Why a Unified PLM and QMS Platform Is Essential for AI Adoption in MedTech | Propulsion 2026

Recalls are up, regulatory scrutiny is intensifying, and the gap between companies that are making big bets on AI and those still running pilots is widening fast. The question is whether your organization will lead that shift or get left behind.

At Propulsion 2026, Propel CRO Zack Jha sat down with Arnab Biswas, AI Consulting and Transformation Leader for Healthcare and Life Sciences at Google Cloud, for a sharp, unfiltered conversation on where AI is actually delivering results in medtech and pharma, and what's still holding companies back. Arnab draws on his experience driving AI transformation across some of Google Cloud's largest strategic accounts, and his roots in manufacturing give him an unusually grounded perspective on what these shifts mean on the factory floor.

The conversation covers why medtech needs to catch up on AI adoption, why a unified data platform is no longer optional when FDA is scrutinizing end-to-end traceability, and why Arnab's most provocative advice to industry leaders is to stop starting small.

Key Takeaways

Medtech Has a Clear On-Ramp for AI Adoption, and The Time to Move Is Now

Regulatory submissions, tech transfer, and component orchestration across complex product lines are among the highest-value areas where medtech companies can deploy AI and see meaningful results fast. Companies that identify their specific entry points now, rather than waiting for a perfect strategy, are the ones pulling ahead.

Fragmented Data Is the Enemy of Effective AI

If a complaint comes in and your design history files, non-conformances, and CAPA records all live on separate platforms, no AI system can build the context it needs to surface meaningful conclusions. A unified platform isn't just a nice-to-have. It's the foundation for any serious AI application in quality and compliance.

FDA Is Building Its Own AI Ecosystem

The agency recently announced ELSA, a platform where FDA is developing its own agent and AI infrastructure. Regulators are upskilling fast, and companies that treat compliance as a roadblock rather than a framework for responsible AI adoption will find themselves on the wrong side of that shift.

Think of AI as a Co-Engineer, Not a Feature

The conversation has moved on from which model to use. What matters now is building the right "AI harness," the scaffolding of memory, tooling, context, and access that transforms a stateless LLM into a productive partner for engineers working on design, quality, and product release.

Start With Your Hardest Problem, Not Your Easiest One

Arnab's counterintuitive advice: skip the conversational AI pilot and go straight after your most complex, most persistent challenge. If it succeeds, you move the needle in a meaningful way. If it doesn't, the learnings accelerate everything else on your roadmap.

Change Management, Security, and Clear Success Criteria Are Non-Negotiable

The three most common AI transformation killers are not technical. They are people's ability to adopt change, security gaps that AI-generated threats are already exploiting, and use cases that launch without a well-defined outcome. Get those three things right before you write a line of code.

Don’t just follow the shift, lead it. Watch all Propulsion 2026 sessions.

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Ready to put these ideas into practice?

Explore Propel One to see how Propel's unified platform gives medtech teams the connected data foundation and agentic AI capabilities they need to move faster on design, quality, and compliance, without sacrificing the traceability that regulators demand.

About Propulsion 2026

‍Propulsion brings together everything product and manufacturing leaders need to know in one place. Bringing you Propel’s boldest platform reveals, critical AI and market insights from top industry analysts, and real customer results. These sessions are designed to cut through the noise so you can put what matters most to work immediately.