AI Readiness

Take Copilot and
Data Agents to Production

BI Pixie assesses the AI Readiness of your semantic models, optimizes them, and benchmarks Copilot and data agents to prove they stay correct. The first AI Readiness assessment of each semantic model is free, for up to 500 semantic models on every plan.

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Free assessment for up to 500 semantic models · No credit card needed

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 in Power BI 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 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.

How do you get AI Readiness at scale?

Optimizing one semantic model takes many iterations and tests, and you have hundreds of semantic models with a team that can properly curate a few of them 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 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 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 agents. It can take time and many iterations until your data analysts can scope down context and resolve the ambiguity, so AI attention stays on what answers the question.

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 hundreds or even thousands of 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.

BI Pixie produces one AI Readiness score from 0 to 100, read directly from the semantic model definition. The findings are ranked by the fields your people actually query, so you are told what to fix first.

2

Improve

Fix what holds AI back.

BI Pixie drafts the metadata an agent needs to answer correctly, from descriptions to AI Instructions. Every change is previewed, editable, and applied by you. Drafting uses an AI provider you connect, 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 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 assesses the AI Readiness of your semantic models for Copilot in Power BI and for Fabric data agents, 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 in Power BI, 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.

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 in Power BI to confirm the result, or through your own Fabric data agent. The generic AI agent is your connected AI provider 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. No personal data is collected by default.

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 agent gives. Because the questions are identical, the comparison is honest.

You choose whether the correct answers are fixed or recomputed

You can score 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 scored against.

The 12 AI Readiness dimensions

BI Pixie reports how each dimension contributed to the score, 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 75 with the verdict Needs curation, beside thirteen dimension tiles that each carry a count and a percentage: AI data schema, AI Instructions, Description coverage at 6 percent, Name disambiguation, Field clarity, Indexing limits, Field defaults, Display folders, Implicit measures, Model structure, Time window safety, PII and RLS safety, and Verified answers. A footer reads eleven tables and 216 fields analyzed.
One score from 0 to 100, and how each dimension contributed to it.

Getting started

The first AI Readiness assessment of each semantic model is free, for up to 500 semantic models

A first look at 500 distinct semantic models is included on every plan, paid or free. The Free plan also covers one tracked report, one tracked semantic model, and one benchmark run on that 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 every semantic model you own, and drafts the descriptions and instructions for your review.
How do you measure AI readiness?
AI Readiness is measured against the context an agent needs before it can answer correctly. BI Pixie scores each semantic model from 0 to 100 across twelve dimensions that include description coverage, name disambiguation, semantic model structure, and PII and RLS safety, then ranks the remaining work by the fields your people actually query, so the fields that carry the most usage are addressed first. A score 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 in Power BI 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 Power BI 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 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 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 ground truth from your own data, runs the questions against Copilot or a Fabric data agent, and scores the responses. You run the same benchmark again after every change to your semantic model, 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.

Is your semantic model ready for AI?

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

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