As AI models democratize access to information and analysis, traditional data advantages will disappear. The only durable competitive advantage will be an organization's ability to learn and adapt. The speed of the "breakthrough -> implementation -> behavior change" loop will separate winners from losers.

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.

Many firms engage in "innovation theatre," building a portfolio of impressive but isolated AI pilots. Without a unifying strategic architecture connecting them to core growth objectives, these initiatives remain islands that fail to scale, compound, or move overall enterprise performance.

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.

The true enterprise value of AI lies not in consuming third-party models, but in building internal capabilities to diffuse intelligence throughout the organization. This means creating proprietary "AI factories" rather than just using external tools and admiring others' success.

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.

The primary reason most pharmaceutical AI projects fail to deliver value is not technical limitation but strategic failure. Organizations become obsessed with optimizing algorithms while neglecting the foundational blueprint that connects AI investment to measurable business outcomes and operational readiness.

Novartis's CEO views AI not as a single breakthrough technology but as an enabler that creates small efficiencies across the entire R&D value chain. The real impact comes from compounding these small gains to shorten drug development timelines by years and improve overall success rates.

McKinsey finds over half the challenge in leveraging AI is organizational, not technical. To see enterprise-level value, companies must flatten hierarchies, break down departmental silos, and redesign workflows, a process that is proving harder and longer than leaders expect.