5 signs your AI strategy needs a clearer plan

An AI proposal should make a business decision easier. If you are responsible for approving one, read it with a specific task in mind. What changes for the person doing that task, and how will you know whether the change was worthwhile?

The five checks below are a proposal review method. They are not observations about a particular client or a claim that every project must become production software.

1. The goal does not name the work

“Adopt AI” leaves too much undecided. Describe the task, who performs it, and the difficulty they face. An illustrative goal could be reducing the time spent preparing an internal handoff while preserving the original commitments accurately.

The qualification matters. Speed without accuracy could make the handoff worse.

2. The deliverable is unclear

Discovery can be a worthwhile assignment if it resolves an uncertainty. Training can be complete when the team can perform and review the agreed task. A build should specify the working capability.

Ask what you can inspect at the end. If every milestone describes an activity, add the artifact or decision that activity is meant to produce.

3. The evaluation ignores rework

Record a baseline for the whole task, including preparation, checking, and repairs. Compare similar work after the change. Decide what would count as an unacceptable result even if the process is faster.

Avoid a target chosen only because it looks impressive in a proposal. A team may first need a small evaluation to understand the current process.

4. Essential capabilities are assumed

List the tools, access, integrations, and permissions the proposal needs. Test the critical path using the account and environment that will actually be used.

If something depends on an unavailable feature or an unapproved data source, make that dependency visible. It may justify a different approach or a pause.

5. Ownership ends at the presentation

Name the person responsible for the result after handoff. For software, include failure handling and maintenance. For training, include instructions and follow-up practice. For advice, identify who decides whether to implement it.

NIST's voluntary framework is useful background for this kind of responsibility and evaluation planning. Referencing the framework does not certify an engagement. NIST AI RMF.

Replace the vague proposal with a brief

Write one page containing the task, intended deliverable, baseline, evaluation criteria, required access, owner, and reasons to stop or defer. Then ask the provider to respond to it.

Use the consultant selection guide to compare responses. If the assignment concerns existing tools and workflows, AI Automation describes the help available at Jiberish Labs.