A significant implementation roadblock is the ownership battle between IT and business functions. IT wants to control infrastructure and moves slowly, taking years. In response, business units run their own unsanctioned initiatives to move quickly, leading to a disconnected and unscalable approach to AI.

By 2030, pharmaceutical companies are expected to double their product launches without a proportional increase in headcount or budget. This "grow without growing" pressure necessitates a fundamental shift towards technology-driven efficiency and productivity.

Companies run numerous disconnected AI pilots in R&D, commercial, and other silos, each with its own metrics. This fragmented approach prevents enterprise-wide impact and disconnects AI investment from C-suite goals like share price or revenue growth. The core problem is strategic, not technical.

Large pharmaceutical companies face losing up to 50% of their revenues by 2030 due to the largest patent expiration wave in history. To survive, they will be forced to acquire innovation from the biotechnology sector, fueling a sustained M&A cycle for years to come.

While the FDA is often blamed for high trial costs, a major culprit is the consolidated Clinical Research Organization (CRO) market. These entrenched players lack incentives to adopt modern, cost-saving technologies, creating a structural bottleneck that prevents regulatory modernization from translating into cheaper and faster trials.

Large pharma companies are discovering that implementing AI to solve one part of the drug development workflow, like target discovery, creates new bottlenecks downstream. The subsequent, non-optimized stages become overwhelmed, highlighting the need for a holistic, fully choreographed approach to AI adoption across the entire R&D pipeline.

The pharmaceutical industry risks repeating Kodak's failure of inventing but ignoring a disruptive technology. For Kodak, it was digital photography; for pharma, it's AI. The industry possesses vast amounts of data (the new 'film'), but the real danger lies in failing to embrace the AI-driven intelligence layer that can interpret and act on it.

Recognizing their lag in technology adoption, pharmaceutical companies are now recruiting executives from consumer goods (CPG) and retail. These industries have a more mature approach to data and customer-centricity, and pharma aims to inject this DNA into its traditionally conservative corporate culture.

A decade of active M&A left large pharmaceutical companies with a tangled mess of disparate technology platforms and data standards. The immense difficulty of integrating these acquisitions became a primary catalyst for investing in unified, scalable data foundations and modern IT infrastructure.