THE SHORT ANSWER
Most enterprise AI programs cannot say what they will change or by when. The metrics that prove one in R&D are not AI metrics at all. They are R&D metrics: cycle time from data to decision, the share of results that can be rerun without their author, and the time it takes to answer an audit question.
The old question is retired
Two years ago, nearly every leadership conversation I was in started the same way: do you have an AI strategy? That question has been answered everywhere, usually more than once. The one that replaced it is harder. What does AI change, and by when?
Does it shorten the path to a candidate? Does it improve the quality of a go or no-go decision? Does it reduce the rework nobody budgets for? Or is it a feature?
The number behind the shift
MIT NANDA found that 95% of organizations saw no measurable P&L impact from an estimated $30 to $40 billion in enterprise GenAI spend.
Pharma is not exempt, and the R&D leaders I talk with know it. The internal conversation has moved from ambition to accounting. I read that as healthy, not pessimistic.
The budget asymmetry nobody names
Here is the uncomfortable part. Most AI budgets fund what the model gives you. Almost nothing funds what makes the answer worth trusting.
Most AI budgets fund what the model gives you. Almost nothing funds what makes the answer worth trusting.
That asymmetry is why a well-funded program can produce a technically correct output nobody will act on. The money went to the half of the problem that demos well.
The metrics that prove an AI program are not AI metrics
They are R&D metrics. Cycle time from data to decision. The share of results that can be rerun without the original author. Time to answer an audit question. AI either moves those numbers or it does not, and all three can be measured this quarter without a new dashboard.
The question I now hear from finance, in one form or another: if this were a drug program, what would the readout be? Most AI proposals do not have an answer. The ones that do tend to be the proposals worth funding.
The question to take into your next AI review
If your AI program were a clinical program, what is its primary endpoint?
If naming it is difficult, the program may still be worth running. It is not yet worth defending, and at some point this year someone will ask you to defend it.
Sources
MIT NANDA, “The GenAI Divide: State of AI in Business 2025,” July 2025.
Coverage: Forbes, 26 Aug 2025.