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.
The primary barrier to successful AI implementation in pharma isn't technical; it's cultural. Scientists' inherent skepticism and resistance to new workflows lead to brilliant AI tools going unused. Overcoming this requires building 'informed trust' and effective change management.
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.
With digital twins for drug testing and local 3D printing of drugs, pharma's role could shift from mass manufacturing to licensing molecule formulas. A doctor would test a drug on a digital twin and a pharmacy would print the personalized dose on site.
Companies like Cortical Labs are growing human brain cells on chips to create energy-efficient biological computers. This radical approach could power future server farms and make personal 'digital twins' feasible by overcoming the massive energy demands of current supercomputers.