Pharma AI stays stuck in pilot as drugmakers chase output

Digital twins are being tested to improve existing factory capacity. 

Artificial intelligence (AI) in pharmaceutical manufacturing remains largely in the pilot stage, even as drugmakers test it for production and quality monitoring amidst rising demand for obesity and diabetes treatments. 

A GlobalData report said companies face challenges from outdated systems, uneven data quality, and the difficulty of applying AI in regulated manufacturing environments.

Success will depend on execution and the ability to combine manufacturing expertise with digital infrastructure in day-to-day operations, said Edita Hamzic, Healthcare Analyst at GlobalData.

Tools such as digital twins, predictive maintenance, and real-time quality monitoring are being tested to reduce downtime, limit waste, and improve batch consistency.

“Companies that see AI as part of their operational model, not as a standalone technology project, are most likely to benefit,” Hamzic added.

The manufacturing push comes as drugmakers face wider commercial pressure. A previous GlobalData report said delays in turning approved medicines into revenue due to pricing and reimbursement (P&R) processes have become a key challenge for the pharmaceutical sector.

On regulatory and macroeconomic risks, P&R constraints ranked as the third most negative factor, cited by 22% of respondents, behind Trump administration actions and trade wars.

Meanwhile, regulators are assessing AI’s role in pharmaceutical manufacturing.

The report said the US Food and Drug Administration is using AI to help set inspection priorities under a one-day inspection pilot, whilst the European Medicines Agency is focused on transparency and human oversight.