Across this series, we've made the case that what looks like a scientific productivity problem in biopharma is, in many ways, an operational one. Eroom's Law continues to describe the macro trend (the rising cost and complexity of bringing new therapies to market) but inside organizations, the day-to-day reality tells a more specific story.
Programs are not slowing down because scientists lack ideas or capability. They are slowing down because of everything that surrounds the science: how teams access external partners, how work gets approved, how suppliers are onboarded, and how financial and operational data is managed across systems.
This accumulation of friction is what we've called the Discovery Tax.
And while it rarely appears on a balance sheet, it shows up everywhere that matters: in delayed programs, underutilized scientific talent, and missed opportunities to move faster when timing is critical.
The more important question, then, is not whether the Discovery Tax exists, but what the organizations that are actually reducing it are doing differently. What does it look like, in practice, to treat R&D operations not as a cost center, but as a strategic capability that directly shapes scientific outcomes?
The Discovery Tax doesn't show up on a balance sheet — but it shows up everywhere that matters: delayed programs, underutilized scientists, and missed windows to move fast.In most R&D organizations, operations are still managed as a support function. Procurement is reactive, initiated when a scientist needs something. Supplier qualification happens repeatedly, even when similar work has already been done elsewhere in the organization. Compliance is enforced at the end of a process, and spend visibility is assembled after decisions have already been made. This model persists not because teams are underperforming, but because it was never designed for the scale, complexity, and pace that modern R&D now demands.
The organizations beginning to reverse their Discovery Tax have made a more fundamental shift in how they think about this layer of work. Rather than treating operations as overhead to be contained, they treat it as infrastructure that enables science. This distinction is subtle in language but profound in practice. Infrastructure is something you invest in, standardize, and continuously improve. It is designed to scale across programs, rather than reset with each one, and it is measured by how effectively it accelerates outcomes rather than how efficiently it reduces cost.
Increasingly, this infrastructure is not just digital, but intelligent. It connects workflows, suppliers, and data into a system that actively reduces friction, anticipates needs, and guides execution. This is what we mean by Intelligent Infrastructure for Science: an operational foundation that does not simply support R&D, but actively accelerates it.
When you look across the organizations making this shift, the differences are not about isolated tools or one-off process improvements. They reflect a consistent set of operating principles that compound across every program and every team.
At a practical level, leading R&D organizations are doing four things differently:
These shifts are already playing out in leading organizations.
One top 10 global pharmaceutical company rethought how its HEOR team managed external data suppliers. With 20+ providers and a new onboarding process for every engagement, it often took 6–11 weeks to start a study, while researchers spent significant time on coordination instead of analysis.
By moving to Science Exchange, they unlocked a unified, infrastructure-driven model, standardized supplier qualification, streamlined contracting, and connected workflows. With this, the team removed that friction from the system.
The impact was immediate:
What changed was not the science, it was the system around the science. Procurement shifted from a bottleneck to an enabler, allowing the organization to move at the pace its research demanded.
→ Read the full case study for more detail
When procurement shifts from bottleneck to enabler, R&D teams can finally move at the pace their science demands — not the pace their systems allow.Eroom's Law reflects real structural challenges in drug development. The science is harder, the regulatory bar is higher, and the complexity of bringing new therapies to market continues to increase.
No operational model eliminates those realities.
However, the operational layer around that science is not fixed. The way organizations access external capabilities, initiate work, manage spend, and enforce compliance are all choices. And those choices, when made differently, can return measurable value in the form of faster timelines, better resource utilization, and greater organizational agility.
The organizations leading in this space are applying the same discipline to R&D operations that they have long applied to other critical domains such as manufacturing, clinical trial execution, and quality systems. They are treating it as intelligent infrastructure that actively guides and accelerates work.
The Discovery Tax, in that context, is not a law of nature. It is the result of how systems are designed and how work flows through them. And as the example above demonstrates, it is not only possible to reduce it, it is possible to do so quickly, with impact that compounds across every program and every team.
The organizations that recognize this are building a structural advantage in how they move from idea to insight, from study to decision, and ultimately, from discovery to impact.
→ Calculate your organization's Discovery Tax.