An AI-Native Supply Chain Requires The Right Foundation, Says One Volkswagen Group Engineering Department Head

For decades, supply chain management has been built around the assumption that a disruption is an exception. Something breaks, the organization absorbs the shock, recovers, and returns to normal. That model is failing because the exception has become the condition. Companies now face multiple simultaneous disruptions rippling across suppliers, plants, logistics networks, and demand at once, and the old absorb-and-recover posture can't keep pace. AI offers a way to shift from reacting to disruption toward continuously sensing, predicting, and adapting ahead of it, but that shift only pays off if the underlying foundation, the baseline, the human oversight model, and the data architecture are built deliberately before the intelligence layer goes on top.

Advocating for a different approach is Dr. Praveen Mishra, Senior Department Head of Engineering R&D at Volkswagen Group's India technology hub, who has more than a quarter-century of experience spanning automotive manufacturing, supply chain, and digital transformation, and a Doctorate in Business Administration. He recently authored a white paper on AI-native synchronized supply chains for automotive operations, which argues that the value of AI in the supply chain depends less on the sophistication of the models than on the discipline of the groundwork beneath them.

"The mindset of recovering after a disruption should change to a mindset of continuously sensing, predicting, and adapting," Mishra says. That reframing is the starting point, and it changes what the supply chain is fundamentally designed to do.

From one-off disruption to continuous adaptation

The core problem Mishra identifies is that the traditional model treats every disruption as an isolated event. Whether it's a single supplier issue, a weather delay, or a transportation problem, it's to be absorbed and recovered from individually. That framing, he asserts, no longer matches reality. "One of the biggest problems of supply chain is treating everything like a one-off disruption. That approach doesn't work anymore." The disruptions organizations face now are interconnected and simultaneous, and a problem in one area ripples across the entire chain.

His proposed answer is a system that doesn't wait for the shock. Built on a real-time synchronization layer, digital twins, AI agents, and a reinforcement learning loop, it continuously monitors events, tests scenarios, and evaluates alternatives before a problem materializes. The goal is a supply chain that recommends actions in advance rather than scrambling to recover after the fact, turning raw operational signals into usable data intelligence the business can act on early.

Measure before you model

The discipline Mishra is most emphatic about is establishing a baseline before promising any AI-driven savings, a lesson he's learned the hard way. "One of the biggest mistakes is approving AI projects based on forecasts. Even I have made mistakes on this, and I've learned with time and experience that is not the best way." The reason is that without an established baseline, there's nothing legitimate to measure the result against.

His alternative is to instrument the operation first. Before launching a program, he collects months of data across concrete operational metrics: production downtime, inventory levels, logistics cost per vehicle, equipment efficiency, on-time delivery, yard unloading and docking. Only once that data exists can a mature baseline be negotiated and a program's real benefit be proven afterward. "If anyone comes and asks me what benefit I've obtained, I have baseline data and a result I can compare. The principle is to measure before you model," he says.

The point goes beyond technical rigor. Credibility hinges on it. A savings claim without a baseline is unfalsifiable, and the discipline of baselining is what lets a leader defend the investment when results come due.

Keep the human in the loop where the stakes are high

Mishra is direct about the limits of autonomy in an environment where a wrong call has physical, immediate consequences. His framing, which he's presented at industry leadership forums, is that AI is an intelligent advisor rather than a decision maker. "Absolutely, AI can help make decisions, but the moment it comes down to the final say that can have a significant operational risk or a financial risk, that's where the human has to be there." The distinction becomes especially concrete in a manufacturing context. "We're talking about stopping a production line or rescheduling production, switching a carrier, or changing a delivery commitment. In these cases I would recommend human in the loop," he notes.

The division of labor is deliberate. Routine, low-risk tasks that sit within predefined rules are exactly where AI should operate freely, because there it delivers speed and efficiency without introducing meaningful risk. AI's genuine strengths, like analyzing vast data, simulating many scenarios quickly, and recommending strong options, are best pointed at those decisions, while the consequential calls stay with people.

Design data residency from day one

The point Mishra frames as most fundamental, and most often underestimated in global manufacturing, is where data can legally live and how it can move. "One thing that must be decided very early on is the data architecture and data residency," Mishra advises. Building the integrations, pipelines, models, and dashboards first, then discovering a regional or sovereignty constraint later, is where projects unravel.

The stakes are specific to global operations. Different regions carry different homologation, sovereignty, privacy, and compliance requirements, which means the data itself is governed differently depending on where it originates and where it flows. Mishra wants those questions answered before anything is built: what can leave the plant, what must stay in the country, what can stay within a region, what can be centralized. "The data-residency-first approach is very important, because then I'm able to pin the data to regional architecture," Mishra says. Reverse that order and the cost of adaptation and migration becomes so high it can unwind the entire strategy.

This isn't theoretical for a global OEM. Mishra notes that his organization operates geographically defined cloud tenants across regions, with clear rules about which data belongs to which region, precisely because the data governance has to hold across Europe, China, India, and beyond.

The payoff, honestly scoped

Mishra is careful not to oversell the returns of his proposed approach, which itself reflects the measure-before-you-model discipline. Rather than promising dramatic figures, he scopes the benefit realistically. "If it's properly deployed, I'm seeing efficiency gains of 15 to 27%," he shares, noting that the upper end reflects gains compounding over the next few years as more of the architecture matures. He says the number isn't higher because many of those efficiencies have already begun to be realized, so the projection reflects genuine incremental gain rather than headline inflation.

The through-line of Mishra's argument is that the intelligence layer is the easy part. What determines whether an AI-native supply chain actually delivers is the unglamorous groundwork beneath it: a real baseline to measure against, a clear line between what AI advises and what humans decide, and a data architecture that respects where information is allowed to live. Get that foundation right, and the shift from reacting to disruption toward anticipating it becomes durable rather than aspirational. The supply chain that continuously senses, predicts, and adapts is within reach, but only for the organizations disciplined enough to build the foundation first.