Despite pervasive AI marketing at JPM—on billboards, in presentations, and even on Uber apps—the industry has yet to see a fully AI-designed drug reach approval. This gap highlights a technology hype cycle where branding and perceived necessity are currently outpacing proven, real-world outcomes in drug discovery.

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 are stuck in "pilot purgatory," launching numerous small, siloed AI tests. While individually successful, these experiments fail to integrate into the broader business system, creating an illusion of progress without delivering strategic, enterprise-level value.

Many pharma companies allow various departments to run numerous, disconnected AI pilots without a central strategy. This lack of strategic alignment means most pilots fail to move beyond the proof-of-concept stage, with 85% yielding no measurable return on investment.

A common implementation mistake is the "technology versus business" mentality, often led by IT. Teams purchase a specific AI tool and then search for problems it can solve. This backward approach is fundamentally flawed compared to starting with a business challenge and then selecting the appropriate technology.

The increasing volume of new therapies requires pharma companies to stop treating each launch as a unique event. Instead, they must develop a scalable, repeatable, and excellent launch capability to handle the future pipeline efficiently and consistently.

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.

A primary driver of recent pharma launch failures is underinvestment in pre-launch market conditioning. Cautious investors and tighter budgets mean companies have fewer resources to tell their scientific story effectively before launch. This delayed and underfunded approach has a dramatic negative impact on commercial success.

The massive abandonment rate of health apps stems from a core design flaw: they are built to achieve company objectives (e.g., increase diagnosis) rather than integrating into patients' and doctors' existing workflows and behaviors, making them burdensome to use.