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.

ProPhet's CEO notes his conviction in AI wasn't a sudden breakthrough. Instead, it was a growing understanding that machine learning's ability to handle noisy, incomplete data at scale directly solves the primary bottlenecks of traditional pharmaceutical research.

AI delivers the most value when applied to mature, well-understood processes, not chaotic ones. Pharma's MLR (Medical, Legal, Regulatory) review is a prime candidate for AI disruption precisely because its established, structured nature provides the necessary guardrails and historical data for AI to be effective.

Many pharma companies have breakthrough AI results in isolated functions, or "pockets of excellence." However, the ultimate competitive advantage will go to the company that first connects these disparate successes into a single, integrated, enterprise-wide AI capability, thereby creating compounded value across the organization.

As AI enables early disease prediction (like Grail's cancer test), the number of sick patients will decrease. This erodes the traditional drug sales model, forcing pharma companies to create new revenue streams by monetizing predictive data and insights.

The relationship between AI startups and pharma is evolving rapidly. Previously, pharma engaged AI firms on a project-by-project, consulting-style basis. Now, as AI models for drug discovery become more robust, pharma giants are seeking to license them as enterprise-wide software suites for internal deployment, signaling a major inflection point in AI integration.

Big pharma is heavily investing in AI-driven drug discovery platforms. Deals like Sanofi with Irindale Labs, Eli Lilly with Nimbus, and AstraZeneca's acquisition of Modelo AI highlight a strategic shift towards acquiring foundational AI capabilities for long-term pipeline generation, rather than just licensing individual preclinical assets.

AI tools can be rapidly deployed in areas like regulatory submissions and medical affairs because they augment human work on documents using public data, avoiding the need for massive IT infrastructure projects like data lakes.

Pharma companies engaging in 'pilotitis'—running random, unscalable AI projects—are destined to fall behind. Sustainable competitive advantage comes from integrating AI across the entire value chain and connecting it to core business outcomes, not from isolated experiments.