Pharma’s Shifting Approach to AI Partnerships: From Ownership to Collaboration

Selling to pharma has never been easy. Long sales cycles, entrenched ways of working, and a bias toward building in-house often make it feel like an uphill battle for startups. But the landscape is shifting — and the evidence is clear.

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In our recently published report — which features C-suite insights from 16 of the top 20 global pharma companies — we found a consistent takeaway. Pharma’s historical “ownership mentality” is giving way to a new era of hybrid approaches. For founders, this creates a real opportunity, but only if you understand how pharma thinks and what it takes to win trust.

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The Ownership Mentality Is Evolving

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Traditionally, pharma preferred to build internally. As one leader put it: “Everything to date was internal… we want to do most of this in-house.” Another added, “If it’s a black box, it’s hard to trust.”

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That skepticism remains — but it’s no longer absolute. Today, only 30% of pharma leaders plan to stay fully in-house. The rest are open to partnerships: 40% expect to take a hybrid approach, while 30% are leaning external-first.

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Structurally, pharma remains inclined to build at the application and data layers, where some capabilities exist. However, we believe this posture is beginning to change, especially as startups demonstrate differentiated value, speed, and flexibility that internal teams can’t always match. In contrast, partnering at the compute and foundation model layers is already more common, where scale, specialization, and infrastructure demands make external collaboration both necessary and efficient.

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The reality is that internal teams can’t move fast enough or capture the full breadth of AI’s value alone. That’s where startups come in. The challenge? Proving you can add value without disrupting everything pharma has already built.

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Integration, Not Performance, Is the Real Barrier

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Many founders assume the hurdle is proving technical superiority. In reality, integration is the sticking point.

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One pharma executive told us:
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“I have to come in over the top for my team to move beyond a succession of AI pilots to committing to a partner for scaled implementation. The hesitation relates to the work required of the team to integrate the new partner into their workflow, and the potential implications to team size and budget if business objectives of the partnership are met.”

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In other words: even if your tech works, if it creates too much friction for teams, it won’t scale.

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That’s why many companies are turning to sandbox environments — secure spaces to test AI models without risking compliance or disrupting core systems. Founders who can plug into these environments and show quick wins will have a clear advantage.

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The New Rules for External Partners

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Pharma isn’t rejecting external innovation — it’s raising the bar. To succeed, AI startups must:

• Be transparent. Black-box solutions won’t fly. Explain how your model works and builds in compliance.
• Prove ROI early. Expect limited data access upfront; show value anyway.
• Reduce friction. Design for pharma workflows and governance realities.
• Build credibility. Use milestone-based delivery and proactive communication to earn trust.

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As Dan Sheeran, GM of Healthcare & Life Sciences at AWS, shared:
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“No single pharma company has all the data or compute to do it alone. What we’re seeing is a shift toward starting with open-source or startup foundational models, then adapting them with internal data to create differentiated value… the real traction today is with hybrid models.”

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For founders, the takeaway is clear: the winners won’t be those trying to replace pharma’s internal efforts, but those who design solutions that can complement them — evolving from external-first to hybrid.

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The Bottom Line for Founders

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If you’re building AI for pharma:

• Expect scrutiny at the application layer.
• Design for strict data ownership and governance.
• Show value without immediate access to proprietary datasets.
• Earn trust before asking for deeper integration.

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Pharma’s mindset is shifting, but the burden of proof is on startups. Those who can combine transparency, differentiation, and seamless integration will move beyond pilots and into scaled, strategic partnerships.

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This snapshot is just one part of a bigger picture. Our AI Pharma Report dives deeper into how pharma is making AI investment decisions, where the most promising opportunities lie, and how both executives and entrepreneurs can bridge the gap between potential and impact. You can also dive into how pharma companies are partnering with AI startups across the value chain here.

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