Which AI tool is best for Power BI?

The Real Cost of Power BI's Default AI Option

If you want AI in Power BI, Microsoft Copilot is the obvious answer — and that's exactly the problem. Copilot is gated behind Premium or Fabric capacity, with the F64 tier required for real-world use costing roughly $5,000 per month at list price, according to practitioner analysis.

The entry point looks cheaper on paper. Fabric's F2 capacity runs $262.80 per month, but community measurements report an F2 capacity being exhausted after roughly 20 questions. Even Premium Per User at $24 per month doesn't include Copilot at all.

Price aside, the quality question is just as uncomfortable. SQLBI — the leading independent Power BI authority run by Microsoft MVPs Marco Russo and Alberto Ferrari — states that conversational BI with Copilot has been "rather underwhelming", and that AI in Power BI remains "far from the transformative impact" promised in marketing materials. A Power BI MVP has reached a similar verdict, calling Copilot's ask-a-question features "promising but kind of underwhelming for many users so far."

Microsoft's own documentation doesn't help the sales pitch. It acknowledges that Copilot outputs "can contain inaccurate and low-quality content" and are "nondeterministic." So before committing budget, teams should be clear about what they're actually buying:

• A capacity bill of roughly $5,000/month for F64, or a severely constrained F2 tier
• Conversational BI that independent experts judge underwhelming for free-form analysis
• One genuinely strong capability: DAX and M code generation, which practitioners call Copilot's most useful feature
• Answers that scale with your semantic model quality — a well-modeled star schema gets sensible answers; a flat table gets confused ones

That last point matters. Practitioners rate Copilot's DAX generation as "the single most useful piece of Copilot in 2026 for most analyst teams" — but it's a developer productivity tool, not the conversational analyst many buyers imagine. If you already pay for Fabric capacity, use it for authoring and DAX help rather than expecting chat-style insights.

The real issue is that the default option locks AI behind infrastructure spend, which pushes most teams to look beyond it. That search is worth doing carefully: the same independent experts who criticize Copilot point to alternatives like MCP servers that need no Fabric capacity at all — and warn that any third-party extension touching your data needs proper vetting. Research on agent skills found roughly 26% carry at least one vulnerability, which is why we're building SkillzBench to independently scan, benchmark, and verify AI extensions before you connect them. The default isn't the only option — but the alternatives deserve scrutiny too.

Three AI Approaches That Actually Work with Power BI

The good news? You don't have to choose between "expensive" and "useful" anymore. Independent experts have converged on three approaches that genuinely deliver value with Power BI — and each one solves a different problem.

1. Copilot, used for what it actually does well

Microsoft Copilot is the native option, but it's not a universal win. SQLBI, a leading independent Power BI authority, notes that conversational BI with Copilot has been underwhelming, while its biggest practical impact so far has been helping write and troubleshoot DAX and M code. Practitioners agree: Thinklytics calls Copilot's DAX generation "the single most useful piece of Copilot in 2026 for most analyst teams." If you already pay for Fabric capacity, lean on Copilot for authoring and calculations — not free-form analysis. One caveat: Copilot requires Premium capacity or Fabric F64+, at roughly $5,000/month list price.

2. MCP servers: the dramatic, low-cost alternative

The Model Context Protocol is the development SQLBI describes as having "potential for a dramatic impact" on how we work with Power BI. An MCP server lets an LLM host like Claude Desktop control Power BI in natural language — without any Fabric capacity, running locally on Windows or Mac. Unlike Copilot's one-question-one-query flow, MCP enables a reasoning flow with multiple queries and alternate approaches on failure. Best of all, a custom server can be built and running in less than an hour. Just validate third-party servers carefully before connecting production data — a concern SkillzBench is addressing directly with security scanning and sandboxed benchmarking for agent tools and skills.

3. AI coding assistants for custom dashboards

Claude Code and Cursor are quietly disrupting Power BI's visualization layer. Power BI professionals with no coding background now build fully interactive dashboards in HTML, React, and d3.js from natural-language prompts — and AI-generated dashboards frequently outperform Power BI reports visually, according to Power BI MVP Nikola, who notes Power BI's visual layer "has been stagnant." His advice: multiclass your skills, because the visual layer faces serious competitive pressure within two to three years, while the semantic model and governance remain durable assets.

Which approach fits depends on your starting point:

• No Fabric capacity? Start with an MCP server — free, fast, and expert-endorsed.
• Already paying for capacity? Use Copilot for DAX and report authoring.
• Need standout visuals? Use a coding assistant for custom dashboards.

Whatever you pick, the same rule applies: verify before you trust. SkillzBench's free directory exists precisely because independent validation of AI extensions — not vendor promises — is what separates real productivity from hype.

How to Choose: Matching the Tool to Your Team and Budget

The cheapest AI upgrade for your Power BI stack may already be sitting in your existing license — or it may cost nothing at all. The deciding factor is simpler than most teams expect: whether you already pay for Fabric capacity.

If you do, lean on Copilot where it actually earns its keep. Practitioners rate DAX generation as Copilot's single most useful capability — "the closest thing to a productivity tool the platform has shipped in the last five years" — rather than expecting transformative conversational analysis, which independent experts at SQLBI describe as underwhelming.

If you don't pay for capacity, the math changes everything. Copilot requires Premium capacity or Fabric F64+, at roughly $5,000/month list price, and even the $262.80/month F2 entry gate was reportedly exhausted after about 20 questions in community testing, per Colrows' analysis. A custom MCP server, by contrast, can be running in less than an hour with no capacity cost at all.

Before any tool choice, though, check three prerequisites:

• Semantic model quality: Q&A quality scales with the underlying model — a well-modeled star schema gets sensible answers; a flat ungoverned table gets confused ones.
• Q&A retirement deadline: Microsoft retires classic Power BI Q&A at the end of December 2026, with Copilot as successor — plan your migration window now.
• MCP server validation: SQLBI warns that third-party and open-source MCP servers must be carefully vetted before touching production data; Microsoft-built or custom servers are the safe path.

The governance risk is subtler than most teams assume. Both Copilot and its peers inherit existing row-level security, so the danger isn't access bypass — it's that AI makes it dramatically easier to discover, share, and combine information across boundaries that used to be friction-protected. Tighten what each user can reach before you make everything easier to find.

That validation burden extends to the AI extensions themselves. Research has found roughly 26% of published agent skills carry at least one vulnerability, which is why SkillzBench is building independent security scanning and benchmarking for agent skills and MCP-connected tools — so teams can verify what they're plugging into their data before it's live.

Getting Started Safely: Implementation Steps and the Verification Problem

The cheapest AI tool for Power BI is worthless pointed at a bad model. Before you evaluate anything, fix the foundation.

Start with the semantic model. Practitioners are blunt about this: Copilot's Q&A quality "scales with the quality of the underlying model," where a well-modeled star schema gets sensible answers while a flat ungoverned table gets confused ones, per Thinklytics' analysis. MCP servers reduce — but don't eliminate — up-front model preparation, so clean relationships, clear measures, and proper DAX come first.

Then set up an MCP server. SQLBI reports that a custom Power BI MCP server can be created and running in less than an hour, running locally on Windows or Mac, with no Fabric capacity required. Compare that to Copilot's ~$5,000/month capacity gate, and the appeal is obvious. Microsoft-built or custom-built servers using Microsoft APIs are the safe choices.

Here's the part most guides skip: the verification problem.

• Third-party MCP servers need careful validation before they touch production data — SQLBI explicitly warns users must vet open-source servers before connecting them.
• Published research found roughly 26% of agent skills carry at least one vulnerability, so "it's on GitHub" is not a security review.
• AI governance risk is oversharing, not bypass — AI makes it dramatically easier to combine information across boundaries that used to be friction-protected, per practitioner analysis.
• Microsoft itself acknowledges Copilot outputs "can contain inaccurate and low-quality content" and are nondeterministic, per Colrows' assessment — verification applies to native tools too.

A sensible sequence: harden the semantic model, build or adopt a verified MCP server, connect it first to a sandbox workspace with non-sensitive data, and only then point it at production. Test with questions whose answers you already know, and check what the agent actually sees and can access.

This is exactly the gap SkillzBench is being built to close: a free, independent directory of agent skills where every version is pinned to a commit SHA, security-scanned, license-checked, and benchmarked in a sandbox — plus a free MCP connector so your agent only loads what you've explicitly vetted. For Power BI teams, that means you can discover and verify AI extensions before they ever touch your semantic models, rather than trusting a README.

The tools are cheap and fast to set up. The discipline around them is what keeps your data safe.

Skills, proven. Browse and verify free agent skills at skillzbench.com — every skill security-scanned, benchmarked, and pinned before it earns a trust score.