Accelerating AI Readiness in Pharma: Building the Foundations for Scalable Impact

Across the life sciences industry, the conversation around AI readiness has shifted from if to how fast. As pharma companies race to embed AI in pharma across commercial, medical, and operational functions, one truth has become clear: the winners won’t be those with the flashiest tools, but those who are ready to act.

At the Indegene Digital Summit 2025, industry leaders including Brian Cantwell, VP of Digital Strategy and Product Operations at Bayer, and Peter Marchesini, SVP at Indegene, explored what it takes to build an AI-ready commercial engine. Their discussion surfaced a set of principles that transcend any single organization—offering practical guidance for pharma leaders preparing for the next wave of AI-driven transformation.

True AI readiness in pharma begins long before deploying large language models or experimenting with generative AI. It starts with data excellence—clean, structured, and connected data ecosystems that can support advanced analytics and AI.

Many pharma organizations still struggle with fragmented datasets and unstructured content. Creating standardized metadata, taxonomies, and interoperable systems ensures that when AI tools are introduced, they deliver actionable intelligence instead of noise.

Building that foundation is not a one-time digital project—it’s a strategic commitment. Data infrastructure, content architecture, and technology partnerships must be treated as enduring enablers of innovation, not isolated IT initiatives.

AI maturity grows through use, not theory. Many companies remain stuck in “pilot purgatory,” mapping out long-term roadmaps that fail to keep pace with technology’s rapid evolution. The organizations leading in AI in pharma understand that execution beats perfection.

Rather than multi-year plans, these companies operate in short, outcome-driven cycles—testing real use cases, measuring impact, and scaling what works.

Progress in AI doesn’t come from the size of the vision, but from the speed of learning. Every experiment accelerates enterprise readiness, regardless of its success.

AI is not an IT initiative, it’s a business transformation. The most effective organizations are forming small, cross-functional teams that bring together commercial leaders, technologists, and data scientists to co-create solutions. This breaks the traditional “throw it over the fence” cycle between business and IT, replacing it with shared accountability and faster delivery.

Bayer’s own shift toward decentralized, self-organizing teams illustrates this principle well. By empowering teams to work toward common outcomes rather than follow rigid hierarchies, the company unlocked a culture of collaboration and speed.

For any pharma organization, this mindset of co-creation—working together to define and solve real business problems—is the real catalyst of AI readiness.

In the AI-driven enterprise, IT’s role has evolved from gatekeeper to co-creator. Leading companies are engaging their technology teams early in the ideation process, empowering them to shape strategy alongside business stakeholders.

This shift is essential. IT can no longer simply deliver applications—it must help define the business cases where AI can create measurable impact.

Building direct relationships with technology partners—rather than relying solely on intermediaries—can accelerate learning, experimentation, and adoption, allowing teams to stay ahead of the curve.

As organizations advance in AI readiness, a critical evolution occurs in how decisions are made. Instead of relying on top-down governance or rigid budget cycles, leadership must trust empowered teams to make responsible investment choices based on shared enterprise goals.

Bayer’s approach is a deliberate move away from a “brand mindset” toward an organizational mindset. Teams are encouraged to view every resource allocation—whether for a capability, pilot, or AI investment—through the lens of collective impact.

By trusting teams to make decisions “safe to try,” companies can maintain both speed and accountability. This approach removes bottlenecks, encourages collaboration across brands, and fosters a culture of mutual responsibility. AI in pharma succeeds when leadership enables people closest to the work to act—guided by context, not constrained by control.

In the race to adopt AI, the real differentiator is not adoption rate—it’s measured impact. AI initiatives must connect clearly to business outcomes, whether improving engagement, saving time, or enabling better patient decisions.

By grounding every AI initiative in measurable value, companies ensure that enthusiasm translates into tangible progress and avoid chasing hype cycles that fade as quickly as they rise.

The journey toward AI readiness is not about technology maturity, it’s about organizational intent. The companies that act, iterate, and learn will outpace those still waiting for the “perfect” strategy. As Brian summarized,

For pharma leaders, that means embracing imperfection, empowering teams, and turning every use case into a steppingstone toward enterprise-wide intelligence. AI readiness isn’t a milestone—it’s a motion.