New AI tools launch weekly, each promising to save time or unlock some new capability. Most organizations don't have a formal process for deciding which ones are actually worth adopting — tools get added because one person tried it and liked it, then it spreads informally. That's how inconsistent, ungoverned AI use creeps into an institution. A short, repeatable checklist fixes this without slowing things down much.
The Six Questions
1. What data does it collect, and where does it go? Check the privacy policy specifically for training data use, third-party sharing, and retention periods — not just the marketing page.
2. Does it have an education or business tier with a data agreement? Free consumer tools rarely offer the same protections as their paid, institutional counterparts.
3. What happens when it's wrong? Every AI tool produces errors. Ask what the failure mode looks like and whether it's low-stakes (a slightly off email draft) or high-stakes (a grading or diagnostic decision made with no human review).
4. Is there a human in the loop by default? Tools that make final decisions autonomously — auto-grading, auto-flagging, auto-approving — deserve more scrutiny than tools that generate suggestions for a person to review.
5. Who has used it, and what did they find? A quick internal check — has anyone in your organization already piloted this tool? — often surfaces problems before you spend a budget cycle finding them yourself.
6. Does it duplicate something you already have approved? Tool sprawl creates its own risk: more logins, more data footprints, more policies to track. If an approved tool already does 80% of the job, that's often reason enough to skip the new one.
Making This Stick
A checklist only works if it's actually used before adoption, not after. Consider building this into your procurement or IT approval process directly, so it's not left to individual discretion. Five minutes of structured vetting is a small cost compared to walking back a tool that's already embedded in daily workflows and holding data you can no longer easily account for.
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