At Pharma Maintenance Europe (Berlin, October 2026), one topic consistently captured the attention of reliability leaders… how AI and condition-based maintenance can help reduce risk, improve reliability and extend product life, without compromising compliance.

For pharmaceutical manufacturers, the challenge is clear. Maintenance teams are under constant pressure to maximize plant availability, execute shutdowns seamlessly, control maintenance costs and support increasingly ambitious sustainability targets. Yet many critical products are maintained using fixed replacement intervals, a strategy that often results in unnecessary interventions, excessive component replacement and significant maintenance effort being spent on products that may not require attention.

At Pharma Maintenance Europe, one of the event’s most discussed sessions was a collaboration between Biogen and Saunders, exploring how AI-supported condition-based maintenance, could help transform maintenance decision-making for diaphragm valves in pharmaceutical manufacturing. The presentation shared insights from an ongoing project that combines Biogen’s operational asset data, with Saunders‘ diaphragm expertise, to better understand the factors that influence valve performance, degradation and service life. The strong audience response highlighted a growing industry desire to better align maintenance activity with actual product condition.

The Current Risks

Unplanned failures remain one of the largest risks facing pharmaceutical manufacturing operations. At the same time, maintenance shutdown windows continue to shrink, placing greater pressure on teams to make the right decisions, at the right time. Every unnecessary intervention introduces cost, resource requirements and potential process risk, while every missed issue can impact production, product supply and operational performance.

The Biogen Saunders collaboration provides a practical example of how this transition is beginning to take shape. Rather than relying solely on fixed replacement schedules, the project explores how operational data, maintenance history and equipment condition indicators can be used to support smarter maintenance decisions. The objective is to move from traditional time-based maintenance towards a more predictive, data-driven approach that improves equipment reliability, extends service life, reduces unplanned downtime and supports more effective maintenance planning.

For many maintenance leaders attending the event, this real-world application was particularly compelling. The discussion moved beyond theoretical AI concepts and focused on how data-driven approaches can be applied within highly regulated pharmaceutical environments to improve reliability, reduce operational disruption and support more effective asset lifecycle management.

Condition-based maintenance offers an alternative approach. By combining product performance data, operational insights and AI-supported analysis, maintenance teams can gain a deeper understanding of equipment health, identify developing issues earlier, and make maintenance decisions based on evidence, rather than prescriptive schedules.

For reliability leaders, the potential value extends far beyond maintenance efficiency.

Improved asset visibility can help reduce the likelihood of unexpected failures, optimize shutdown planning and focus maintenance resources on the areas that matter most. This enables organizations to improve reliability, while reducing overall maintenance burden and operational risk.