Ask ten people in pharma where AI is having an impact and most will point to drug discovery. But the less visible it is the shift happening on the commercial side: pricing, contracting, rebates, and market access that determines whether an approved medicine actually reaches patients at a viable price.
The complexity of the industry with regulation, country-specific rules and processes that were built for a slower, more manual era leave a fertile ground for AI: the problems are structured, the data exists and the cost of error is high enough to justify investment.
From reactive to predictive
The traditional model in commercial operations is reactive: a rebate gets calculated after a sale happens, a contract gets renegotiated after a compliance issue surfaces, a pricing anomaly gets caught during an audit. AI is starting to shift that timeline earlier. Predictive models can flag a contract likely to breach its terms before it does. Anomaly detection can catch a pricing error before it compounds across thousands of transactions. The value is not replacing human judgment but it is giving the humans making these calls more time to act before the damage is done.
The trust problem
The honest constraint is not technical capability, it is trust. A pricing decision that is wrong by a fraction of a percentage point can mean a contractual breach, a regulatory inquiry or a damaged relationship with a key account. That is a different risk profile from a recommendation engine getting a product suggestion wrong. So the pattern that is actually working in practice is not full automation, it is AI doing the heavy computational lifting, with a human still accountable for the final sign-off.
That may sound like a modest ambition compared to the more dramatic AI narratives elsewhere in the industry. But in a function where the two ends of transformation are massive efficiency gains and equally massive compliance exposure, modest and well-governed is usually the right pace.
Article written and edited by the author with the support of Claude (Anthropic).