Report
How data platforms and governance must evolve for an agent-driven world
AI works in pharma but it still doesn't pay
A joint Pistoia Alliance and Thoughtworks poll of 170 pharmaceutical R&D leaders found that 30% now run an enterprise-wide AI platform.
Only 4% can point to more than 10% cost or time impact from it, and 69% have no clear metrics to judge impact at all.
This report looks at why, and what data and AI leaders should do next.
What's inside the report
The report examines five findings from the poll, checks them against industry benchmarks, and sets out what leading organizations are doing differently:
01
AI ambition is accelerating, but enterprise scale remains elusive.
02
Adoption is concentrated in low-risk, knowledge-intensive work, not core scientific research.
03
Data quality, not model quality, is the defining constraint on AI value.
04
Most organizations are still stuck between pilots and platforms.
05
Five priorities for building the intelligent enterprise, and how to sequence them across R&D, manufacturing and commercial.
What's actually holding AI back
Fixing data quality and accessibility matters most for AI success in the coming year, according to 59% of respondents, far ahead of embedding AI agents, change management or AI governance platforms. Foundation models are no longer the bottleneck. Scientific knowledge quality is.
The report also shows where AI is landing today: drafting presentations and reports is the top area of real impact, and teams producing reports and regulatory submissions are the main beneficiaries. Wet-lab scientists, the people closest to the science, report the least benefit so far.
Who is this report for
CDOs
Own the data foundation the rest of the AI agenda depends on, and need a defensible way to show its return.
CIOs and CTOs
Set the platform and governance strategy behind AI at scale, and answer for it at the board level.
R&D leaders
Turn scientific knowledge into AI-ready data and move pilots into products researchers actually use.
Ops leaders
Face the same platform and measurement gap in a different data environment, from equipment historians to consent data.
Get your copy
What you'll walk away with
- A clear picture of where pharma AI maturity actually stands, benchmarked against McKinsey, Deloitte, ZS, MIT and Pistoia Alliance research.
- The three moves to prioritize in your next planning cycle: closing the measurement gap, funding the data foundation as infrastructure and taking one journey per domain all the way to platform.
- Case studies from Roche, Bayer and other leading pharma organizations showing the pilot-to-product-to-platform path in practice.
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