AI Tools That Drive Competency in Complex Pharma Environments

As a part of PDA Week 2026, Christopher Lewis, Emoja Biopharma and president of the Mountain States PDA Chapter, reflects on three transformative ways in which AI is beginning to reshape how pharmaceutical companies operate, from workforce training to predictive analytics and smarter use of existing data.

Lewis challenges a deeply ingrained industry habit: the reliance on document-heavy training models. "Our industry in the past has gotten really stuck in how we handle learning and performance, and we've gotten really stuck in this model of you generate hundreds of documents and expect people to read it and understand everything," he says. In its place, he sees AI enabling personalized, task-specific competency development that meets people where they are, accounting for learning styles, complexity, and the realities of dynamic work environments where no two days are the same.

Beyond training, Lewis highlights AI's potential as a predictive tool, one that continuously monitors process performance data to flag issues before they escalate. This connects directly to his third point regarding AI: the opportunity to automate repetitive, low-value tasks that currently consume the time of highly skilled professionals. When talented people are bogged down in routine work, complacency sets in and mistakes follow, he notes, adding that AI can change that equation.

Lewis zeroes in on a long-standing industry paradox around data. "The issue has never been how much can we generate the data. We've always been really good at generating all the data," he says. "The challenge has always been getting the data out and looking at the data in a really meaningful way to draw meaningful conclusions." For Lewis, AI finally offers a path to unlocking the value already sitting inside pharma's vast data ecosystems across QC testing, supply chains, product stability, and risk assessment, and using it to drive quality and compliance forward proactively.