/microsoft-fabric/what-are-fabric-data-agents/), which can span multiple data sources rather than one semantic model, and from [Copilot Studio agents](https://atlan.com/know/ai-agent/ai-agent-applications/what-are-microsoft-copilot-studio-agents/), a separate conversational-app product for different workflows.

---

## What capacity do you need before Copilot works in Power BI?

Copilot in Power BI requires paid Fabric capacity F2 or higher, or Power BI Premium P1 or higher; trial and free SKUs don't qualify. This corrects a claim still circulating on Reddit that Copilot needs F64 or higher. According to [Microsoft's Copilot for Power BI overview](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-introduction) (Microsoft Learn, 2026), that dropped to F2+ in April 2025; F64 hasn't been accurate in over a year.

Not every [Microsoft Fabric Copilot](https://atlan.com/microsoft-fabric-copilot/) surface is GA yet. The report-pane experience, the one most users touch first, is; standalone cross-item Copilot, Copilot in apps, Copilot in web modeling, and Copilot for Data Factory, Data Engineering, Data Science, Data Warehouse, and Real-Time Intelligence all sit in preview. Enabling any of this is a tenant-level decision before it's a capacity one; [Microsoft's guide to enabling Fabric Copilot for Power BI](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-enable-power-bi) covers the admin settings.

Capacity and tenant settings are the floor, not the readiness bar. A tenant already running [translytical task flows](https://atlan.com/know/microsoft-fabric/translytical-task-flows/) on the same Fabric capacity should confirm that capacity also clears Copilot's F2+ floor, since both draw from the same allocation. Even then, Copilot can still return wrong or inconsistent answers if the underlying semantic model isn't ready, which the sections below address.

---

## Does Power BI Copilot respect your row-level and object-level security?

Copilot inherits whatever Power BI permissions a user already has: workspace roles, item permissions, row-level security, and sensitivity labels.

According to [Microsoft's documentation on Power BI Q&A limitations](https://learn.microsoft.com/en-us/power-bi/natural-language/q-and-a-limitations) (Microsoft Learn, 2026), row-level security (RLS) is respected across both the report-pane and standalone experiences, so a user only receives data they're already authorized to see. Object-level security (OLS) is supported for models hosted in the Power BI service, but not live-connect to Azure Analysis Services or on-premises SQL Server Analysis Services tabular models.

The argument most readiness content misses: incorrect RLS isn't only a compliance checkbox, it's an accuracy failure mode. A wrong-but-authorized answer looks exactly like a right one, with no visible signal the scope was wrong. Compliance teams audit RLS for exposure risk; almost nobody audits it for silently producing confidently wrong numbers.

A [role-based access control](https://atlan.com/know/ai-agent/context-layer/context-layer-role-based-access-control/) model built for an AI context platform treats this as an accuracy problem, not just a compliance one, because the two failure modes look identical from the outside. The same reasoning extends to [governing Fabric AI agents](https://atlan.com/know/microsoft-fabric/fabric-ai-agent-governance-questions/) more broadly, and to the open question of [who governs a Fabric item an agent created](https://atlan.com/know/microsoft-fabric/who-governs-agent-created-fabric-items/), rather than a person. [Purview's Unified Catalog](https://atlan.com/know/ai-agent/microsoft/purview-unified-catalog/) is the native enforcement layer sensitivity labels flow through before reaching Copilot's grounding context. The same RLS-as-accuracy argument holds a level up, too: [Power BI data governance](https://atlan.com/power-bi-data-governance/) and [Microsoft Fabric governance](https://atlan.com/know/ai-agent/microsoft/microsoft-fabric-governance/) make the identical case for the wider estate, not just inside one Copilot session.

---

## What "Approved for Copilot" certifies, and where it stops

Approved for Copilot, renamed from "prepped for AI," is Microsoft's model-owner certification flag, point-in-time, not a continuous guarantee.

According to [Microsoft's guide to preparing data for AI](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-prepare-data-ai) (Microsoft Learn, 2026), once a model owner enables it, standalone Copilot removes friction treatment from answers grounded in that model, typically within an hour, up to 24 hours for models with many dependent reports. There's no equivalent certification for a report, dashboard, or app, only for the model itself.

One constraint catches most model owners off guard: descriptions on measures, tables, columns, and calculation groups are cut at 200 characters for Copilot's use, according to [Microsoft's guide to optimizing semantic models for Copilot](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-evaluate-data) (Microsoft Learn, 2026). A carefully written 400-character description reads fine to a human and gets silently truncated for Copilot.

The gap competing readiness content doesn't cover: Approved for Copilot has no mandated review cadence, and nothing in Microsoft's own tooling flags it when the underlying source-table schema drifts after certification, so a model can stay approved long after the ground truth it was certified against has moved. Metadata-completeness signals surfaced continuously, rather than checked once at certification time, are what close that gap.

---

## Is Power BI Q&A really going away, and does Copilot still need it?

Power BI Q&A is being retired in February 2027, with Microsoft directing users to Copilot as the forward path. The nuance most competing guides skip: Copilot's data-question answering still requires Q&A enabled on the semantic model's dataset settings.

According to [Microsoft's documentation on Power BI Q&A limitations](https://learn.microsoft.com/en-us/power-bi/natural-language/q-and-a-limitations) (Microsoft Learn, 2026), that's likely to change before the 2027 retirement, but it's true today, corroborated by [Microsoft's privacy and security documentation for Copilot in Power BI](https://learn.microsoft.com/en-us/fabric/fundamentals/copilot-power-bi-privacy-security) (Microsoft Learn, 2026). Switching Q&A off on a model otherwise prepared for Copilot can quietly break the feature it was enabled for.

Microsoft is already building toward that change: a preview toggle for [Fabric IQ](https://atlan.com/know/microsoft-fabric/what-is-fabric-iq/) inside the Copilot pane itself answers data questions through multistep reasoning instead of Q&A's single pass, according to [Microsoft's guide to asking data questions with Copilot and Fabric IQ](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-ask-data-question) (Microsoft Learn, 2026). That's the direction the dependency above is moving, not a promise it already has.

| Dimension | Power BI Q&A | Power BI Copilot |
|---|---|---|
| Natural-language scope | Single dataset, keyword-style questions | Full report and model context, conversational follow-up |
| RLS/OLS behavior | Inherits RLS; OLS unsupported for live-connect models | Inherits RLS; OLS unsupported for live-connect models |
| Retirement status | Retiring February 2027 | Active; Microsoft's forward path |
| Current dependency | None; self-contained | Still depends on Q&A being enabled on the dataset, per current docs |

For the related distinction between Copilot and a [Fabric data agent](https://atlan.com/know/microsoft-fabric/fabric-database-agent-vs-data-agent/), see that comparison directly; the two are built for different scopes of question.

---

## The data-readiness checklist your Power BI semantic model needs before Copilot works

Every independent source that covers this, Microsoft's own documentation, the implementer-blog cluster, and Reddit and Fabric Community threads, converges on the same diagnosis: semantic-model structure, not the AI, is what breaks Copilot answers. The six items below distill that diagnosis into a single checklist.

[Adastra](https://adastracorp.com/insights/copilot-in-power-bi-wont-fix-bad-data/), [Bismart](https://blog.bismart.com/en/power-bi-copilot-errors-wrong-answers), MAQ Software, [Collab365](https://spaces.collab365.com/posts/ai-answers-are-only-as-good-as-the-power-bi-semant-Sl8vh1), RSM, and TTMS reach the same diagnosis independently: poorly structured semantic models, duplicate or ambiguous metrics, unclear field names, missing relationships, and misconfigured RLS cause most "Copilot gave a wrong answer" complaints, not the AI. At [SQLBits 2026](https://sqlbits.com/sessions/event2026/Prepare_Your_Power_BI_Semantic_Model_for_AI-Ready_Analytics), Yulia Kraynova's session put the same diagnosis in front of a live practitioner audience, not just a blog comment section.

| Prerequisite | Why Copilot needs it | What goes wrong if you skip it |
|---|---|---|
| Clean relationships and cardinality | Copilot's grounding depends on the model's join logic matching reality | Ambiguous or circular joins produce confident, wrong aggregations |
| Clear fact-vs-dimension structure | Copilot needs to know which table holds the measure, not just the label | Measures get calculated against the wrong grain |
| Reviewed, deduplicated measures | Duplicate "revenue" measures give Copilot more than one right answer | Copilot picks one silently, with no way for the user to know which |
| Unambiguous column and measure naming | Copilot matches natural language to field names | Vague names like "Amount" or "Value" get mismatched to the wrong field |
| Defined RLS roles | RLS is an accuracy control here, not just an access control | Unscoped or misconfigured roles produce wrong-but-authorized answers |
| Metadata under 200 characters | Copilot truncates longer descriptions silently | A carefully written description never reaches Copilot at all |

[Microsoft's own guide to optimizing your semantic model for Copilot](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-evaluate-data) remains authoritative on the mechanics behind each row; the harder question, covered next, is what happens once the concept exists outside this one model. A business glossary with certified term definitions, the kind [a data catalog built for AI agents](https://atlan.com/know/data-catalog-for-ai/) holds, keeps naming and measures consistent across models, which matters once [a text-to-SQL approach is weighed against a semantic layer](https://atlan.com/know/ai-agent/semantic-layer/text-to-sql-vs-semantic-layer-for-self-serve-analytics/) for self-serve analytics and once the model lives inside a broader [OneLake catalog](https://atlan.com/know/microsoft-fabric/onelake-catalog/) rather than standing alone.

---

## What happens when the same metric lives in Power BI, Fabric, and Snowflake too?

Every piece of ranking content on this topic treats semantic-model readiness as a single-workspace exercise. None of it addresses what happens when the same metric, "revenue," is defined once in a certified Power BI semantic model and differently in a Snowflake view or a second Fabric workspace, exactly where most real, Microsoft-first estates run. Certification tells you one model is ready; it says nothing about whether the others answering the same question agree.

### A worked example: three "revenue" numbers, one Copilot answer

Picture bookings revenue in a certified Power BI model, recognized revenue in a Snowflake view, and consumption revenue in a second Fabric workspace: three legitimate, differently defined numbers inside one company. Copilot answers confidently from whichever model it's grounded in, with no signal a different, equally "correct" number exists one workspace away.

The mechanism isn't Power-BI-specific, even though the scenario is. As corroborating evidence from a different platform, not direct proof of the case above, Google reported a [two-thirds reduction in errors](https://cloud.google.com/blog/products/business-intelligence/how-lookers-semantic-layer-enhances-gen-ai-trustworthiness) (Google Cloud, 2026) grounding Gemini in Looker's semantic layer instead of raw schemas. A secondary write-up from [EPC Group](https://www.epcgroup.net/insights/semantic-model-certification-ai-grounding) describes sensitivity labels flowing from source data through a lakehouse and semantic model into Copilot's grounding context; treat that as directional, not confirmed against Microsoft's own docs.

Microsoft's own AI stack shows the problem from the inside: [Fabric IQ, Work IQ, and Foundry IQ](https://atlan.com/know/microsoft-fabric/fabric-iq-vs-work-iq-vs-foundry-iq/) are three separate context sources feeding Copilot and its sibling agents, not one, and a [talk-to-data agent blueprint](https://atlan.com/know/ai-agent/talk-to-data-agent-blueprint/) built around a single platform's semantic layer inherits the same blind spot once a second platform enters. [Databricks AI/BI Genie](https://atlan.com/know/databricks/databricks-ai-bi-genie/) drives conversational analytics over Databricks' semantic layer, and [Snowflake Cortex Sense](https://atlan.com/know/snowflake/snowflake-cortex-sense/) does the same over Snowflake's; each is confidently right inside its own walls and silent about the walls themselves.

[AI-ready data lineage](https://atlan.com/know/ai-readiness/ai-ready-data-lineage/) lets a team trace "revenue" through all three systems instead of taking one certified answer on faith, the same reason [AI agent accuracy](https://atlan.com/know/ai-agent/ai-agent-accuracy/) is a context problem, not a model-quality one. A context layer keeps the business definition, lineage, and ownership behind "revenue" consistent across every system Copilot's certified model touches, which is why [AI agents need an enterprise context layer](https://atlan.com/know/why-ai-agents-need-an-enterprise-context-layer/) as infrastructure, not an edge case. None of this replaces Microsoft's own certification tooling; it extends the same discipline past the one workspace that tooling sees.

---

## How ready is your Power BI estate for Copilot, not just one semantic model?

"Is Copilot ready to roll out" and "is one semantic model certified" are different questions, and most readiness content only answers the second.

| Criterion | What Microsoft's own tooling covers | What it doesn't reach |
|---|---|---|
| Model certification | Approved for Copilot flags one semantic model as ready | No report-, dashboard-, or app-level certification exists |
| Description completeness | Flags models with thin or missing descriptions | Doesn't catch descriptions truncated at 200 characters until Copilot already has |
| RLS/OLS correctness | Confirms RLS and OLS are configured | Doesn't confirm the configuration produces the correct restricted view |
| Cross-model metric consistency | Not addressed; scoped to one model | Whether "revenue" means the same thing in a second Fabric workspace or Snowflake |
| Schema-drift monitoring | Not addressed; certification is point-in-time | Whether a certified model is still accurate after the source schema changes |
| Estate-wide ownership | Not addressed; ownership is tracked per workspace, if at all | Who's accountable when two certified models disagree |

Before a rollout, ask these internally, not of a vendor:

1. Is our semantic model Approved for Copilot, and when was it last reviewed?
2. Do our RLS roles produce the same restricted view Copilot sees?
3. Is "revenue," or our highest-stakes metric, defined identically everywhere it's calculated?
4. Who owns schema-drift detection after certification?
5. Do we have a single source for term definitions that a model outside Power BI can also draw from?

Thomas LeBlanc, Business Intelligence Architect and Microsoft MVP at SHI International Corp., frames it plainly in a 2026 [Visual Studio Magazine interview](https://visualstudiomagazine.com/articles/2026/08/27/enhancing-power-bi-with-copilot.aspx): "Use Copilot as a co-pilot, not a pilot, you are the pilot." The five questions above are what "you are the pilot" looks like in practice for a Power BI estate.

A single Power BI workspace can answer most of those five questions with Microsoft's own tooling alone; questions 3 through 5, the ones that only exist once a second workspace, platform, or team enters the picture, are where a [context layer](https://atlan.com/know/ai-readiness/context-layer-101/) earns its place, answering them continuously: not "did we pass certification once," but "is it still true today, everywhere this metric lives." The [Fabric governance evaluation checklist](https://atlan.com/know/ai-agent/microsoft/fabric-governance-evaluation-questions/) walks through the same exercise for Fabric overall, [Power BI semantic model and a dedicated semantic layer](https://atlan.com/know/ai-agent/semantic-layer/power-bi-semantic-model-vs-dedicated-semantic-layer/) covers that distinction directly, and [AI-ready data](https://atlan.com/know/ai-readiness/ai-ready-data/) asks the broader version of this question across a data estate.

---

## How Atlan approaches Power BI Copilot readiness

Microsoft's own readiness tooling is real and does exactly what it claims inside one Power BI workspace. What it can't see is the rest of a Microsoft-first-but-heterogeneous estate, where the same metric is defined once in a certified model and differently in a Snowflake view or a second workspace. Certification answers "is this model ready," not "do they all agree."

Atlan is the Context Layer for AI. For a Power BI estate, that means a business glossary with certified term definitions that "revenue" or "churn" can draw from, whether the consumer is a Power BI semantic model, a Snowflake view, or a Fabric lakehouse table; column- and measure-level lineage from source systems into Power BI models, with [Power BI column-level lineage](https://atlan.com/know/data-catalog/microsoft/power-bi-column-level-lineage/) covering the connector-specific depth; and metadata-completeness signals surfaced continuously, so "is this model ready for AI" gets answered on an ongoing basis, not at a single moment. Atlan's Fabric and Power BI connectivity is read-only, authenticated through Entra ID service principals or APIM-managed identities; it doesn't write access changes back to source systems.

No named Power BI Copilot deployment exists yet in Atlan's public customer-story roster, so none is forced in here. The broader finding behind the argument above, a 3x improvement in text-to-SQL accuracy from Atlan AI Labs' partnership research with Snowflake, is a general context-infrastructure result, not Power-BI-specific research; it already has its own sourcing on conversational analytics, Atlan's industry-wide piece, rather than being re-derived here.

The same shift, from a catalog or governance platform toward a true [enterprise data graph](https://atlan.com/know/enterprise-data-graph/), is what a [context graph](https://atlan.com/know/what-is-a-context-graph/) and [the enterprise context layer](https://atlan.com/know/what-is-the-enterprise-context-layer/) describe, and what [implementing an enterprise context layer for AI](https://atlan.com/know/how-to-implement-enterprise-context-layer-for-ai/) and [context engineering](https://atlan.com/know/what-is-context-engineering/) cover as next steps. The same pattern repeats in Microsoft's own AI stack: [Azure AI Foundry](https://atlan.com/know/ai-agent/microsoft/azure-ai-foundry/) and the [Microsoft Agent Framework](https://atlan.com/know/ai-agent/microsoft/agent-framework/) cover the agent-infrastructure side of the same estate, and [Fabric's own MCP servers](https://atlan.com/know/microsoft-fabric/fabric-mcp-servers/) cover what an agent can already do natively inside Fabric before a context layer enters.

---

## FAQs about conversational analytics on Power BI

### 1. What capacity do you need to use Copilot in Power BI?

Copilot in Power BI requires paid Fabric capacity F2 or higher, or Power BI Premium P1 or higher. Trial and free SKUs don't qualify. That dropped from the older F64 figure still circulating in some community threads; Microsoft's own documentation has required only F2+ since April 2025.

### 2. Does Power BI Copilot respect row-level security?

Yes. Copilot inherits whatever row-level security, workspace roles, item permissions, and sensitivity labels a user already has, across both the report-pane and standalone experiences. The one exception is object-level security for live-connect models: OLS works for models hosted in the Power BI service, but not for live-connect Azure Analysis Services or on-premises SQL Server Analysis Services tabular models.

### 3. Why does Power BI Copilot give wrong or inconsistent answers?

Most wrong Copilot answers trace back to the semantic model, not the AI. Ambiguous measures, missing relationships, duplicate metrics, and stale or truncated descriptions are the recurring causes practitioner communities and Microsoft's own documentation both point to. The fix is semantic-model hygiene, not a different AI model.

### 4. What does "Approved for Copilot" mean in Power BI?

It's Microsoft's model-owner certification flag: once enabled, standalone Copilot removes friction treatment from answers grounded in that model, typically within an hour, up to 24 hours for models with many dependent reports. There's no equivalent certification yet for a report, dashboard, or app, only for the model itself.

### 5. Is Power BI Q&A the same thing as Copilot, and is Q&A going away?

No, they're different features, and yes, Q&A is retiring, in February 2027. Copilot's data-question answering still depends on Q&A being enabled on the semantic model, a dependency likely to change before then but true today.

### 6. Does a certified Power BI semantic model guarantee accurate Copilot answers?

No. Certification covers one semantic model in one workspace; it says nothing about whether the same metric is defined consistently elsewhere in the estate. A certified model can produce a confidently wrong answer if a different, equally valid definition of that metric lives in another Fabric workspace, a Snowflake view, or a Databricks model.

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## The certified semantic model is the floor, not the ceiling

Copilot's verified requirements, F2+ or P1+ capacity, inherited RLS, Approved for Copilot certification, the 200-character description limit, and the Q&A dependency, are hygiene any Power BI owner can check this week. None of it is hard to fix, and Microsoft's own tooling covers every item on that list well.

The harder, undercovered question is what happens once that same business data exists outside Power BI, in Fabric, Snowflake, Databricks, or another BI tool, where Microsoft's own readiness tooling has no visibility. A certified model that quietly disagrees with a second certified model is a bigger risk than an uncertified one, because nothing flags it.

Treat the certified semantic model as the floor, not the ceiling, for a Microsoft-first-but-heterogeneous estate.

---

## Sources

1. [Copilot for Power BI overview, Microsoft Learn](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-introduction)
2. [Enable Fabric Copilot for Power BI, Microsoft Learn](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-enable-power-bi)
3. [Limitations of Power BI Q&A, Microsoft Learn](https://learn.microsoft.com/en-us/power-bi/natural-language/q-and-a-limitations)
4. [Prepare your data for AI, Microsoft Learn](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-prepare-data-ai)
5. [Optimize your semantic model for Copilot, Microsoft Learn](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-evaluate-data)
6. [Privacy, security, and responsible use for Copilot in Power BI, Microsoft Learn](https://learn.microsoft.com/en-us/fabric/fundamentals/copilot-power-bi-privacy-security)
7. [Ask data questions with Copilot and Fabric IQ, Microsoft Learn](https://learn.microsoft.com/en-us/power-bi/create-reports/copilot-ask-data-question)
8. [Copilot in Power BI Won't Fix Bad Data. It Will Expose It., Adastra](https://adastracorp.com/insights/copilot-in-power-bi-wont-fix-bad-data/)
9. [Power BI Copilot errors: why it gives wrong answers, Bismart](https://blog.bismart.com/en/power-bi-copilot-errors-wrong-answers)
10. [AI answers are only as good as the Power BI semantic model, Collab365](https://spaces.collab365.com/posts/ai-answers-are-only-as-good-as-the-power-bi-semant-Sl8vh1)
11. [Prepare Your Power BI Semantic Model for AI-Ready Analytics, SQLBits 2026](https://sqlbits.com/sessions/event2026/Prepare_Your_Power_BI_Semantic_Model_for_AI-Ready_Analytics)
12. [Semantic model certification & AI grounding, EPC Group (secondary source)](https://www.epcgroup.net/insights/semantic-model-certification-ai-grounding)
13. [How Looker's semantic layer enhances gen AI trustworthiness, Google Cloud](https://cloud.google.com/blog/products/business-intelligence/how-lookers-semantic-layer-enhances-gen-ai-trustworthiness)
14. [Enhancing Power BI with Copilot, Visual Studio Magazine](https://visualstudiomagazine.com/articles/2026/08/27/enhancing-power-bi-with-copilot.aspx)