Reviewed and updated October 8, 2026.
An AI evaluation needs more than a model choice. Before approving a pilot, check whether the workflow, sources, review method and operating responsibilities are defined. Use the unscored worksheet below to identify what is ready, what is uncertain and who must resolve it.
This is a practical planning aid. It does not assign an AI maturity score or rank industries. Readiness depends on the particular task and deployment conditions.
Start with one workflow
Write down the input, requested output, user and accountable owner. Describe what people do today and which failure you want to address. Include a process change or existing software as an alternative. An AI pilot is useful when it resolves a specific uncertainty, rather than demonstrating that a model can produce an answer.
Unscored readiness worksheet
Copy the table and add an evidence link, owner and next action for each row. Use “documented,” “needs a test” or “unresolved” as plain status labels. Do not add them into an overall score.
| Question | Evidence to inspect | Decision it supports |
|---|---|---|
| Is the task bounded? | Workflow steps, exceptions, user needs and current failure examples | What the evaluation must include and exclude |
| Are the sources usable? | Representative records, source ownership, update process and missing information | Whether the proposed system has the inputs it needs |
| Is access agreed? | Permissions and a map of prompts, files, outputs, logs, backups and external services | Which deployment options can be considered |
| Can outputs be judged? | Baseline, held-out examples, rejected errors and named reviewers | What evidence permits acceptance |
| Can people act on the output? | Approval, escalation and correction steps, plus user training needs | Where a person remains responsible |
| Can the workflow be operated? | Monitoring, incident handling, source refresh, change approval and support ownership | Whether a successful test can proceed to rollout |
| Is the cost comparison traceable? | Implementation and recurring costs, benefit assumptions and alternatives | Whether the next phase is justified |
Make unresolved items specific
For an internal knowledge assistant, “data ready” is too vague. A more useful entry names the document collection, identifies who maintains it, checks a sample for outdated instructions and tests whether restricted documents stay restricted. This is an illustrative planning example, not a finding about your organization.
Resolve the dependency that could change the decision first. If access is unclear, agree the data path before loading information. If answer quality is unclear, test representative questions before expanding integrations. If ongoing ownership is unclear, define that responsibility before rollout.
Use firsthand reports within their limits
The intent-aware retrieval report documents a demonstration proof and its remaining evaluation work. The production-gap review separates a production platform from proofs, prototypes and planned capabilities. Neither establishes an industry readiness score.
For a scoped evaluation plan, see AI roadmap and business-case planning. Request a project review with the worksheet, current sources and the decision you need to make next.
