The three questions every pharma executive should ask before approving an AI budget

Most large AI investments in pharma get approved on conviction rather than diagnosis. The result is that significant budgets are committed before the questions that determine success have been answered. There are three of them, and they are surprisingly simple.

An AI investment decision in a pharma organisation often looks the same as any other capital decision. A business case is built. A vendor is selected or shortlisted. A budget is approved. A programme is launched. Six to nine months later, the executive team is asked to make a follow-on decision, and the conversation suddenly becomes complicated. The metrics that mattered before launch are not the ones being reported now. The scope has shifted. The original promise feels distant.

The reason is not that the original case was wrong, or that the technology underperformed. It is that three foundational questions never got asked at the point of approval. When those questions remain unanswered, the investment proceeds on momentum rather than direction, and the programme ends up in a place that resembles success without quite reaching it.

The three questions every pharma executive should ask. Every time.

Question one: what specific decision will this AI improve?

The most common framing problem in AI investments is generality. AI is described as something that will improve a function, accelerate a workflow, enhance a capability. None of those are decisions. They are categories of activity inside which dozens of decisions get made every day, and AI improves outcomes only when it changes one of them.

The clearer the decision, the clearer the model, the data, and the measurement. A model that helps medical affairs prioritise which clinical questions to escalate is a specific decision support tool with a specific accuracy target. A model that “enhances medical affairs” is an aspiration with no target.

What to ask in the room

If you are approving an AI investment, the question to put on the table is: “name the single most important decision this AI is meant to change, and tell me how we will know in six months whether it has changed it.” If the answer takes more than two sentences, the case is not yet ready.

Question two: what is the cost of being wrong, and who is accountable?

Every model is wrong sometimes. In low-stakes environments that is a tolerable property. In regulated industries it is the central design problem. A pharma AI system that misclassifies an adverse event, mis-prioritises a clinical signal, or mis-recommends a market access decision has consequences that go well beyond a missed forecast.

Yet AI investment approvals routinely happen without an explicit answer to two related questions. How wrong is acceptable, on what dimensions, and who has authority to make that call. And when the model is wrong in production, who is accountable, what is the escalation path, and what is the timeline to correct.

What to ask in the room

“If this model makes a wrong recommendation in production six months from now, who owns the consequence, what is the escalation, and how do we know we are inside or outside acceptable error rates.”

Question three: what needs to change in our operating model?

The third question is the one most likely to be deferred. AI investments tend to be discussed as if they will arrive as an additional capability into a stable operating model. In practice they almost never work that way. The whole point of an AI deployment is to change how decisions are made, and that change does not happen unless the operating model around it changes too.

What to ask in the room

“What about the operating model needs to change for this AI to deliver its value, and is the leadership team willing to make those changes.” If the operating model changes are still notional, the AI investment is funding optionality rather than outcomes.

Why these three are different from the usual questions

Most AI investment reviews focus on three other questions. What is the technology, how much does it cost, and what is the ROI. Those questions matter, but they are downstream of the three above. A model with strong economics and weak decision-level definition will not produce the projected ROI. A model with sophisticated technology and unclear accountability will create more risk than value. A model with credible economics and unwilling operating-model change will be approved and deployed and underused.

The three questions above are not technical questions. They are governance questions. They protect the investment from the predictable patterns of AI programme failure, and they force the organisation to confront the changes the investment is actually asking it to make. They take time to answer. That time is the work that distinguishes serious AI investments from expensive experiments.

The pharma organisations that have got AI to work in production tend to have one thing in common, and it is rarely the sophistication of their technology. It is the rigour of the questions they asked before any technology was selected.