Smarter Fermentation Takes Hold in US Pharma

US pharma leans on AI to steady fermentation and push bioprocessing into a digital era

12 Nov 2025

US drug manufacturers are beginning to introduce artificial intelligence into fermentation lines, replacing long-standing manual routines with real-time digital control. The shift is intended to reduce variability in biologic production, a persistent issue in processes that depend on sensitive temperature, oxygen and nutrient conditions.

A study in the World Journal of Microbiology and Biotechnology highlighted the scale of the problem, noting that small deviations during a run can alter output quality. AI models trained on historical production data can detect these early drifts and adjust operating settings before they affect a batch.

Several companies are testing the approach. Quartic.ai and TAPI Technology have developed machine-learning platforms now installed in selected pharmaceutical plants. In one project with a global biopharma group, Quartic’s system produced yield improvements of “about 10% to 15 per cent”, according to published documentation. The gains are limited to specific deployments but offer insight into the potential of digital fermentation.

Activity around the broader ecosystem is increasing. Startups are entering the field, major equipment suppliers are adding AI functions to sensors and control units, and investment in bioprocess automation has risen this year. Analysts argue that the combination of advanced sensing, cloud-based analytics and automated control could become as important to the sector as earlier moves toward continuous manufacturing.

Regulators are still defining how AI-guided systems should be assessed, and many facilities rely on older hardware not built for data sharing. Researchers and executives describe these obstacles as operational rather than structural, pointing to upgrade programmes already under way.

Industry groups see the current transition as more than a technical update. As digital tools become embedded in production lines, US drug makers are creating a system that can learn from each batch and apply those insights immediately, supporting faster and more consistent output of complex biologic medicines.