An AI readiness checklist for industrial and energy businesses

AI readiness is not the number of tools your company has bought. It is the ability to choose a useful workflow, supply suitable inputs and decide whether a pilot improves the work.

For energy and industrial businesses, begin with an assistive use case that people can review. Document preparation, information retrieval and draft summaries may be easier to evaluate than changes affecting equipment operation. Suitability still depends on the specific business and risk assessment.

Choose a bounded workflow

Describe the task in plain language. Identify its inputs, users, output and current review process. Define where the AI system would assist and where a person retains control.

Avoid a pilot whose scope is “transform the company.” A bounded task makes errors easier to detect and the result easier to assess. State what the pilot will not cover so that adjacent teams do not silently expand its purpose.

Check the data before the model

Inspect a representative sample of the information the workflow requires. Check completeness, inconsistent naming, duplicate records, outdated versions and missing context. Ask who can correct an error at its source.

Identify which data can appropriately be shared with a vendor. The need to review data access is distinct from the question of whether the model performs well. Both matter.

Create a limited pilot dataset with a named owner and a documented purpose. Resolve access before promising an implementation date.

Identify the operating owner

Assign one business owner to the pilot. That person should be able to define acceptance criteria, coordinate users and decide whether the result is useful. Technical support does not replace process ownership.

Clarify who approves access, investigates incidents and communicates changes. When these responsibilities are unclear, the team cannot reliably evaluate what the tool changed.

Build an evaluation set

Collect ordinary tasks, difficult tasks and tasks where the correct response is to seek clarification. Agree how outputs will be reviewed and how disagreements between reviewers will be handled.

NIST’s AI Risk Management Framework is a voluntary reference for considering AI trustworthiness and risk. Use it to support a structured conversation, while assessing the actual workflow and vendor separately.

Track correction work and user effort. A faster first draft may offer little benefit if it creates more checking elsewhere.

Plan for failure and exit

Document what users do when the tool is unavailable or produces an unsuitable result. Keep a workable fallback during the pilot. Test data export and account administration before expanding usage.

Set a decision date and a limited budget. At the review, choose to proceed, redesign or stop based on the agreed criteria. Completing a pilot does not automatically justify scaling it.

Use the checklist as a readiness discussion

You are ready for a controlled pilot when the workflow, owner, inputs, evaluation and fallback are clear enough to test. Missing items are preparation work, not reasons to buy a larger platform.

Before purchasing, extend the review with the AI vendor due diligence checklist. Keep readiness and procurement connected: the best vendor proposal still needs a business that can use and evaluate it.