Commercial pharma doesn’t have an AI problem. It has an adoption problem.
It’s no longer a question of whether commercial pharma organizations will adopt AI. Most already have.
Across targeting, forecasting, access strategy and field effectiveness, many teams are running pilots or using production tools. And yet, when commercial leaders step back and look at outcomes, the results are uneven.
Some initiatives deliver localized wins. Many generate insight. Fewer materially change how decisions are made or how teams operate. Over time, the gap between ambition and impact has become difficult to ignore. This disconnect rarely shows up as failure; more often, it surfaces as quiet frustration:
“The insights are interesting, but…”
“We’re not ready to scale this yet.”
“It hasn’t really changed how the field operates.”
Over time, momentum fades — not because the work was misguided, but because it never fully integrated into how commercial organizations function. When AI initiatives stall, the instinct is often to look for technical explanations. But across organizations with very different levels of analytical maturity, similar patterns emerge. The issue isn’t whether AI works. It’s whether organizations are set up to use it.
Organizations need to be set up to implement AI properly
Commercial organizations are built for stability. Roles are defined. Planning cycles are fixed. Incentives reinforce established behavior. AI introduces a different dynamic: new signals, shifting priorities, faster feedback. Without deliberate changes to how decisions are made and owned, these two realities coexist without ever fully connecting.
As a result, AI outputs are often reviewed alongside existing reports rather than embedded into execution. They inform discussion without driving action — and over time, become optional. This is why so many initiatives plateau after the pilot phase. The organization absorbs the technology without changing how it operates.
It’s also why timing matters. The decisions that matter before launch are not the same ones that matter during early growth, expansion or as loss of exclusivity approaches. When AI feels disconnected from the commercial moment, adoption suffers — regardless of technical quality.
The next phase of AI value in commercial pharma will not be defined by more sophisticated models alone. It will be defined by organizations that learn how to operationalize what they already have.
Scaling AI beyond the pilot requires clearer prioritization, lifecycle awareness and an intentional focus on adoption — not just analytics. For teams willing to address those challenges directly, the opportunity is real.
For more information on where pilots begin to break down, read our deep dive on why AI fails and what organizations should consider before starting any pilot.