Schneider Electric: People are the Future of Supply Chain

Laure Collin is the Senior Vice President of Human Resources for Global Supply Chain at Schneider Electric, where she leads the people and organisation agenda for one of the world’s most complex and global industrial supply chains. 

Laure’s role is to ensure that talent, skills and culture are decisive enablers of performance, resilience, digitalisation and sustainability across our end‑to‑end value chain.

With more than 30 years at Schneider Electric, her career reflects the evolution of both the company and the HR function – having held a wide range of roles across regions and functions. 

From launching operations in Eastern Europe and Central Asia countries, to building the European HR Shared Service Center, to shaping global health and safety policies and then, more recently, leading HR for France and European countries. 

Laure explores how when we talk about AI in manufacturing, it tends to explore data pipelines, predictive maintenance and automation. But, Laure believes we are missing the most critical element: the human one. As AI transitions from isolated pilots to real-time cognitive networks, the real differentiator is not the technology itself, but how fast your workforce can adapt to it. 

Speaking to Supply Chain Digital, Laure breaks down why Industry 4.0 readiness is a cultural challenge rather than a technical one, shares a blueprint for successful human-AI teaming from Schneider Electric’s award-winning Wuhan factory and explains why talent must be treated as core operational infrastructure.

What is the biggest mistake manufacturers make when they talk about Industry 4.0 readiness? 

Treating readiness as a technology problem. In many organisations, AI is stitched into daily production and end-to-end value chains but the sites that sustain step-change results invest in talent with the same rigour they invest in technology.

If your people systems (skills, roles, incentives, governance, etc.) can’t scale as fast as AI, performance won’t stick. So, I think one of the biggest mistakes businesses make when it comes to AI is not driving cultural change at the same speed as technical adoption. 

Why is this becoming urgent now? What has changed?

We’re moving from pilots to “cognitive networks” where connected plants, logistics and suppliers are operating in real time and AI is embedded in decision-making. That raises the bar. It is no longer a question of “can we build the AI use case?" but “can our team own it and run it safely, consistently and at scale?”

Why are so many businesses struggling to upskill talent when it comes to AI?

One of the key challenges is that many organisations do not have effective ways to measure AI readiness. Traditional workforce metrics often stop at headcount, hiring or course completion. Those are activity measures, not readiness measures. We need to build a metrics-based system focused on outcomes such as productivity, critical skills and internal mobility into AI-impacted roles. 

Practically, that means treating skills with the same rigour as business assets by mapping and forecasting capability needs, embedding human-AI ways of working into everyday operations and scaling them across sites. It is also important to build a strong talent pipeline through partnerships that co-create job-ready curricula and “earn and learn” pathways.

Can you give me a concrete example of human–AI teaming in action?

At Schneider Electric’s Wuhan factory in China - one of only three Global Lighthouses for Talent designated by World Economic Forum – we implemented a large-scape talent transformation programme to help drive productivity and AI adoption.  

Wuhan is a good example because the pressure was very real and very practical. The site went through rapid automation and a major expansion in its product portfolio, and capability just couldn’t keep up at first. 

So, what actually changed in Wuhan? What did you do differently that other factories could learn from?

The big shift was treating capability like a system, not a set of training events. We introduced AI-driven competency management so we could match skills supply and demand, assign personalised learning and connect progression to a ‘pay for skills’ model. We also changed how work was planned, making it more people-centric so task allocation improved. 

On the shopfloor, GenAI tools helped technicians solve problems faster and build their confidence, which had a real impact on retention. And we did not rely on internal development alone. We built an external pipeline through partnerships with vocational institutions, apprenticeships and initiatives like R&D labs and scholarships, so the inflow of skills became sustainable rather than reactive. Over the course of the transformation, workforce readiness rose from 20% to 76% and we managed to reduce critical-role turnover from 48% to just 6%. 

If you could give other business leaders one piece of advice, what would it be?

Talent isn’t a “nice to have”, it is the multiplier. The Lighthouse story used to be mainly about productivity, resilience and sustainability. Now talent is being recognised as a core ingredient of excellence. That shift is important because it reframes workforce capability as operational infrastructure, not an HR initiative you bolt on after the fact.

Technology isn’t going to replace people who keep adapting, but it will expose skills gaps much faster. The risk isn’t “AI taking your job”. It is being outperformed by someone who knows how to use AI better, unless upskilling becomes non-negotiable.

Our responsibility is to protect people’s ability to adapt, contribute and grow as the operating model evolves.