Why biopharma launch performance depends on stronger operating systems
For many biopharma launch teams, readiness can look convincing from the inside. Milestones are tracked, dashboards are reviewed, cross-functional meetings are happening and the launch plan is thick, detailed and carefully managed.
Then the product reaches the market, and the real questions begin.
Are healthcare professionals confident enough to try it? Can patients start therapy without unnecessary delay? Are access barriers showing up where the team expected them? Are field teams hearing the same issues that patient services, medical affairs and market access teams are seeing? Is the organization learning fast enough to change course before the launch trajectory hardens?
That is the risk hiding inside many launch operating systems. They can create the appearance of control while still moving too slowly, too generically and too far from the customer barriers that shape adoption.
In a recent webinar, ZS launch experts Komal Gurnani and Andrew Singley explored how biopharma companies can rethink launch for a more complex decade ahead. The discussion covered ZS’s analysis of 340 launches from 2008 to 2025, the market archetypes that will shape future launch strategy and the role AI can play in helping teams sense, decide and act faster.
This article recaps the themes of that conversation. To get the full discussion, watch the webinar here.
Key takeaways
- Biopharma companies are facing a major launch wave, with more entering unfamiliar markets and highly competitive categories.
- A one-size-fits-all launch plan won’t work, as adoption risks differ by market, stakeholder influence and system pressure.
- Clinical differentiation matters, but launch success increasingly depends on how well manufacturers remove the clinical, operational and financial barriers that slow adoption.
- AI can create value when it helps teams improve launch decisions end-to-end, not just automate existing checklists.
The next launch wave will test old launch models
Biopharma is entering a period when launch volume and launch complexity are rising together. Across top pharmaceutical companies, more than 350 new molecular entities may launch by 2030. Many will enter markets that are unfamiliar to the companies launching them. A large share, about 75%, will be concentrated in oncology, cardiovascular, immunology and neurology.
That matters because launch complexity is no longer defined only by the product profile or order of entry. It also depends on the market situation around the asset: Who drives adoption? How could adoption fail? Is the risk concentrated in pockets of customers, or could it spread across the system?
ZS’s launch adoption matrix frames this challenge through four archetypes: managed uptake, market contest, adoption governance and economic gatekeeping.
In managed uptake markets, adoption is largely moderated by healthcare professionals, payers, providers and office staff. The risk of failure is more contained, so the launch question is how to launch efficiently without overengineering or overinvesting against relatively small pockets of risk.
In market contest launches, competition is intense and share battles are won at the margins. The question is how to compete effectively without letting spending grow faster than value. Here, real-time switching analytics and faster customer insight can become a source of advantage.
In adoption governance launches, demand can become the problem. Patient voice is stronger. Budgets can be large. And broad adoption may create pressure on supply, affordability or the healthcare system itself. The question is how to manage success before it creates backlash. Many cardiometabolic and neurological launches may fall in this category.
In economic gatekeeping launches, the asset may be highly differentiated, but access constraints are significant. These launches often need evidence strong enough to withstand payer, policy and financial scrutiny. The question here is how to secure access and protect value so the innovation reaches the patients who need it. Think of advanced cell and gene therapies or highly specialized oncology assets as examples of this category.
The point is simple: Different markets need different launch operating systems. When companies treat every launch like a variation of the same checklist, they risk solving the wrong problem.
The ZS launch adoption matrix helps explain why performance diverges even when products look similar on paper. Different archetypes create different barrier patterns and ways launches can fail, which show up clearly in launch results.
Clinical differentiation is not enough
Launch teams naturally focus on the product. Scientific innovation, clinical evidence and unmet need are central to launch strategy.
But they are not the whole story.
In ZS’s launch performance study, 31% of launches underperformed analyst expectations, falling below 80% of consensus. About a quarter met expectations and 44% overperformed. The data showed that success was not explained by traditional factors alone, such as clinical differentiation or order of entry.
The reason is familiar to anyone close to launch execution. A product can be clinically compelling and still run into practical barriers that slow adoption. A healthcare professional may believe in the mechanism but lack confidence in patient identification. A patient may receive a prescription but stall because of prior authorization, affordability concerns or unclear next steps. A payer may recognize the value but still require evidence and process changes before access expands.
ZS’s 2025 study of 690 healthcare professionals across nine specialties found a similar pattern. When asked what drives their experience with pharmaceutical manufacturers, healthcare professionals pointed first to the product. But when ZS examined what actually changed behavior, the product’s role dropped by 24 points, while factors such as people, support services and reputation became more important.
That is the differentiation paradox. The more differentiated an asset appears, the easier it can be for organizations to underinvest in the system around it. Teams may believe the product will carry the launch, but customers don’t adopt a therapy in a vacuum. They adopt it through workflows, access pathways, support programs, beliefs, habits and constraints.
The launch operating system has to address all of that.
Manufacturer commitment is the new launch advantage
The webinar framed one of the strongest predictors of launch success as manufacturer commitment, which is the organization’s ability to understand and remove the barriers that stand between a customer’s intent and a patient’s ability to start and stay on therapy.
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Those barriers can be clinical, operational or financial. They can include limited disease understanding, uncertainty about the mechanism, workflow hurdles, complex prior authorization, formulary delays, affordability concerns or unclear resource deployment. Individually, each barrier may look manageable, but together, they can define the launch trajectory.
ZS’s Launch Commitment Signal diagnostic focuses on three elements that help predict launch forecast risk.
The first is service density. This is the depth of a manufacturer’s understanding of customer barriers and the strength of the services, solutions or people assigned to address them. The question is not just whether the team has a support model, but whether the model is built around the barriers that matter most.
The second is orchestration velocity. Teams need to detect signals in the market, direct resources to the right customers and act at the right level, from individual offices to accounts, regions and the national market.
The third is trust and equity. Customers need consistent support, not one-off efforts. Trust builds when manufacturers deliver reliably, respond to issues quickly and make it easier for customers to help patients.
The scale of the opportunity is significant. ZS has mapped 188 launch barriers, and brands typically face 25 to 65 of them within the first six months of launch. In many cases, only about a third have meaningful interventions. That gap is where launch value can disappear. It is also where smarter launch design can create advantage.
AI should change launch decisions, not just tasks
AI capabilities are already changing how biopharma teams work. In the context of launch, AI is most powerful when it amplifies manufacturer commitment by turning barrier insights into faster, better decisions rather than just automating today’s workflows. The webinar emphasized that AI creates real value when it changes the quality, speed and consistency of decisions, not when it merely makes today’s slow processes faster.
Many organizations are still in the first horizon of AI adoption. They are applying AI to personas or processes, using it to summarize information, generate content, automate pieces of workflows or help teams complete existing tasks faster. This can improve productivity, but it may not change the launch operating model in a meaningful way.
The second horizon is more consequential. Static planning gives way to continuous decision flywheels. Teams use real-time sensing, modeling and action loops to bring forward the places where human judgment matters most. AI helps leaders see the market more clearly, evaluate trade-offs faster and act before issues become entrenched.
The third horizon is the broader ecosystem shift. As customers adopt AI, the healthcare ecosystem itself will change. New data assets, partnership models and customer expectations will emerge. Companies that understand how customers are using AI and where new barriers are forming will be better positioned to create a durable launch advantage.
The lesson for launch leaders is to move beyond simply adding AI to launch excellence. It is to ask where launch decisions are too slow, too disconnected or too dependent on manual interpretation, then redesign those decisions around better signals and faster action.
Build decision flywheels, not launch checklists
Launch management often depends on granular decisions made during the first six to 12 months. This period can define whether a product’s trajectory accelerates, stalls or falls behind expectations.
Today, too many launch decision cycles are manual. Teams gather data periodically. Analyses are prepared for leadership meetings. Leaders debate trade-offs with incomplete information. Decisions are captured in slides. Actions follow later. Outcomes are reviewed inconsistently. Much of the learning stays in people’s heads instead of flowing back into the system.
AI creates an opportunity to redesign that rhythm:
- A stronger launch decision flywheel starts with sensing. The organization continuously captures signals from the field, medical affairs, patient services, access teams, market research, customer interactions and other sources.
- It then moves to modeling. Teams assess what those signals mean, where barriers may be emerging and whether the risk is contained or broader across the system.
- Next comes decision-making. Leaders evaluate scenarios with better evidence, clearer trade-offs and a more current understanding of customer behavior.
- Then the organization acts. Recommendations move through governed workflows, connected teams and approved channels.
- Finally, the system learns. Outcomes feed back into the model so the next cycle is smarter than the last.
This is how launch momentum compounds through a system that shortens the distance between signal, decision and action.
How biopharma leaders can start
The path to a stronger launch operating system doesn’t require changing everything at once. It starts with a few practical shifts.
First, plan by archetype. A managed uptake launch, a market contest launch, an adoption governance launch and an economic gatekeeping launch should not have the same investment logic or operating model. Leaders should define the market situation early and align the launch system to the risks that matter most.
Second, diagnose customer barriers before launch. Too many teams wait for the market to reveal friction after launch. A better approach is to use cross-functional customer barrier assessment two to three years before launch, when teams still have time to shape engagement, evidence, services and resource allocation.
Third, engage with customers before launch. Prelaunch action should do more than prepare messaging. It should help teams understand where customer conviction is strong, where it is fragile and which barriers are likely to slow adoption. Medical, market access and other customer-facing teams can play a critical role in bringing those signals back into launch planning.
Fourth, invest where adoption risk lives. The issue is not always the size of the launch budget. Sometimes the budget is large but pointed at the wrong problems. Leaders need to move investment toward the barriers, capabilities and decisions most likely to change customer behavior.
Finally, use AI to connect the system. AI should help teams integrate market signals, prelaunch engagement and organizational intelligence into decisions that improve day-one readiness. It should make the launch operating system more responsive, not just more automated.
Taken together, these shifts move launch teams from static planning and generic checklists to systems that see barriers earlier, act faster and compound momentum in the first six to 12 months.
The leadership question is direct: Is your launch operating system creating value, or is it creating risk?
For biopharma companies, that question can become a useful filter for strategy, investment and AI transformation. Customers don’t experience a launch plan. They experience whether the company understands their barriers, responds to their needs and helps patients move from possibility to treatment.
A better launch operating system starts there.
