Full Decision Memo
Why unprecedented AI investment in biopharma has not reduced cost, time, or failure — and what leadership judgment is missing.
Prepared for executive leadership and venture capital
Biopharma has absorbed billions of dollars in AI investment, platform partnerships, and computational capability.
Despite this, aggregate R&D cost, development timelines, and late-stage failure rates remain largely unchanged.
This memo argues that the limiting factor is no longer technology or data availability, but leadership judgment under conditions of irreducible uncertainty.
Industry-wide, the total cost and duration of drug development have not materially improved, despite sustained investment in computational tools and AI-enabled platforms.
At the same time, the ecosystem has seen a proliferation of AI partnerships, platforms, and claims — without corresponding evidence of FDA approvals or late-stage success directly attributable to these technologies.
The result is a widening gap between technical sophistication and operational outcomes.
Reduced R&D cost
Accelerated development timelines
Higher probability of clinical success
Costs remain structurally high
Timelines largely unchanged
Late-stage attrition persists
The gap between expectation and outcome is no longer technical. It is structural.
AI has meaningfully improved pattern recognition, hypothesis generation, and data processing efficiency.
However, these improvements primarily optimize within existing strategic assumptions. They do not resolve ambiguity around market viability, regulatory interpretation, organizational alignment, or cross-cultural execution.
In practice, AI has accelerated analysis without improving decision quality at the moments where error is most costly.
The most consequential failures in biopharma do not occur because of insufficient data, but because leadership decisions are made under false certainty.
These decisions typically involve:
Related Decision Memo
These structural failures often first appear in moments that seem operational —
meetings, negotiations, and early partner interactions.
Organizations that continue to treat AI as a substitute for judgment will see diminishing returns on capital and increasing organizational friction.
Those that explicitly design decision architectures — separating analysis from judgment, and clarity from certainty — will maintain strategic advantage even under uncertainty.
Written by
Shuying He Ph. D
Strategic advisor to biotech and deep-tech leadership teams
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Continue the Decision
This memo outlines a structural failure pattern. For some leadership teams, the next step is to apply this judgment to a live, irreversible decision.
A focused 90-minute closed-door working session, centered on one live strategic decision. Conducted directly by the founder — no slides, no junior team, no templates.
We clarify the real decision being made, surface hidden assumptions, and stress-test decision paths against regulatory, cultural, and strategic shifts.
A clear decision posture (proceed, defer, or avoid), a risk map tied to timing and exposure, and an optional executive-ready summary for internal alignment.
This engagement may stand alone or serve as the entry point to deeper advisory work.