AI Tool Evaluation Checklist for Project Managers
A 12-question checklist for project managers evaluating AI tools before recommending them to their team. Covers data security, workflow integration, governance, and ROI — what vendor demos skip.
Evaluate AI tools before you recommend them to your team.
A 12-question checklist for project managers who need a structured, defensible evaluation framework — not vendor demos.
Who this is for
Project managers, PMO leads, and delivery leaders who have been asked to evaluate, recommend, or approve an AI tool for actual team use. If you need to make a credible recommendation — and be accountable for it — this checklist is for you.
What you get
- 12 evaluation questions across four categories
- Data and security — what the tool does with your project data
- Workflow and integration — whether it fits your actual delivery process
- Governance and accountability — who owns outputs, what can be audited
- ROI and adoption — whether the tool will actually stick with a real team
- A clear scoring guide: proceed to pilot, conditional approval, or reject
Why it matters
Most AI tool evaluations focus on features. Project managers are accountable for a different set of criteria: data handling, integration risk, output auditability, and whether the tool will survive contact with a real delivery workflow. This checklist covers what vendor demos skip.
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About AI for Project Managers
A practical weekly publication for mid-career PMs and PMO leads navigating AI adoption in real delivery environments. No hype. No theory. Tools, frameworks, and decisions you can use this week.
Published by Indigo.pub — practical intelligence for the AI-native professional.