Before the Board Approves an AI Initiative: The Six Questions

AI proposals arrive at boards in a standard costume: transformative potential, a vendor demo that worked beautifully, and a price that seems modest against the promise. The board is asked to approve something it cannot fully evaluate, on the strength of a presentation designed to be unevaluable.

Directors do not need to understand the model. They need to understand the decision the tool will improve, the data it will touch, and what happens when it is wrong. Six questions, asked consistently of every AI initiative, get there.

None of them are technical. All of them are hard to answer with a demo.

Key takeaways

• Ask what decision or task improves, and how anyone will know.

• Ask what data the tool touches and where outputs go.

• Ask who checks the output and what a wrong answer costs.

• Ask how the company exits: data out, contract out, process out.

• Ask for the answers in writing; verbal fluency is not governance.

The six questions

What decision or task does this actually improve?

'Efficiency' and 'insights' are not answers. A fundable proposal names the work: the proposal drafting that takes half the time, the support queue that resolves faster, the analysis that currently never gets done at all.

If the sponsor cannot name the task, the initiative is a technology in search of a budget. Send it back.

How will we know it worked?

Every AI initiative should carry a measure set before launch: the baseline, the target, and the date of the honest look. Without it, the initiative cannot fail, which means it cannot be managed.

Ask for the measure in the approval paper, not as a follow-up. A proposal without a success test is asking for a permanent license.

What data does it touch, and where do the outputs go?

The answer should name the data categories, whether any leave your environment, and who can see the prompts and outputs. Customer data, personal data and unpublished financials each raise the bar.

NIST's AI guidance puts data mapping at the front of AI risk work for a reason: you cannot assess the exposure of a system whose inputs you have not listed.

What happens when it is wrong, and who checks?

AI systems are confidently wrong some of the time. The question is whether the use case tolerates that: an internal draft with a human editor absorbs errors; an unreviewed customer-facing answer does not.

Ask who reviews output, at what point, and what the worst plausible wrong answer costs. The answer separates a sensible deployment from a future apology.

Whose job is this after launch?

AI initiatives fail quietly when the launch team disbands and nobody owns the system: its drift, its vendor changes, its growing list of unreviewed exceptions.

One named owner after launch, with the authority to suspend the system if the answers to the other questions change. No owner, no approval.

How do we get out?

Vendors change terms, models get retired, better options appear. Ask how the company exits: whether your data and configurations come out in usable form, what the contract's exit terms are, and whether the process the tool runs could be moved.

A good answer makes the initiative safer to approve, not harder. The inability to answer is the warning.

The related discussion of building trustworthy AI is worth your time alongside this one.

Frequently asked questions

Should the board approve every AI tool?

No. The board approves initiatives of consequence - customer-facing systems, material spend, sensitive data. The six questions work just as well for the management tier that approves everything smaller.

What if the answers are 'we do not know yet'?

Then the proposal is a pilot and should be approved as one: small scope, real data limits, and a date when the six questions get answered for real. Pilots are fine; unlabeled pilots are not.

Do these questions slow innovation?

They slow unexamined spending. Teams that can answer six direct questions ship faster, because they stop relitigating the same concerns at every stage.

A Defensible Next Step

Put these six questions in front of the next AI proposal that reaches you. The quality of the answers will tell you more than the demo did. Book a Decision-Clarity Call if you want them tailored to your portfolio.