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AI Readiness

Take Copilot and
Data Agents to Production

BI Pixie makes your semantic models and Fabric data agents ready for AI, and proves AI accuracy with benchmarks and A/B tests on your own data. The first AI Readiness assessment is free, for up to 500 semantic models and data agents on every plan.

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Free assessment for up to 500 semantic models and data agents

Three problems between you and production

Your organization has committed to adopting AI, and the question is no longer whether to do it but whether the answers can be trusted. Copilot and Fabric data agents generate their answers from the metadata inside your semantic models.

A wrong answer by AI looks like a right one

An AI agent that lacks context does not report an error. It returns a confident answer that may be wrong, and the first warning is a wrong number in an executive review. BI Pixie measures whether a semantic model can support correct answers before you open it to AI.

Get AI Readiness at scale

Optimizing one semantic model takes many iterations and tests, and a team can properly curate only a few semantic models a quarter. BI Pixie assesses your mission-critical semantic models and ranks the work by the fields your people actually query, so with AI assisting you optimize each one in minutes instead of hours.

Stay correct as semantic models change

A new measure or a deleted description can break an AI agent that answered correctly last month, and nothing flags it until a user reports it. You run the same benchmark after every change to your semantic models and data agents, and BI Pixie compares it with the run before.

The semantic model is the AI surface

Your Power BI semantic models already carry the business context AI needs, but it was written for people, not for AI agents. It can take time and many iterations until your data analysts can scope down context and resolve the ambiguity, so AI stays accurate.

To help you prepare your semantic models for AI, Power BI released Prep data for AI, which lets you curate the metadata AI needs. But as demand rises to prepare your mission-critical semantic models for Copilot and data agents, you need a way to scale up the effort.

  • How do you know which fields to exclude from the AI data schema?
  • How do you test AI on the optimized semantic model?
  • When do you know the semantic models are ready?
  • How can you reduce the iterations and speed up the process?
  • Can you scale the effort across business units and your mission-critical semantic models?

BI Pixie was built to answer these questions.

The AI Readiness page in the BI Pixie Portal. Four semantic models are listed with a readiness score, a benchmark score, the change since the previous run, the date each was last assessed, and how many benchmark runs it has. Above the list are Start assessment and New benchmark buttons, tabs for Overview, Assessments, and Benchmarks, and a line reading one schedule running with its next run time. The list is narrowed by a search for Marketing Campaigns.
Each semantic model carries its readiness score, its benchmark score, and the date it was last assessed. Lowest readiness score first.

The AI Readiness Loop

Assess, improve, prove, protect, then repeat

A semantic model drifts as people add measures and rename columns, so keeping it AI-ready is ongoing work.

1

Assess

Assess the AI Readiness of your semantic models and data agents.

BI Pixie reads each definition directly, so you know which semantic models and data agents are ready for AI. The findings are ranked by the fields your people actually query, so you know what to fix first.

2

Improve

Fix what holds AI back.

BI Pixie drafts the metadata an AI agent needs to answer correctly, from descriptions to AI Instructions. Every change is previewed, editable, and applied by you. Drafting uses an AI you connect yourself, such as Azure AI Foundry, OpenAI, or Anthropic. Assessment never does.

3

Prove

Test AI on answers you already know.

BI Pixie asks questions whose correct answers are computed live in DAX from your own data, then has the AI answer them. You see each answer, the query the AI wrote, and the correct answer beside them.

4

Protect

Catch inaccuracy before your users do.

Semantic models change, and a new measure or a deleted description can break an AI agent that worked yesterday. You run the identical benchmark again, and BI Pixie compares the result with the run before it.

The Optimize semantic model panel in the BI Pixie Portal, introduced by the line BI Pixie can make these changes to your semantic model for you. Cards offer Curate the AI data schema with a Review fields to exclude button, AI Instructions for Copilot and data agents with a Suggest AI Instructions button, Table descriptions noting eleven of eleven tables have no description, and Field descriptions covering 163 fields, grouped by severity with a checkbox on each table.
Step 2, Improve. Nothing is written into the semantic model until you review it and apply it.
How BI Pixie works. First, BI Pixie adds Pixies, invisible tracking pixels native to Power BI, to your reports and collects usage: who uses each report, how often, and how satisfied they are. Second, BI Pixie makes your semantic models and Fabric data agents ready for Copilot and AI, drafting every fix for your approval. The usage from the first step is what prioritizes the business context for AI: BI Pixie puts the fields your people actually use first, and no other tool can, because no other tool sees that usage. Third, after you apply the fixes you approve, you choose which AI to test: the query engine behind Copilot, your own Fabric data agent, or a generic AI agent. BI Pixie asks it the benchmark's real business questions and grades every answer against the correct one, computed from your own data, so you can show the accuracy improved after optimization and catch regressions when the semantic model changes.

Your BI developers can optimize a semantic model using a variety of tools. Only BI Pixie knows which fields your people actually use, and can scale up that optimization to get you ready for production.

Assess a Fabric data agent, and test whether a change made answers better

Your Fabric data agents decide how AI uses that business context. A data agent with unclear instructions, the wrong data sources or weak example queries can give wrong answers, even on a semantic model your team has already prepared for AI. A data agent carries instructions of its own, the data sources you ground it on, example queries written for those sources, and a runtime. Microsoft Fabric ignores some of what people write there. Instructions on the data agent that explain how to query a semantic model never reach the tool that writes the DAX query, and nothing in Fabric says so. BI Pixie assesses the data agent itself, and an A/B test tells you whether the work you did on a semantic model or on a data agent raised the number of questions answered correctly.

You find out which of your Fabric data agents are ready to answer your users' questions

You know which of your data agents are ready for AI and what to fix first, measured against Microsoft's published guidance for data agents. You read the findings that matter most first, with the Microsoft rule behind each one quoted in full and a link to the page it comes from. BI Pixie assesses a data agent whatever data it is grounded on: semantic models, lakehouses, warehouses, KQL, SQL and mirrored databases. A data agent cannot answer better than the semantic models it is grounded on, so BI Pixie limits its AI Readiness by theirs. Assessing writes nothing to your data agent, and when you want a finding fixed, BI Pixie drafts the change to the instructions or to a data source and you edit and approve it before it is applied.

Read about data agent assessments

You learn whether a change to a semantic model, a data agent or the AI you ask made answers better

You choose two sides, A and B, and say what you expect before anything runs, such as side B answering more of the questions correctly. BI Pixie asks both sides the same questions and checks both against the same correct answers, computed from your own data before either side is asked anything. At the top of the result you read what you expected and whether it held, so a change that did not help is as plain to read as one that did. Side A and side B can be the semantic model in production and the copy your team is optimizing, one semantic model answered by both a Fabric data agent and Power BI Copilot, or two data agents that differ only in their instructions. BI Pixie keeps every A/B test with its questions and its result, so the next person who asks whether a change is worth making can read what happened the last time it was tried.

Read about A/B tests

A benchmark's questions and its correct answers come from a semantic model, so they cover the data in that semantic model rather than a data agent's other sources. A data agent grounded on no semantic model at all cannot be benchmarked or put in an A/B test yet. BI Pixie assesses that data agent all the same, and checks the instructions, the descriptions and the example queries of each lakehouse, warehouse, KQL database, SQL database or mirrored database it is grounded on against Microsoft's guidance for that kind of source.

An A/B test result comparing two semantic models, each answered by its own Fabric data agent. A card for each side names its semantic model, its workspace, the kind of AI answering for it, which Fabric data agent that is, and that semantic model's AI Readiness score. Side A is the unprepared semantic model, at 47 out of 100 and labelled Not ready for AI. Side B is the prepared one, at 75 out of 100 and labelled Needs curation. Both sides answered 4 of 6 questions correctly, 67 percent each, which BI Pixie reports as a difference of 0 points read from side A to side B. BI Pixie states that the hypothesis that side B will answer more questions correctly than side A is rejected. Below the verdict, a strip shows every question twice, once for each side, above four tiles headed The sides differ, Improvements, Regressions and Same data.
An A/B test result in the BI Pixie Workload for Microsoft Fabric. In this run side B scored 28 points higher on AI Readiness and still answered the same 4 of 6 questions correctly, so BI Pixie rejected what the team expected. A readiness score tells you what stands between your data and a correct answer, and an A/B test tells you what your own work changed.

What only BI Pixie can do

You fix what your people actually use first

Anyone can read your semantic model's structure. Only BI Pixie knows which fields your people actually query, so the findings are ranked by what your audience uses rather than by a generic rulebook.

You get an AI Readiness assessment and a benchmark in one product

BI Pixie assesses the semantic model and then answers whether it is actually better, with a benchmark keyed to your own data.

You test the AI you actually ship

You can benchmark through a generic AI agent to diagnose problems, through the same query engine behind Copilot to confirm the result, or through your own Fabric data agent. The generic AI agent is your own AI answering from the semantic model, the way a custom agent would, and every wrong answer it gives is traceable to the query behind it.

You stay in control

Assessment is read-only. Every change is previewed, editable, and applied by you, and assessments never run on your capacity.

Assessment is read-only and repeatable, and every benchmark is graded against answers computed from your own data. BI Pixie collects viewer user names only when you turn on Capture user identity, and until then it counts viewers by a masked IP address.

Catch inaccuracy before executives lose trust

Getting a semantic model AI-ready is work you can finish. Keeping it AI-ready through a year of changes requires evidence that nothing has regressed. On the Enterprise plan, BI Pixie also runs AI Readiness assessments on a schedule you set, so drift in the context is caught early.

You set a baseline before you go live

BI Pixie saves the benchmark questions built from what your people actually use, and you review every question and its computed answer before the first run.

You run the identical benchmark after every change

A single new measure or a deleted description is enough to change the answers an AI agent gives. Because the questions are identical, the comparison is honest.

You choose whether the correct answers are fixed or recomputed

You can check against the answers saved when the benchmark was created, or recompute them from current data. BI Pixie tells you when the correct answers themselves have drifted, and marks a benchmark that was edited, so you always know whether two runs can be compared.

You compare any run with the run before it

Every run is kept per semantic model with its change from the run before, grouped by the AI that was tested. Scores from different AIs are never combined into one trend.

A benchmark result in the BI Pixie Portal, headed Marketing Campaigns, checked Aug 20, 2026, via a generic AI agent. A 58 percent correct score sits above a strip of per-question marks and the line seven of twelve questions answered correctly. An About this run block lists who answered, the questions asked and scored, the question mix, the reporting period, what the AI could see, and which correct answers were used. Below it, a control chooses between the original answers and answers recalculated from your data, and a Weak spots section begins.
Every run records which AI answered, what it could see, and which correct answers it was checked against.

What BI Pixie checks in a semantic model

BI Pixie shows how each of these contributes to a semantic model's AI Readiness, so you know which fixes are worth making first.

  • Description coverage
  • Name disambiguation
  • Field clarity
  • AI data schema
  • AI Instructions
  • Indexing limits
  • Semantic model structure
  • Time window safety
  • PII and RLS safety
  • Field defaults
  • Display folders
  • Implicit measures
An AI Readiness assessment result in the BI Pixie Portal. A dial shows a readiness score of 61 with the verdict Needs curation, beside twelve dimension tiles that each carry a count and a percentage: AI data schema at 0 percent, AI Instructions at 67 percent, Description coverage at 34 percent, Name disambiguation, Field clarity, Indexing limits, Field defaults, Display folders, Implicit measures, Semantic model structure, Time window safety, and PII and RLS safety at 0 percent. A trend card beside the dial shows a change of minus 9 since the last assessment, and the header reads eleven tables, 169 columns and 47 measures analyzed.
Whether the semantic model is ready for AI, and what to fix first.

Getting started

The first AI Readiness assessment is free, for up to 500 semantic models and data agents

A first look at 500 distinct items, semantic models and data agents together, is included on every plan, paid or free. The Free plan also covers one tracked report, one tracked semantic model or data agent, and one benchmark run on your tracked semantic model. Paid plans are priced by the number of items you track, with no usage meters and no overage charges. Building a benchmark is included on every plan, and paid plans include more benchmark runs.

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Download the AI Readiness one-pager (PDF, one page, no form.)

Frequently asked questions

How do I prepare my data for AI in Power BI?
Copilot and Fabric data agents generate answers from the metadata inside your semantic model rather than from your reports. Preparing data for AI means giving that metadata enough context to answer from: business-friendly table and column names, descriptions on the fields people actually query, explicit measures rather than implicit aggregations, relationships that match how the business really works, and AI instructions carrying your organization's vocabulary. Power BI groups several of these under the Prep data for AI button on the semantic model ribbon. BI Pixie assesses which of them are missing across your mission-critical semantic models, and drafts the descriptions and instructions for your review.
How do you measure AI readiness?
AI Readiness is measured against the context an AI agent needs before it can answer correctly. BI Pixie assesses each semantic model on description coverage, name disambiguation, semantic model structure, PII and RLS safety and more, so you know whether it is ready for AI, then ranks the remaining work by the fields your people actually query, so the fields that carry the most usage are addressed first. An assessment on its own is a prediction, so the second half of the measurement is a benchmark that asks the AI questions whose correct answers are computed from your own data.
Can Power BI be used with AI?
Yes. Copilot answers questions in natural language from a semantic model, and Fabric data agents answer across several data sources at once. Both generate their answers from the metadata your semantic model exposes, which is why two organizations running the same version of Copilot can get very different quality of answers from it.
Why does Copilot give wrong answers?
Usually because the semantic model does not carry enough context to resolve the question. Two measures with similar vague names leave the AI agent choosing between them. Missing column descriptions leave it guessing what a field holds. Relationships that do not reflect the real business structure let it combine data in ways that read as coherent and are analytically wrong. An AI agent does not report an error when this happens. It returns a confident answer, so the first warning is often a wrong number in an executive review.
How do you test whether Copilot or a Fabric data agent answers correctly?
By asking it questions whose correct answers are already known. BI Pixie builds a benchmark from your semantic model, computes the correct answers from your own data, runs the questions against Copilot or a Fabric data agent, and checks the responses. You run the same benchmark again after every change to your semantic model or data agent, and BI Pixie compares the result with the run before it. That is how you catch the day a previously correct answer quietly stops being correct.

Find out whether your semantic models are ready for AI

The first assessment is free for up to 500 semantic models and data agents, so you can find out where you stand before you commit anyone's time.

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