> For the complete documentation index, see [llms.txt](https://docs.datalogz.io/guides/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.datalogz.io/guides/ai-readiness-assessment.md). # AI Readiness Assessment Before you point AI at your analytics, you need to know what it will inherit. Every copilot, agent, and AI-generated answer in your organization draws on the semantic models that exist right now: their definitions, their structure, and their drift. The AI Readiness Assessment in Datalogz Control Tower scores how ready your Power BI semantic models are for AI consumption, and shows you exactly what is changing in them, so you can fix trust problems at the source before they reach an AI-generated answer. **Availability** * **Power BI**: AI Readiness Assessment is available for Power BI semantic models (datasets). * **Databricks**: Coming soon ### How it works Datalogz continuously analyzes the semantic models your team owns and surfaces AI-exposure findings: conditions in a model that make its output harder for AI (or people) to trust. Each finding is raised as an alert, and every model receives a readiness status based on its open findings. A model's readiness feeds directly into its overall health. Every model carries one of three statuses: * **Healthy**: no open alerts of any kind. The model is AI-ready and passing all monitors. * **Needs Attention**: the model's only open alerts are AI Readiness findings. Nothing is broken today, but the model is not ready to be consumed by AI. * **Unhealthy**: the model has open alerts from monitors outside the AI Readiness Assessment, such as governance, cost, or performance monitors. This is the same status you see in the Inventory, and it is carried through consistently: a CSV export of your inventory reports the same health label as the app. ### The AI Readiness Summary The AI Readiness Summary gives your team a single view of readiness across its Power BI semantic models. * **Readiness overview**: how many of your team's models are AI-ready, and how many need attention. * **Needs Attention**: models with open findings link straight to the asset's open alerts and its place in the Team Inventory, so you can move from a readiness gap to the specific finding in one click. * **Last synced**: the timestamp reflects the end of the most recent successful analysis run covering your team, so you can always tell how fresh the assessment is. {% hint style="info" %} Accepted findings are shared connector-wide. If another team accepted a finding on a shared connector, it counts here too, which is why accept counts can exceed the number of alerts your own team has handled. {% endhint %} ### Recently Changed Models Model drift is one of the biggest hidden risks in AI adoption: a measure gets redefined, a column disappears, and every AI answer built on that model silently changes with it. The **Recently Changed Models** card surfaces this drift as it happens. For each recently changed model, the card shows: * **What kind of change occurred**: tables, columns, and measures that were **added**, **removed**, **modified**, or **renamed** between the model's consecutive snapshots. * **The specific fields affected**: each change line lists the actual object names and counts, with hover to see the full list. {% hint style="info" %} Change tracking begins when the assessment first runs, with an initial look-back window of 30 days by default. Cosmetic source-only edits that do not change the model's schema are not flagged. {% endhint %}
### Acting on findings AI Readiness findings behave like any other alert in Control Tower, which means they come with a next action: * **Alerts**: open findings appear in Alerts under the AI Readiness category, with the usual workflow statuses, comments, and ownership. * **Monitors**: AI Readiness monitors are labeled with the **AI Readiness** type in the Monitor Catalog. * **Workflows**: run a remediation workflow directly from the alert, individually or in bulk across an asset's active alerts, so every finding is routed to the right owner and tracked to resolution. ### Why this matters AI does not fix a broken analytics foundation. It amplifies it, and inherits every stale model, every silent schema change, every conflicting definition your estate has accumulated. The AI Readiness Assessment turns that risk into a scored, monitored, and actionable view: which models your AI can trust today, which ones need work, and what changed since you last looked. --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter: ``` GET https://docs.datalogz.io/guides/ai-readiness-assessment.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.