Drawing from her presentation at the 2026 ISPE Facilities of the Future Conference, Matthews offers a candid and deeply practical look at how Sanofi is using AI to transform quality from a reactive function into a predictive, insight-driven capability.
A Role at the Intersection of Quality, Regulation, and Innovation
Matthews’ role at Sanofi sits squarely at the crossroads of regulatory science, quality strategy, and digital innovation. Her team acts as an early warning system for the organization—tracking emerging regulatory expectations, influencing industry standards, and working proactively with health authorities. Increasingly, that work includes helping to define how AI can be responsibly integrated into GxP processes.
As Matthews explains, this combination keeps the work dynamic. One day may involve analyzing new regulatory guidance; the next could be presenting industry-wide learnings on AI-enabled quality systems. It’s a vantage point that allows her to see both the promise—and the complexity—of AI adoption across pharma.
The Biggest Challenge with AI? Trust
When asked about the opportunities and challenges of AI-powered technologies, Matthews is direct: the biggest hurdle is trust. Patient safety is non-negotiable, and introducing AI into quality systems raises essential questions. How do you validate AI models? How do you explain their outputs to regulators? How do you ensure algorithms don’t drift over time?
Beyond validation, there’s also a cultural shift underway. Quality organizations have long relied on deterministic, rule-based decision-making. AI, by contrast, introduces probabilistic insights that can feel uncomfortable without the right context and controls. Layer on a regulatory framework that is still evolving, and it’s clear why thoughtful governance is critical.
Yet the opportunities are equally compelling. Predictive quality—spotting issues before deviations occur—has the potential to change the industry fundamentally. AI can uncover patterns across massive datasets that humans simply can’t process alone, dramatically improving efficiency while enabling quality professionals to focus on higher-value work. Ultimately, that means getting lifesaving medicines to patients sooner.
Plai AI: Scaling AI the Right Way
To bring these ideas to life, Matthews shares how Sanofi developed and scaled Plai—an AI-powered quality intelligence tool now used across multiple manufacturing sites globally.
Plai began with a tightly scoped pilot: automating deviation and complaint trending while generating potential root-cause hypotheses. Within months, the team saw faster identification of trends and more targeted investigations. But Matthews is quick to stress that technology alone wasn’t the differentiator.
From the start, Sanofi embedded change management, involving end users in design, being transparent about limitations, and focusing heavily on usability. Just as important was governance. Sanofi’s RAISE framework (Responsible AI at Sanofi Enterprise) provides clear guardrails for developing, validating, and deploying AI tools—ensuring risk, ethics, and compliance are addressed systematically.
The formula was simple but powerful: start focused, prove value, govern responsibly, then scale.
Measurable Benefits for Quality Teams
Today, Plai is delivering tangible results. Investigations that once took hours or days are completed significantly faster. AI-driven pattern recognition improves insight and consistency, reducing variability that naturally arises across sites or individual reviewers.
Perhaps most importantly, Plai is elevating the role of quality professionals. By automating manual analysis and report drafting, investigators can focus on critical thinking, decision-making, and risk assessment—the areas where human judgment matters most.
As Matthews emphasizes throughout the conversation, AI isn’t replacing people. It’s augmenting them.
From Scorecards to Predictive Risk
The episode also explores two complementary metrics Sanofi uses to understand quality across its network: the Quality Maturity Index (QMI) and Quality Risk Exposure (QRE). QMI measures internal quality performance using data-driven key performance indicators like deviations and corrective and preventive actions, while QRE layers in external signals such as inspection trends and audit outcomes.
Together, these metrics help Sanofi prioritize action where risk is highest—and AI makes those insights far more dynamic and predictive.
Looking Ahead: AI as Standard Practice
So where is the industry headed? Matthews predicts that within five years, AI-assisted investigations, predictive quality intelligence, and automated reporting will be standard practice, not differentiators. Organizations that haven’t begun adopting these tools may find themselves falling behind.
She also anticipates deeper integration across quality, manufacturing, supply chain, and regulatory systems—creating holistic digital ecosystems with patient impact at the center. At the same time, regulatory expectations around AI are likely to mature and become more harmonized globally.
Reasons to Listen
This episode is more than a success story—it’s a practical roadmap for organizations navigating AI adoption in pharma. Matthews offers grounded insights on governance, change management, and cultural adoption that extend well beyond technology.
If you’re exploring how to move quality from reactive to predictive, or wondering how AI can be deployed responsibly at scale, this is an episode you won’t want to miss.
Listen to the full conversation on the ISPE Podcast: Shaping the Future of Pharma.
