PLM & AI in Manufacturing: Deloitte's 30-Year Perspective | Propulsion 2026

Most manufacturers know they need to modernize their product data, but few have the governance, process clarity, or data discipline to actually pull it off. The gap between ambition and execution is where transformation projects go to die.

At Propulsion 2026, Propel CEO Ross Meyercord sat down with Kevin Prendeville, Senior Partner at Deloitte and one of the most seasoned voices in PLM, to unpack what's really happening inside Global 2000 manufacturers right now. With 30 years of PLM transformations spanning aerospace, automotive, semiconductor, med devices, consumer products, and more, Kevin brings a practitioner's clarity to the biggest questions in product development today.

The conversation covers three converging pressures reshaping manufacturers, why AI's biggest obstacle is foundational data hygiene, and why the most dangerous place to start a transformation is with the technology itself. If you're navigating a PLM modernization, an AI initiative, or both, this session delivers the hard-won perspective you won't find in a vendor pitch deck.

Key Takeaways

Manufacturers Are Juggling Three Distinct Challenge Sets at Once

Business-as-usual pressures like constrained R&D budgets and rising competition now sit alongside a rapidly changing product mix — more software, more sensors, more subscription models — and the volatility of globally distributed supply chains. Understanding which challenge you're solving for changes everything about how you approach it.

The Digital Thread Has Expanded Far Beyond Its PLM Roots

What started as part, BOM, engineering change, and document management has grown to span portfolio planning, requirements, design, quality, manufacturing routings, field service, and customer telemetry. Kevin explains how the platform and the scope of what manufacturers must connect and manage have expanded in parallel.

AI Is Most Powerful When Process and Data Come First

Kevin's "100 extra people" framework cuts through the AI hype: before you deploy AI agents, you need to know where your bottlenecks are, where your risks live, and whether your data is clean enough to trust. Global 2000 companies largely remain in proof-of-concept mode — not because AI isn't compelling, but because the foundation isn't ready.

The Engineering Change Process Is Ripe for AI-Driven Transformation

Change management is no longer just an engineering judgment call. Kevin and Ross explore how AI can synthesize enterprise-wide data — inventory, open POs, warranty data, active orders — to surface the true cost and impact of a change before it's made, turning a volume-heavy, audit-intensive process into a focused, high-value decision.

Human-in-the-Loop Is a Design Principle, Not a Limitation

Especially in regulated industries where product decisions affect human safety, keeping humans in the lead on AI-assisted workflows is essential. Kevin and Ross align on this as a deliberate design philosophy for AI in product development and quality management, not a temporary workaround.

Governance, Deployment, and Adoption Determine Whether a PLM Investment Pays Off

Drawing on 50-plus PLM transformations across industries and geographies, Kevin identifies three non-negotiables: governance that is bigger than your scope, a deployment strategy built from day zero that accounts for the "construction phase," and business adoption built in from requirements through go-live. Without all three, even the best-configured system risks becoming a sunk cost.

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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.