How to prioritise AI projects

AI budgets are finite, but AI ideas aren’t. The question is never “is this a good idea?” Almost every pitch is. The real question is “is this a better bet than everything else competing for the same capital?” Teams that skip that comparison fund the loudest champion, not the highest-value opportunity.

The cost of getting this wrong is well documented. MIT’s NANDA initiative found 95% of enterprise generative-AI pilots produce no measurable P&L impact, and RAND puts the AI project failure rate above 80%, twice that of non-AI IT. Prioritization is the cheapest place to avoid that waste, before the build budget is committed.

Comparability is the point. Score each candidate on the same five dimensions, using a simple 1–5 scale, so a customer-service agent can be weighed against a document-processing workflow:

Weight the five criteria to your context. Most companies weight business value and strength of evidence highest. Combine the scores into one ranked list. That single view is the portfolio: every AI bet, ordered by how much value the evidence says it will create per dollar.

Funding then flows top-down until the budget is spent. Bets below the line aren’t rejected forever. They are held until they earn more evidence or the portfolio is re-scored.

Scoring gets you a ranked list, but the bets clustered around the funding line are where the real money is made or lost. They usually differ on one thing: the strength of evidence. That is the criterion you can cheaply improve.

Pretotyping, created at Google and taught at Stanford, is how you improve it fast. A Fake Door test, a Mechanical Turk run, or a Concierge trial produces real behavioural evidence in days rather than quarters. Run one on each close call and the tie breaks itself on data, not debate.

Prioritization only holds if the rules are agreed before the scores come in. Decide in advance the score and evidence a bet needs to be funded, sent back for more proof, or stopped. Pre-committed thresholds turn a hard political conversation into a routine decision the board can defend.

This is the discipline Exponentially installs inside a Rapid Experimentation Operating Model: map the portfolio, rank the bets, pretotype the highest-stakes assumptions, and decide what to fund, fix, or kill.