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.

AI strategies often fail to get sustained funding because they lack detailed financial models beyond simple cost savings. A credible blueprint must quantify projected revenue uplift for each initiative, a step often skipped because strategists lack the deep pharma AI experience to make accurate forecasts.

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 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 85% AI project failure rate isn't a technology problem. It stems from four business and process issues: failing to identify a narrow use case, using data that isn't clean or ready, not defining success and risk, and applying deterministic Agile methods to probabilistic AI development.

Many AI projects become expensive experiments because companies treat AI as a trendy add-on to existing systems rather than fundamentally re-evaluating the underlying business processes and organizational readiness. This leads to issues like hallucinations and incomplete tasks, turning potential assets into costly failures.

Long-term competitive advantage will belong not to firms with the best algorithms, but to those that build the most intelligent organizations *around* AI. The key is developing the ability to absorb, direct, and compound AI's power in service of coherent strategic goals.

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.