Legacy manufacturing systems in the automotive industry often struggled with latency, rigid workflows, and fragmented reporting. They reacted to issues after the fact, whether that meant identifying quality defects post-production or responding to disruptions once delays had already occurred.
Modern systems, by contrast, are built to sense and respond. With AI at the core of operational platforms, real-time telemetry is analyzed instantly, allowing teams to adjust timelines, reconfigure task allocation, and anticipate failure points before they impact production.
“AI has changed how we interpret performance data,” says Jagtap. “We’re not just logging what’s happening, we’re running continuous simulations that tell us what might happen, and how to respond before it does.”
Jagtap’s thinking around intelligent operational systems is also reflected in his scholarly research, including . While focused on broader digital infrastructure, the paper outlines how the integration of real-time data systems can unlock new levels of coordination, responsiveness, and automation across traditionally siloed operations, principles equally vital to the automotive sector.
