Brief

Why Pharma Transformations Stall—and How to Fix Them

Pricing pressure, AI, and market shifts are converging, and the industry built to never change is suddenly being asked to change fast.

Dale Stafford, Anna Cochemé, Kai Grass, and Fabio La Mola

First published on августа 03, 2026
min read


Резюме Three forces—US pricing pressure, market unbundling, and AI—are emerging simultaneously, and pharma’s legacy operating model isn’t prepared for them. The cost of these operating model gaps used to be manageable. As pressures mount, the primary risk is being able to respond fast enough to disruption. The companies pulling ahead are building change into how they operate, designing strategy, operating model, and technology as one. Pharma hasn’t had to be good at change, and the window to build that muscle is narrowing fast. For 30 years, the economics rewarded companies for moving with caution. Wide US margins meant that even a slow-moving, process-heavy organization generated enough of a return to make wholesale redesign hard to justify. The industry's transformation track record reflects it. Only 11% of healthcare and life sciences transformation programs fully achieve their targets. Another 83% (vs. 75% across all industries) settle for less and land somewhere between halfway and the finish line. That margin protection is now eroding on two fronts at once: US pricing pressure and the unbundling of markets by geography and therapeutic area. Meanwhile, AI is reshaping how work gets done faster than most organizations can absorb. A company that hasn’t built the capacity to change is suddenly being asked to change everything. But the strategy is the easy part. What will separate the companies that pull ahead is a standing muscle for execution, one built to adapt to continuous change. Why old pharma operating models are breaking down Pharma built its operating model for a world that no longer exists. For decades, the route from molecule to patient followed the same logic: one development strategy, one regulatory approach, one commercial playbook adapted at the margins. The Internet, managed care, and the data revolution each should have forced a redesign. Instead, pharma added layers. The result is 30 years without meaningful change to the commercial model and little reason to build a transformation muscle. What makes this moment different is simultaneity. Any one of these forces could be handled through incremental adaptation. Together, they’re far more difficult to manage. Start with pricing. The US umbrella is under unprecedented pressure, and, as it compresses, it’s already affecting launch decisions in other geographies. If that pressure intensifies, pharma lands where retail, consumer goods, and tech have always lived: forced to innovate operationally because the economics demand it. Those industries are better at change precisely because they have never had a choice. Then there's the market itself, unbundling along two dimensions at once: geography and therapeutic area. A swelling pipeline of increasingly tailored therapies, rising disease awareness, and a population that needs ever more care are squeezing payers’ and plan sponsors’ budgets everywhere. Geographies are diverging in how they respond: on pricing, on access, on what evidence they’ll pay for. Therapeutic areas are splitting, too, by how individualized the care is and who controls the treatment decisions. Because of this, companies are faced with a higher number of trade-offs and difficult ROI decisions across R&D, commercialization, patient support, and elsewhere. A company that hasn’t built the capacity to change is suddenly being asked to change everything. In the face of this, AI is advancing in bursts, faster than the readiness of most organizations. The technology is available, and the use cases are proving themselves. Yet, most companies are still running fragmented changes that generate more internal swirl than competitive advantage. In our 2026 survey, 45% of pharma clinical trial sponsors said that they are piloting AI in select functions but haven’t restructured a single role. Pharma has access to AI as well as facing the increased costs of its use, but few companies are operating differently because of it at this stage. Pharma's real risk is exposure These gaps aren’t new, but the cost of them is. Pharma companies articulate sophisticated transformation visions, but they struggle to connect them to market realities and frontline behavior. AI gets declared a priority at the top, but it’s barely discussed at the front line because "the center has it covered." Programs get announced, then absorbed by in-year cost targets. Repeated cycles of change without visible impact breed fatigue, skepticism, and shrinking confidence in leadership. The most telling symptoms can be under the surface. In a typical all-day leadership workshop, for example, a highly paid chief of staff sits with the senior team and spends eight hours taking notes, managing the technology, and keeping the session running. They say almost nothing. It's a sharp illustration of how organizations pull their most capable people toward coordination and away from the work that matters. A company that struggles to adapt won't be surprised by disruption; it will simply be unable to respond fast enough once it arrives. Consider how your organization would respond if: the US pricing umbrella contracted meaningfully, erasing 30 years of the industry’s margin structure; China built an overseas commercial engine as fast as its innovation engine; AI-native competitors reached physicians and patients faster and more precisely than the legacy model could; or the algorithms powering treatment decisions ended up owned by electronic health record companies, payers, and consumer tech. Each of these is survivable but only for companies fast enough to respond. How pharma companies can build transformations that work Seeing these shifts coming won't be what separates the winners; responding to them will. Several moves will set those companies apart. Lead transformation personally. Most pharma executives show up when the strategy is announced and again when results are reviewed. It's the space in between, where initiatives stall and trade-offs get made by default, that decides whether transformation succeeds. Executives who pull ahead treat it as their own work, painting a picture of where they’re taking the organization and why it matters for employees, challenging the incentives propping up the status quo, and staying committed through years of resistance instead of waiting it out on the sidelines. Ownership can't stop at the top either; the managers closest to the work turn direction into behavior, so sponsorship has to reach them, too. Build pull-through discipline. Pharma is good at generating transformation activity: Initiatives get announced, pilots get launched, progress gets reported upward. What rarely follows is the discipline to turn that intent into how people actually work in their day to day, and without it, inertia quietly absorbs change before it reaches the people who have to operate differently. Companies that adapt fast build cultures where bad news travels quickly and "red" is treated as information, not failure. Design strategy, operating models, and technology together. A company built around a few global blockbusters can't suddenly run like a portfolio of differentiated businesses without rethinking how capabilities, authority, accountability, and the technologies that support them are structured. The organizations that get change right treat these as an integrated problem from Day 1, surfacing problems earlier and allowing the model to adapt in a more fragmented, fast-moving market. Deploy AI with intentionality. The real risk is automating yesterday’s work instead of redesigning tomorrow’s. AI doesn’t fix bottlenecks, silos, or broken handoffs; it exposes them faster. Rolled out function by function, it makes isolated teams more productive while the handoffs between them stay broken. The companies getting the most from it organize around outcomes rather than functions and redesign whole workflows instead of speeding up old ones. Consider your enterprise’s capability to and capacity for change. Assess where complexity has long accumulated and where simplification is overdue. This also means treating organizational energy as finite. Transformations fizzle when they drain energy faster than they replenish it, so creating space for change rather than piling it on top of everything else is essential. The strongest companies make this part of their operating rhythm as an evergreen backlog of critical issues, resolving one before adding another. Make decisions at greater velocity, and learn from them. In a regulated industry, rigor is essential, but too often, layers of reviews and escalation turn rigor into organizational drag. The best companies don’t trade speed for quality. They achieve both by matching governance to the value and risk at stake, letting teams make trade-offs without defaulting to escalation or endless rounds of sign-offs. And because not every variable is knowable up front, they use connected data to track whether decisions improve outcomes and course correct continuously. Pharma has spent 30 years proving it can discover what no one else can. The next test is whether it can change as fast as it can invent. The authors would like to acknowledge and thank the broader team that contributed to this point of view, including Kristin Moneyron, Brittany Rodriguez, and Jeremy Reich.

Резюме

Three forces—US pricing pressure, market unbundling, and AI—are emerging simultaneously, and pharma’s legacy operating model isn’t prepared for them.
The cost of these operating model gaps used to be manageable. As pressures mount, the primary risk is being able to respond fast enough to disruption.
The companies pulling ahead are building change into how they operate, designing strategy, operating model, and technology as one.

Pharma hasn’t had to be good at change, and the window to build that muscle is narrowing fast.

For 30 years, the economics rewarded companies for moving with caution. Wide US margins meant that even a slow-moving, process-heavy organization generated enough of a return to make wholesale redesign hard to justify. The industry's transformation track record reflects it. Only 11% of healthcare and life sciences transformation programs fully achieve their targets. Another 83% (vs. 75% across all industries) settle for less and land somewhere between halfway and the finish line.

That margin protection is now eroding on two fronts at once: US pricing pressure and the unbundling of markets by geography and therapeutic area. Meanwhile, AI is reshaping how work gets done faster than most organizations can absorb. A company that hasn’t built the capacity to change is suddenly being asked to change everything. But the strategy is the easy part. What will separate the companies that pull ahead is a standing muscle for execution, one built to adapt to continuous change.

Why old pharma operating models are breaking down

Pharma built its operating model for a world that no longer exists. For decades, the route from molecule to patient followed the same logic: one development strategy, one regulatory approach, one commercial playbook adapted at the margins. The Internet, managed care, and the data revolution each should have forced a redesign. Instead, pharma added layers. The result is 30 years without meaningful change to the commercial model and little reason to build a transformation muscle.

What makes this moment different is simultaneity. Any one of these forces could be handled through incremental adaptation. Together, they’re far more difficult to manage.

Start with pricing. The US umbrella is under unprecedented pressure, and, as it compresses, it’s already affecting launch decisions in other geographies. If that pressure intensifies, pharma lands where retail, consumer goods, and tech have always lived: forced to innovate operationally because the economics demand it. Those industries are better at change precisely because they have never had a choice.

Then there's the market itself, unbundling along two dimensions at once: geography and therapeutic area. A swelling pipeline of increasingly tailored therapies, rising disease awareness, and a population that needs ever more care are squeezing payers’ and plan sponsors’ budgets everywhere. Geographies are diverging in how they respond: on pricing, on access, on what evidence they’ll pay for. Therapeutic areas are splitting, too, by how individualized the care is and who controls the treatment decisions. Because of this, companies are faced with a higher number of trade-offs and difficult ROI decisions across R&D, commercialization, patient support, and elsewhere.

A company that hasn’t built the capacity to change is suddenly being asked to change everything.

In the face of this, AI is advancing in bursts, faster than the readiness of most organizations. The technology is available, and the use cases are proving themselves. Yet, most companies are still running fragmented changes that generate more internal swirl than competitive advantage. In our 2026 survey, 45% of pharma clinical trial sponsors said that they are piloting AI in select functions but haven’t restructured a single role. Pharma has access to AI as well as facing the increased costs of its use, but few companies are operating differently because of it at this stage.

Pharma's real risk is exposure

These gaps aren’t new, but the cost of them is. Pharma companies articulate sophisticated transformation visions, but they struggle to connect them to market realities and frontline behavior. AI gets declared a priority at the top, but it’s barely discussed at the front line because "the center has it covered." Programs get announced, then absorbed by in-year cost targets. Repeated cycles of change without visible impact breed fatigue, skepticism, and shrinking confidence in leadership.

The most telling symptoms can be under the surface. In a typical all-day leadership workshop, for example, a highly paid chief of staff sits with the senior team and spends eight hours taking notes, managing the technology, and keeping the session running. They say almost nothing. It's a sharp illustration of how organizations pull their most capable people toward coordination and away from the work that matters.

A company that struggles to adapt won't be surprised by disruption; it will simply be unable to respond fast enough once it arrives. Consider how your organization would respond if:

the US pricing umbrella contracted meaningfully, erasing 30 years of the industry’s margin structure;
China built an overseas commercial engine as fast as its innovation engine;
AI-native competitors reached physicians and patients faster and more precisely than the legacy model could; or
the algorithms powering treatment decisions ended up owned by electronic health record companies, payers, and consumer tech.

Each of these is survivable but only for companies fast enough to respond.

How pharma companies can build transformations that work

Seeing these shifts coming won't be what separates the winners; responding to them will. Several moves will set those companies apart.

Lead transformation personally. Most pharma executives show up when the strategy is announced and again when results are reviewed. It's the space in between, where initiatives stall and trade-offs get made by default, that decides whether transformation succeeds. Executives who pull ahead treat it as their own work, painting a picture of where they’re taking the organization and why it matters for employees, challenging the incentives propping up the status quo, and staying committed through years of resistance instead of waiting it out on the sidelines. Ownership can't stop at the top either; the managers closest to the work turn direction into behavior, so sponsorship has to reach them, too.
Build pull-through discipline. Pharma is good at generating transformation activity: Initiatives get announced, pilots get launched, progress gets reported upward. What rarely follows is the discipline to turn that intent into how people actually work in their day to day, and without it, inertia quietly absorbs change before it reaches the people who have to operate differently. Companies that adapt fast build cultures where bad news travels quickly and "red" is treated as information, not failure.
Design strategy, operating models, and technology together. A company built around a few global blockbusters can't suddenly run like a portfolio of differentiated businesses without rethinking how capabilities, authority, accountability, and the technologies that support them are structured. The organizations that get change right treat these as an integrated problem from Day 1, surfacing problems earlier and allowing the model to adapt in a more fragmented, fast-moving market.
Deploy AI with intentionality. The real risk is automating yesterday’s work instead of redesigning tomorrow’s. AI doesn’t fix bottlenecks, silos, or broken handoffs; it exposes them faster. Rolled out function by function, it makes isolated teams more productive while the handoffs between them stay broken. The companies getting the most from it organize around outcomes rather than functions and redesign whole workflows instead of speeding up old ones.
Consider your enterprise’s capability to and capacity for change. Assess where complexity has long accumulated and where simplification is overdue. This also means treating organizational energy as finite. Transformations fizzle when they drain energy faster than they replenish it, so creating space for change rather than piling it on top of everything else is essential. The strongest companies make this part of their operating rhythm as an evergreen backlog of critical issues, resolving one before adding another.
Make decisions at greater velocity, and learn from them. In a regulated industry, rigor is essential, but too often, layers of reviews and escalation turn rigor into organizational drag. The best companies don’t trade speed for quality. They achieve both by matching governance to the value and risk at stake, letting teams make trade-offs without defaulting to escalation or endless rounds of sign-offs. And because not every variable is knowable up front, they use connected data to track whether decisions improve outcomes and course correct continuously.

Pharma has spent 30 years proving it can discover what no one else can. The next test is whether it can change as fast as it can invent.

The authors would like to acknowledge and thank the broader team that contributed to this point of view, including Kristin Moneyron, Brittany Rodriguez, and Jeremy Reich.