Rahul Gupta authored this article

Key takeaways

• Most emerging biopharma launches miss expectations not because the science fails, but because companies underestimate commercial friction.
• The gap between eligible patients and treated patients is operational, not clinical—and it is rarely modeled with the same rigor as demand.
• The biggest gaps emerge across payer access, physician adoption, patient activation and execution capacity.
• Companies that outperform operationalize earlier, model friction explicitly and deploy resources with greater precision.

Regulatory approval validates the science, but it does not validate the commercial pathway—yet many emerging biopharma companies forecast launches as though clinical demand alone determines commercial success. Most forecasting errors stem less from patient estimates than from underestimating the operational distance between prescription intent and therapy initiation.

That distance is friction—and increasingly it determines launch performance.

Most forecasts are built around epidemiology, diagnosed patients and adoption curves informed by clinical analogs. But emerging biopharma companies often treat commercialization as a linear progression from approval to adoption. It is not. Commercialization is ultimately a series of conversion points, and friction at each stage compounds downstream.

ZS analysis shows that only ~60% of recent launches meet or exceed initial expectations. Among those that miss, analyst estimates are reduced by ~40% in Year 1 and ~60% by Year 2. For emerging pharma leadership teams, the implication is direct: Launch underperformance is far more often an execution and conversion problem than a demand problem.

How do emerging biopharma companies overestimate coverage conversion?

Most forecasts are built around patient eligibility. But coverage doesn’t necessarily equal utilization.

Prior authorization, step edits and utilization management are now standard across specialty categories. Payers increasingly expect comparative value data and real-world evidence alongside clinical efficacy. When evidence packages are incomplete or misaligned, access friction intensifies—and emerging biopharma companies often face this challenge while building reimbursement infrastructure and hub capabilities for the first time.

The result is that conversion from coverage to therapy initiation is slower than forecast assumptions imply. Companies that operationalize access early create greater launch stability during the first 12 months, when investor expectations are formed.

What leaders should do

Leaders should act early and deliberately across three areas:

• Build payer evidence strategies in Phase 3
• Develop hub infrastructure before launch
• Model access as a staged conversion process, not a binary coverage milestone

If they don’t?

Scripts are written but not filled. Early launch momentum weakens before infrastructure matures, forcing organizations into reactive remediation rather than strategic execution.

Why doesn’t physician interest translate into early prescribing?

Physician interest does not translate into early prescribing when clinical uncertainty, reimbursement complexity and reputational risk delay action.

Market research captures what physicians say they may do. Prescribing data reflects what they actually do.

Prescribing a new therapy introduces clinical uncertainty, reimbursement complexity and reputational risk. Many physicians wait, not because they reject the therapy but because the perceived cost of being early exceeds the perceived cost of delay. Forecasts model total prescriber opportunity; real adoption concentrates within a much smaller segment of high-conviction early adopters.

ZS analysis across 92 launches shows only 13% of brands meet or exceed expectations in each of their first three years. Those that do are differentiated not by reach, but by prescriber retention and repeat utilization among early adopters. The key variable is not how many physicians could prescribe; it is how quickly high-propensity prescribers convert into sustained users.

What leaders should do

Leaders should focus early effort on the physicians most likely to convert and stay active. They can do this by:

• Segmenting by adoption propensity, not prescribing volume
• Concentrating early engagement on high-conviction converters
• Investing in peer validation and real-world evidence to reduce prescribing uncertainty

If they don’t?

Commercial investment disperses across low-converting prescribers while early adoption stalls during the period when analyst and investor attention is highest.

The operational bottleneck most launch forecasts miss

Even when physicians prescribe and payers approve, therapy initiation is rarely immediate. Delays in hub services, specialty pharmacy coordination and patient navigation suppress therapy starts during the most commercially important phase of launch.

In rare and specialty disease, patients are also harder to identify and activate. Diagnosed patients often represent only a fraction of the addressable population, with inconsistent referral pathways and long diagnosis timelines. In specialty and rare disease, demand is not discovered passively; it is operationally activated.

Companies that outperform treat patient identification as a funded commercial capability, investing early in referral networks, diagnostic acceleration, disease education and ecosystem partnerships.

What leaders should do

Leaders should strengthen launch readiness in three operational areas before volume builds by:

• Auditing hub readiness six months prelaunch
• Building patient identification into launch planning explicitly
• Investing early in referral and diagnostic acceleration pathways

If they don’t?

Prescription leakage compounds through the first two quarters postlaunch. Patients prescribed therapy never start it, and modeled demand disappears into operational gaps before it becomes realized revenue.

Limited commercial capacity magnifies launch friction

Emerging biopharma companies do not fail because they lack scale. They fail when limited scale is deployed without precision.

Smaller field teams, narrower account coverage and concentrated commercial investment reduce the ability to accelerate prescriber conversion and resolve access barriers quickly. The challenge is not simply reach. It is the ability to convert adoption efficiently with limited commercial capacity. Forecasts that assume rapid adoption without accounting for execution limitations consistently overstate launch velocity.

What leaders should do

Leaders should allocate limited resources where they will remove the most friction. They can do this by:

• Applying large-pharma benchmarks only after adjusting for field force size and share of voice
• Sequencing resources against the highest-friction accounts—typically those generating disproportionate early volume
• Prioritizing sustained engagement over broad awareness

If they don’t?

Commercial teams spread limited resources too broadly, friction compounds in the highest-value accounts, and launch trajectories flatten before adoption reaches scale.

The real forecasting gap is operational

Most emerging biopharma companies believe launch risk is primarily scientific or regulatory. Increasingly, it is operational.

Companies that fail to close this gap face a familiar pattern: slower adoption, downward analyst revisions and a narrowing window to reset commercial strategy before investor confidence erodes.

The companies that outperform are not simply forecasting demand more accurately. They are explicitly modeling friction, and investing early in the capabilities required to reduce it.

Friction is not a launch risk that resolves itself. It requires deliberate investment, early action and forecasts built around what commercialization actually demands, not simply what approval makes possible. In today's launch environment, precision is no longer conservative. It is the price of commercial credibility.