The AI Ambition-to-Value Gap, By the Numbers
Most pharma companies do not have an AI ambition problem. They have a scaling problem. Deloitte's 2026 Life Sciences Outlook, based on a survey of 280 biopharma and medtech C-suite executives, found that 78% expect AI to play a central role in driving major change this year, yet only 22% say they have actually scaled AI successfully, and just 9% report significant returns on what they have spent so far. That gap is the real story for 2026. Closing it comes down to three things.
- 70% of pharma leaders call AI an "immediate priority," rising to 85% among top-ranking Big Pharma companies.
- Only 40% of AI pilots at large pharma and biotech companies advance from pilot to scaled deployment.
- 17% of tech leaders see measurable payoff from AI in research and discovery today; 29% in clinical development.
- Over 80% of surveyed pharma companies are increasing AI budgets, while only 15% hold budgets flat.
Budgets are not the constraint. What separates the small group of companies pulling ahead from the majority still stuck in pilot purgatory is how they handle three things: data and objective-setting, risk-tiered governance, and the operating model around AI.
1. Fix Data Quality and Objective-Setting Before You Scale Anything
ZS's 2026 CDIO research, a Harris Poll survey of 115 senior technology executives at large pharma and biotech companies conducted in July 2025, asked leaders why AI initiatives stall before reaching scale. The top three reasons were weak data quality and governance (68%), unclear objectives and success metrics (67%), and lack of clear business ownership (63%).
One surveyed CIO described the shift bluntly: "We've learned enough with AI to move past experimentation," now concentrating on five to ten high-impact use cases with a defined 20-30% ROI target rather than a wide portfolio of pilots.
Before funding another AI pilot, pharma leaders should be able to answer three questions in writing: what data does this use case require and is it AI-ready today; who in the business owns the outcome, not just IT; and what specific metric defines success before the pilot starts.
2. Build Risk-Tiered AI Governance, Aligned to Where Regulation Is Actually Heading
FDA's Center for Drug Evaluation and Research published draft guidance in January 2025, "Considerations for the Use of Artificial Intelligence to Support Regulatory Decision Making for Drug and Biological Products," setting out a risk-based framework for AI used to generate evidence supporting safety, effectiveness, or quality claims.
That was followed, on 14 January 2026, by a joint FDA-EMA document, "Guiding Principles of Good AI Practice in Drug Development," setting out ten shared principles spanning the medicine lifecycle from early research and clinical trials through manufacturing and safety monitoring. European Commissioner for Health and Animal Welfare Olivér Várhelyi described it as "a first step of a renewed EU-US cooperation" on AI in medicine.
The mistake to avoid is applying one governance standard to everything. GxP-critical processes, such as adverse event detection or data supporting a regulatory submission, warrant strict, validated controls, while lower-risk internal use does not need the same review cycle. Define Ventures' study of C-suite executives at 16 of the top 20 pharma companies found 80% already have a dedicated AI governance structure in place, most focused specifically on ethics and safety.
3. Redesign the Operating Model, Including a Deliberate Build, Buy, and Partner Decision
ZS's research found that business decision-making processes (56%), talent and skills (58%), and technology capability (61%) all need to evolve to close the scaling gap. Roughly 86% of surveyed companies are already testing or implementing new roles and team structures, and 55% of CIOs say they already hold the authority to reshape enterprise operating models to support it.
Define Ventures found pharma companies splitting roughly into thirds: 40% pursuing a hybrid internal-and-external model, 30% prioritizing in-house tools, and 30% going external-first. ZS found 61% of pharma tech leaders specifically planning external AI partnerships, generally to bring in delivery experience and domain expertise rather than to hand off strategy.
Where to Start This Quarter
- Pick 5-10 AI use cases with a named business owner and a written success metric, not a portfolio of unowned pilots.
- Map your current and planned AI use cases against a simple risk tier, GxP-critical versus lower-risk, and check governance intensity against each tier.
- Read the FDA-EMA "Guiding Principles of Good AI Practice in Drug Development" (January 2026) against your current AI governance framework and note the gaps.
- Decide, function by function, what you will build, what you will buy, and where a delivery partner closes the gap faster than either option alone.
How ANG Associates Can Help
ANG Associates works with pharma and life sciences organizations on exactly this intersection: AI strategy, GxP-aligned governance, and IT delivery management, brought together rather than handled as separate workstreams. If your organization is trying to move specific AI use cases from pilot to production without adding regulatory risk, that is the conversation worth having next.
Frequently Asked Questions
Why do most pharma AI pilots fail to scale?
Weak data quality and governance (68%), unclear objectives and success metrics (67%), and lack of business ownership (63%) are the top three reasons cited by pharma tech executives in ZS's 2026 CDIO research. Only 40% of pilots reach scaled deployment.
What AI governance framework should pharma companies use?
A risk-tiered framework, not a single blanket policy: strict, validated controls for GxP-critical uses, aligned to FDA's January 2025 draft guidance and the FDA-EMA joint guiding principles published 14 January 2026, and lighter-touch review for lower-risk internal use cases.
Should pharma companies build, buy, or partner for AI?
Most are choosing a hybrid. Define Ventures found pharma C-suite leaders splitting roughly 40% hybrid, 30% in-house-first, 30% external-first, while ZS found 61% specifically planning external partnerships to fill delivery and talent gaps.
Sources
- Deloitte, "2026 Life Sciences Outlook", survey of 280 biopharma and medtech C-suite executives, published December 9, 2025.
- Zoey Becker, "AI adoption is an 'immediate priority' to most Big Pharmas, report finds", Fierce Pharma, July 17, 2025, citing a Define Ventures study of C-suite executives from 16 of the top 20 pharma companies.
- ZS, "Scaling AI in Pharma and Biotech: 2026 CDIO Research", Harris Poll survey of 115 senior pharma/biotech technology executives, conducted July 2025.
- ZS, "Pharma Industry Outlook 2026".
- U.S. FDA, Center for Drug Evaluation and Research, "Artificial Intelligence for Drug Development", draft guidance published January 2025.
- European Medicines Agency, "EMA and FDA set common principles for AI in medicine development", published 14 January 2026.