AI in Life Sciences: Insights From Life Sciences Quality and Manufacturing Professionals

This blog post is the second in our series on of life sciences executives' perspectives on AI. You can read the first installment here.

As artificial intelligence (AI) transforms industries worldwide, life sciences quality and manufacturing professionals face unique opportunities and challenges. Through conversations with industry leaders in these departments, we've identified key insights into how organizations are implementing or planning to implement AI to enhance operations while navigating complex regulatory requirements. Quotes throughout this blog post reflect feedback from a series of one-on-one interviews MasterControl conducted with industry professionals.

Innovation vs. Compliance

"In the medtech space, given how we are bound by FDA (the U.S. Food and Drug Administration) and bound by a lot of regulatory concerns across different geographies, not everybody is fully augmented to that regulatory landscape, and that limits our ability to scale solutions faster than we like," observed one operations director. This tension between innovation and compliance highlights the current state of AI adoption in life sciences.

AI Applications Transforming Quality Management

While it is clear that AI adoption maturity varies significantly across life sciences organizations, several quality professionals shared specific use cases where they are evaluating AI, have a proof of concept (POC) in place, or have implemented:

• Complaint Processing: "We manage about 15,000 complaints a month. We were able to use artificial intelligence to streamline the complaints that really deserve an investigation as opposed to spending time reviewing things that didn't deserve an investigation," shared one executive. The result: savings equivalent to 15 full-time employees monthly.
• Deviation and Corrective Action/Preventive Action (CAPA) Management: "There's an opportunity for CAPA workflows to use AI/ML (machine learning) to arrive at a more defined root cause investigation, whereas previously people would spend hours and hours, even months, to get to a root cause." These management systems analyze patterns across products, operators, equipment, and procedures to predict which deviations might escalate to CAPAs.
• Regulatory Submissions: Quality teams are exploring AI to auto-generate regulatory submissions, organizing development documentation to meet FDA and other agencies' requirements. "We're investigating how we can take design and development documentation and package that information up in a way that meets the format and requirements of different regulatory bodies," one quality leader explained.
• Audit Preparation: AI tools now evaluate quality systems against current regulations to predict audit success, identify gaps, and highlight missing data capture requirements.

Manufacturing Operations Enhanced by AI

Manufacturing professionals I spoke with also share varying levels of AI implementation, but several themes arose identifying opportunity areas where they might have a POC, early adoption, or implementation:

• Supply Chain Intelligence: "Previously we relied on suppliers for lead times, and if there was a shortage, you wouldn't know until it was too late. Now AI tools have built-in capabilities to look at supplier health, understand potential shortages, and identify alternate vendors and parts."
• Process Control: "The industry has been doing statistical process control, but you're babysitting your processes through SPC (statistical process control) rules. Now AI enables us to synthesize data and provide action-based alerts. When it sees a trend happening, it allows for adjustment before problems occur, sometimes with closed-loop control for automatic correction."
• Predictive Maintenance: "There's powerful use of AI today in predictive maintenance, showing great promise. We're avoiding unexpected breakdowns, extending asset life, and reducing equipment downtime by significant amounts."
• Automated Visual Inspection: Real-time inspection for precision and dimensional verification is accelerating, with industry leaders implementing sophisticated machine vision systems.
• Device History Record Analysis: "We're using AI to assess device history records and look at measurements over time. It's an additional process control parameter that helps us identify trends and migrating specifications across our manufacturing operations."

Both quality and manufacturing professionals make it clear that they see opportunities to further unlock insights and trends in their data captures to help predict trends and make their operations more efficient.

Implementation Challenges

Despite clear benefits and promising use cases, quality and manufacturing life sciences professionals also acknowledge that the industry faces significant hurdles implementing AI:

• Regulatory Uncertainty: "The challenge is how comfortable are the regulators with what we're doing? There's a view that AI and algorithms are black boxes and nobody understands how they really operate." Companies must develop clear governance strategies that satisfy regulatory scrutiny.
• Global Compliance Complexity: "When we look at what's going on in Europe, the U.S., China, and Japan, our products must comply with all of these requirements at once. We're very concerned with requirements in one country opposing the requirements in another." This creates particular challenges for organizations operating global manufacturing networks.
• Data Quality and Standardization: "For advanced therapies where our data isn't consistent or we lack standardized processes, the data generated is not consistent enough to enable meaningful use of AI." Organizations with legacy systems or multiple platforms especially struggle with this challenge.
• Validation Procedures: "Companies today don't have their procedures in place for AI tools. When we say, 'Validate an AI model,' what do we actually mean? What activities, data, and testing are required?" The industry is still developing appropriate validation frameworks.
• Human Verification Requirements: "You still need to keep traditional approaches updated in parallel. Right now, AI is more of a bot—it's there to help with decision-making, but it's not replacing what we're doing from a regulatory or manufacturing perspective." Human oversight and review to ensure the explainability of content generated by AI is essential.

The Path Ahead

Today, as the regulatory environment continues to evolve, quality and manufacturing professionals aim to evaluate, and perhaps rethink their processes to efficiently and effectively incorporate and scale AI across their operations. Looking to the future, these industry leaders anticipate advancements that more seamlessly integrate AI across processes, across their extensive operational data, and across their sites, such as:

• Integration With Enterprise Systems: "In the 3-to-5-year horizon, our AI use cases will be integrated into QMS (quality management system), MES (manufacturing execution system), and ERP (enterprise resource planning) tools we already use. We'll move away from point solutions into integrated solutions."
• Improved Process Control: The evolution of closed-loop control systems will reduce operator intervention and enable proactive quality management.
• Enhanced Documentation and Reporting: "We're looking at ways to use AI to auto-prepare management review documents, pulling in raw data and writing narratives for each section."
• Regulatory Harmonization: As agencies develop more consistent guidelines for AI validation, implementation should accelerate across global operations.

As one executive summarized, "We're going to get a lot more comfortable with the use of AI in driving compliance, productivity, and simplification of work streams. There's going to be a higher utilization of AI moving forward, and that will dovetail with more integration within our existing tools."

Conclusion

The life sciences industry stands at a pivotal moment in AI adoption for quality and manufacturing operations. Organizations that successfully navigate regulatory complexity while implementing these technologies will gain significant advantages in operational efficiency, product quality, and compliance.

For quality and manufacturing professionals, the path forward requires balancing innovation with compliance, carefully selecting use cases with clear ROI, and building frameworks that satisfy global regulatory requirements while delivering meaningful operational improvements.