The Three Ways AI Is Transforming Pharma Quality Management

Christopher Lewis, Emoja Biopharma, sat down with PharmTech during PDA Week 2026 to discuss how AI is reshaping quality management, workforce development, and data utilization in the pharmaceutical industry.

Check out the 2-part video Interview with Lewis:

Part 1: How AI Is Transforming Biopharma Quality and Compliance

Part 2: AI Tools That Drive Competency in Complex Pharma Environments

PharmTech: How Do You Think About the Risks of Implementing AI in Biopharma?

Lewis: It really comes down to risk management and balance. You have to weigh the capabilities and capacity that AI brings against the need to keep humans meaningfully involved throughout the process — not just at the end, but at every step. That means integrating human oversight from a conceptual standpoint, through qualification and validation, and all the way into day-to-day use. When you have a human in the loop at each of those stages, you end up with a balanced, risk-based approach that's appropriate for the environment we operate in.

How Do You Measure Whether AI Tools Are Delivering Value in Quality Operations?

There are a few key metrics I'd focus on. First, cycle time. How long does it take to work through an investigation? That's a meaningful indicator of whether these tools are creating real efficiency. Second, hands-on time. Is automation freeing people up, or are they spending just as many hours on these tasks as before? And third, recurrence rates. Are we seeing the same issues and deviations come back around? I'd expect those numbers to go up initially, because AI is likely to surface repeat issues that may have slipped past human reviewers. But over time, as we use that information to get to true root cause, those rates should come down. That downward trend is where you really see the value.

What Other AI Applications in Pharma Have Caught Your Attention?

A few things stand out. The first is learning and performance. Our industry has long been stuck in a model where you generate hundreds of documents and expect people to read them and absorb everything, and that's just not how people learn effectively. AI offers a way to build competency models that are tailored to the individual, the task, or the complexity of the work. In a dynamic environment where people aren't doing the same thing every day, that kind of personalized, point-of-task support is valuable.

The second is predictive analytics, using AI to continuously monitor process performance data and get ahead of problems before they become problems. And the third, is automation of repetitive tasks. We have a lot of highly skilled people in this industry spending significant time on low-complexity, repetitive work. That kind of work can breed complacency, and complacency leads to mistakes. If we can use AI to handle that, we free those people up to apply their expertise where it matters.

What Emerging AI Opportunity Excites You Most?

It’s utilizing data. Pharma has never had a problem generating data. The challenge has always been extracting it and making sense of it in a way that drives real decisions. Whether it's process monitoring, product quality, stability profiles, or risk assessment, the data is already there. What AI gives us is the ability to use it, to pull it together across systems and get meaningful, actionable insights out of it. We've put enormous time and effort into collecting this data. Now we finally have tools that can help us put it to work.