Collectiv’s AI readiness assessment audits the Microsoft Fabric and Power BI environment you already run: the lakehouses, warehouses, and semantic models behind your reports, checked against the practices Copilot and other AI tools need to interpret them correctly.
A semantic model built for business intelligence is not the same as a semantic model built for artificial intelligence. BI models can work well for human analysts while still lacking the structure, context, relationships, descriptions, and instructions AI needs to interpret data accurately. This assessment identifies those gaps across your Fabric and Power BI assets before you scale Copilot or another AI workload across the business.
Enabling Copilot in Microsoft 365 or Fabric turns the feature on. It doesn’t confirm the data behind it is ready to be interpreted by a model.
A Power BI report built for a human reader can lean on that reader already knowing which measure to use, which date table applies, and what “active customer” means in this context. Copilot has to make that same judgment call on its own, from whatever metadata, descriptions, and relationships actually exist in the semantic model.
When that structure is thin, the gap shows up as:
Inconsistent answers to the same question asked two different ways
Answers that are confidently wrong rather than obviously missing
No answer at all, or a fallback that dodges the question
Users who quietly stop trusting Copilot after a few bad results
Pilots that never expand past the team that built them
The fix is rarely a newer model. It’s usually the semantic layer underneath it, and that’s what the assessment exists to check. For a closer look at how this gap shows up in practice, see our breakdown of whether your data is actually AI-ready.
Collectiv’s AI Readiness Auditor runs 100+ best-practice checks against the Fabric and Power BI assets in your tenant, grouped into four areas.
Whether Copilot and other AI tools can tell what a table, field, or measure actually means.
The modeling practices underneath the semantic layer, and whether they hold up under scrutiny.
How access and certification affect what an AI tool is allowed to surface, and to whom.
Whether the model and platform can support AI workloads at production scale.
A score on its own doesn’t tell you much if you don’t know what produced it. The auditor drills into individual semantic models and other assets to show exactly which checks are passing, which need attention, and what to change.
That includes things like:
Missing measure descriptions
Weak or missing synonyms
Unclear or missing relationships
Orphan tables
Data freshness problems
Governance gaps
DAX and modeling issues
Each finding comes with an initial remediation suggestion, and where it’s useful, AI-generated recommendations that are more specific to your model. Teams that want help acting on the results, rather than implementing every fix themselves, can bring in Collectiv’s managed services team to handle it on an ongoing basis.
Results can be exported as a PDF, so teams that need to share findings with a steering group or a budget owner aren’t stuck screen-sharing a scan.
Most organizations are investing in more than one part of the Microsoft AI stack at once: Copilot, Fabric, Power BI, Azure OpenAI, and other LLM applications built on top of the same data. Few confirm first that the environment underneath can support reliable answers.
Poor readiness leads to unreliable answers, unreliable answers erode trust, low trust suppresses adoption, and low adoption leaves the AI investment sitting mostly unused.
A data foundation with clear business context, real governance, and models structured for interpretation gives every AI tool built on top of it a much better environment to work in, this assessment included. Our post on the four steps to becoming AI-ready with Microsoft Copilot walks through what that looks like in practice.
Install AI Readiness Auditor directly as a Fabric Workload within your Microsoft Fabric environment. This option lets your team run the auditor within Fabric, select the workspaces you want to assess, and evaluate the lakehouses, warehouses, and semantic models you have access to.
Access the auditor as a Collectiv-hosted application through a web browser. Set up the required connections and permissions once, then run scans as needed without managing any infrastructure yourself.
Organizations that need more control can deploy the application within their own environment and firewalls, retaining ownership of the application while continuing to run scans and assessments on their own schedule.
The auditor is built for organizations that:
Are preparing to roll out Microsoft Copilot
Have invested in Microsoft Fabric but aren’t sure whether the data is AI-ready
Have Power BI semantic models that work fine for reporting but produce inconsistent AI results
Are already seeing unreliable Copilot answers
Want to check readiness before expanding AI further
Need a repeatable way to monitor and improve AI readiness across the data estate
If you’d rather have someone walk through the results with you, Collectiv’s AI consulting team can help interpret the findings and prioritize what to fix first.
An AI readiness assessment evaluates whether an organization’s data, models, infrastructure, governance, and business context can reliably support AI workloads, not just whether the AI tool itself is turned on.
It’s software that checks readiness directly rather than asking you to describe it. Collectiv’s tool connects to your Microsoft environment and evaluates the actual Fabric and Power BI assets in it, rather than relying on self-reported answers.
You authenticate with your Microsoft account (or a service principal, for administrators), select the workspaces to include, and the auditor scans the Fabric and Power BI assets in them against 100+ best-practice checks. You get scores, specific findings, and remediation recommendations, and can rescan later to track progress.
That depends on the quality, structure, metadata, governance, and performance of what’s already in your tenant, not just whether Copilot is licensed and enabled. The auditor is built to identify exactly where those gaps are.
Four areas: AI and Copilot readiness (descriptions, synonyms, relationships), data quality (modeling and DAX practices), governance (security and certification), and performance (refresh, filters, model size), with specific technical checks under each.
Yes. It’s available as a Collectiv-hosted SaaS application, or as a client-hosted deployment for organizations that want to run it inside their own environment and firewalls.
Turning on Copilot is not the same as making your data AI-ready. That takes a reliable data foundation, clear business context, real governance, and models structured so an AI tool can actually interpret them.
Collectiv’s AI Readiness Auditor is a practical way to see what’s already working, identify what’s holding AI back, and know what to fix next, with our team available to help you act on the findings.