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.
A significant implementation roadblock is the ownership battle between IT and business functions. IT wants to control infrastructure and moves slowly, taking years. In response, business units run their own unsanctioned initiatives to move quickly, leading to a disconnected and unscalable approach to AI.
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.
Unlike previous technologies, ChatGPT’s launch created immediate, widespread pressure on biopharma executives. Prompted by their boards and even families, they recognized the potential to leapfrog years of development, rapidly elevating AI on the corporate agenda despite concerns about data privacy and IP.
A traditional IT investment ROI model misses the true value of AI in pharma. A proper methodology must account for operational efficiencies (e.g., time saved in clinical trials, where each day costs millions) and intangible benefits like improved data quality, competitive advantage, and institutional learning.
Long before the current hype, a 2005 AI project used electronic medical records and genomic data to find children with an ultra-rare disease who would have otherwise died before age 10. This highlights AI's long-standing, life-saving impact beyond recent commercial applications.