AI: More Than a Tool — Why It’s Becoming Central to Biopharma Strategy

As artificial intelligence continues to mature, its role in the pharmaceutical industry is also evolving rapidly. Once positioned as a functional tool to support R&D, AI is increasingly shaping how we conceptualize, prioritize, and accelerate scientific innovation. The question is no longer whether AI will impact drug discovery, but how deeply it will be embedded into the core of the Biopharma R&D.

The recently released reports below highlight an important industry trend:

• Pharma R&D Review 2025 | Citeline: Shift in AI becoming a core R&D driver, not just a tool. AI-centric M&A and internal capability building are critical. (Pages 3, 7, 62–65)
• Rewiring the biopharma operating model | McKinsey: AI is central to biopharma’s future—but success demands rewiring operating models, not just adopting tools.(Pages 4–7)
• Data 2025 State of AI Talent Report | Zeki: Risk of limiting impact — Pharma companies prefer partnerships and acquisitions over building internal AI capabilities. (Pages 27–29, 36–38)

A Strategic Inflection Point for Investment

This insight reflects an evolving trend—leveraging external expertise to deliver with AI (mostly for short term RoI) rather than developing capabilities in house. Partnerships will continue to be a critical component of the AI-Biopharma innovation ecosystem. As AI becomes more integral to the discovery process, are external engagements sufficient for long-term competitiveness? How can pharma companies become leaders in the race to bring medicines to patients and differentiate from competitors?

A Shift from Enabler to Core Strategic Engine

In recent years, AI has demonstrated its potential to enhance a wide range of R&D activities—from compound screening and trial design to predictive toxicology and patient stratification. With the emergence of GenAI and agentic hybrid architectures, we see a considerable acceleration of the innovation cycle, the enhanced predictive outputs, and the capacity to transform the traditionally linear drug discovery to development workflow.

The industry has entered a phase where AI is not simply enhancing workflows—it is increasingly shaping the scientific questions we ask, and broadening the frame in which we explore answers. In that sense, AI has transitioned from being a functional enabler to an engine that transforms the R&D process end-to-end.

This shift requires a fundamental recalibration of how we think about investment, capability building, and stewardship.

Innovation in Pharma: Where Outsourcing Works, and Where It Falls Short

Pharma has a long history of leveraging external innovation. CROs, academic partnerships, biotech acquisitions, and licensing deals have all played a critical role in advancing science. And in many cases, outsourcing has brought in novel thinking, accelerated timelines, and diversified risk.

AI is not a static asset or one-time breakthrough—it is a dynamic, iterative capability. Its value compounds over time through continuous learning, adaptation, and integration with proprietary data, workflows, and decision-making processes.

To truly harness AI’s full potential, pharma companies must build internal capabilities—not just to develop algorithms, but to embed AI into how science is done, how decisions are made, and how strategy evolves. AI, at its core, is not a service to be procured—it’s a muscle to be built.

Why AI Innovation Is Different

AI innovation is fundamentally different from traditional R&D capabilities, considerably limiting its potential when primarily driven by external partners. Unlike one-time scientific deliverables, AI is inherently iterative—it improves through cycles of experimentation, feedback, and adaptation to context. Its performance and relevance are deeply tied to an organization’s internal data, scientific priorities, and decision-making culture.

Moreover, true value from AI comes not from isolated tools, but from deeply integrating those tools into how teams think, decide, and operate across functions. That level of integration, agility, and nuance requires empowered internal ownership. Without it, pharma companies risk commoditizing their approach, relying on platforms accessible to everyone and differentiating from no one. To build sustainable advantage, AI capability must be embedded—not just licensed. It's not something to plug in—it’s something that is grown.

Long-Term Competitiveness: Strategy is Clear—Execution is Hard!

As AI becomes more central to the way medicines are discovered and developed, pharmaceutical organizations are increasingly faced with a strategic choice: whether to embed AI as a core internal capability, or continue to access it primarily through external partnerships. While both models can add value, long-term competitiveness hinges on a deeper investment in internal AI capabilities.

Building AI capabilities in-house is not without its challenges—hiring, integration, and culture-building are complex and challenging. 70% of transformations fail due to poor change management by upper management (McKinsey & Quantum Black Report, 2024, Page 2). As AI shifts from being a support function to becoming a central driver of pharmaceutical innovation, the implications for long-term competitiveness extend well beyond short-term efficiencies or cost management.Three areas in particular are critical for sustained AI impact: organizational learning, strategic flexibility, and talent positioning.

Pharma's AI Future

AI’s role in pharma is expanding—from optimizing existing processes to shaping the next wave of scientific possibilities. As that shift continues, the question for decision makers and leadership teams become less about whether to make AI a priority, and more about how intentionally they are building the capability to execute this strategic priority.

For some organizations, this may mean expanding in-house AI efforts. For others, it may involve strengthening the connectivity between internal teams and external partners to ensure knowledge transfer and long-term alignment.

What is clear is that AI is no longer merely a function on the periphery. It is becoming part of the central infrastructure of discovery—and with that comes an opportunity to reflect on how we engage, invest, and lead. Not every company will choose to build AI at its core. But those that do will shape how the future of medicine is discovered—not just delivered.