By Christian Hayden, Opus Strategy
Precision medicine has moved from scientific ambition to commercial reality, but for many organizations, the gap between promise and practice remains wide. Despite rapid scientific progress and billions of dollars in investment, precision medicine has delivered uneven results beyond select successes, particularly in oncology. Many biopharma companies continue to struggle to operationalize and commercialize precision-driven innovation at scale. It is tempting to blame the science, but in practice, the limiting factor is more often execution.
What problem is precision medicine actually solving?
At its core, precision medicine is an operating upgrade to the traditional biopharma model: instead of treating the average patient with the average disease, it aims to match the right intervention to the right biology at the right time. Done well, it can address four structural constraints that have historically limited both R&D productivity and commercial impact.
Where is precision medicine most ready to scale?
Precision medicine is most operationalized in oncology and select genetically defined diseases, with growing (but uneven) progress in neurology and mental health.
Oncology remains the most mature environment for precision approaches due to established regulatory precedent, reimbursement pull, and the routine use of biomarker testing in clinical workflows. Biomarker testing for EGFR, ALK, BRAF, KRAS, and HER2, among others, increasingly guides targeted therapy selection across tumor types. Yet even here, scaling remains constrained by workflow integration, testing logistics, and the difficulty of linking data across fragmented systems.
Outside oncology, the path to scale is slower even when the underlying science is compelling. In neurology, clinical development is often constrained by heterogeneous presentations, long timelines, and endpoint ambiguity. In mental health care, there is enormous unmet need and rising interest in digital phenotyping and response prediction. However, clinical validation standards, implementation pathways, and reimbursement models remain immature.
Across these domains, the pattern is consistent: progress correlates less with quality of innovation and more with organizational readiness, evidence strategy, and workflow integration. In many cases, the science is moving faster than operating models.
How investors and innovators can course correct
Organizations attempting to move from promise to practice repeatedly encounter the same execution bottlenecks. In our work across the ecosystem, we see seven recurring organizational gaps:
- No clear capability owner accountable for prioritization, reuse, and scalability
- Inadequate focus on HCP adoption of companion diagnostics and workflow barriers
- A persistent asset-by-asset execution mindset
- Limited cross-functional integration across R&D, clinical, medical, commercial, and data teams
- Underdeveloped evidence and value translation strategies
- Unrealistic timeline expectations
- Talent gaps in program leadership and orchestration
These gaps appear not only in the clinic but also post-launch, where precision assets often require ongoing evidence generation, stakeholder alignment, and operational rigor to sustain adoption.
Recent therapeutic examples illustrate how quickly execution risk becomes enterprise risk. When Applied Therapeutics’ govorestat failed to secure FDA approval due to deficiencies in its clinical evidence package, the company’s valuation reset and external confidence eroded. The lesson? In rare disease, regulatory strategy and data execution are not downstream considerations. Sarepta’s Elevidys gene therapy for Duchenne muscular dystrophy reinforces the point from another angle: severe safety concerns prompted revised labeling and narrowed indications, shifting the narrative from clinical promise to governance, monitoring, and stakeholder trust.
The operating model changes required to scale
These risks begin to fade when companies treat precision medicine as an enterprise capability, not a set of one-off programs. Core operating model changes include:
- Moving from an asset-centric approach to a capability-centric strategy, building reusable playbooks across portfolios
- Designing for HCP adoption, including test logistics, ordering pathways, turnaround times, and patient access
- Making data usable for decisions, including fit-for-purpose RWD, prospective/retrospective collection aligned to decision points, and appropriate use of external controls
- Investing in reusable evidence and analytics platforms, built for longitudinal reuse rather than one-off pilots
- Establishing cross-functional ownership earlier, recognizing that precision programs require aligned decisions upstream
- Integrating medical and commercial teams early, ensuring efforts are co-owned beyond R&D and innovation groups
Why now?
The urgency is rising, and with it the opportunity. Precision medicine’s operational barriers persist even as market tailwinds strengthen:
- Broader availability and linkage of real-world data (RWD) across care settings
- Increasing regulatory openness to RWE, external controls, and pragmatic designs
- AI-enabled approaches that improve phenotype extraction, signal detection, and data harmonization
- Rising payer and provider expectations for measurable outcomes and value demonstration
Capitalizing on these trends requires more than better science. It requires an operating model that integrates evidence, diagnostics adoption, and cross-functional execution into a repeatable system. At Opus Strategy, LLC we help biopharma leaders build capabilities that allow precision medicine to scale across portfolios, not just succeed asset by asset.
For organizations willing to treat precision medicine as a scalable operating model rather than a series of one-off bets, the opportunity is substantial. The future belongs to those who can turn precision from promise into repeatable practice.