From Spark to Sentience in the New Industrial Age

How automotive and automation are entering a new industrial age
In 1834, an early electric motor quietly signaled the beginning of an energy transformation. But commercialization took time—it would be nearly 50 years before the motor evolved into something useful and scalable. Around the same period, Carl Benz’s early automobile designs began moving into low-volume production. And while it may not have resembled today's mass manufacturing, it marked a crucial pivot in personal mobility.

The Spiral of Innovation
1834: Electric motor -> 1888: Early electric vehicle -> 1913: Ford assembly line -> 2000s: IoT & cloud in manufacturing -> 2020s: Acceleration of EVs

From the start, the automobile and its manufacturing methods have evolved in a spiral—each innovation in vehicle design driving new production technologies, and each advance in manufacturing unlocking new possibilities in mobility.
Fast-forward to the 21st century, and we’re seeing the reemergence of the electric car in its second age—this time, layered with sensors, software, and AI.
Today, a modern vehicle is often called a “smartphone on wheels.” High-end models may contain close to 1,000 ECUs (Electronic Control Units) and up to 3,000 semiconductors—controlling everything from in-cabin climate to ADAS braking systems. As the car evolved into a digital machine that continues to improve its performance even after being sold, the factory that builds it had to evolve too.

From Automation to Intelligence
Behind every high-tech vehicle is a manufacturing system undergoing its own transformation. The Fourth Industrial Revolution brought us cyber-physical systems, IoT sensors, and cloud-connected machinery.
But now, the Fifth Industrial Revolution is beginning to emerge— defined by Agentic AI: intelligent systems that don’t just analyze data but make decisions, configure themselves, and collaborate autonomously across networks.
In this new paradigm, AI doesn't just flag faults—it prevents them. Machines equipped with learning capabilities detect abnormal vibration patterns, thermal changes, or power fluctuations, and adjust their operation in real-time. They trigger service protocols automatically and adapt to avoid downtime. This is the shift from descriptive to prescriptive and self-organizing manufacturing.

The Edge of Insight
While cloud platforms have transformed enterprise visibility, many of the most valuable predictive insights are now being made directly on the factory floor.

Modern AI-driven platforms allow engineers and maintenance teams to perform sophisticated data analysis without needing programming expertise. Edge-level systems monitor servo drives, robots, and inverters—learning their behavior over time, identifying anomalies, and preventing faults before they impact production. These technologies also protect sensitive factory data by keeping it within the local network, ensuring security and real-time responsiveness. In some cases, the devices themselves have gained onboard AI capabilities—enabling them to diagnose issues independently. For example, robots can now predict joint wear, and servo systems can detect problems in connected mechanical components such as belts, gears, or ball screws—alerting operators in advance of serious failures.

As one automation expert put it: "We’ve taken capabilities that traditionally required data scientists and made them accessible to the people who know the machines best."

Complexity, Multiplied
The challenge is no longer just about preventing machine failures—it’s about managing exponential complexity. Automotive manufacturers must now produce fossil fuel, hybrid, and electric vehicles, often with overlapping production lines. The ultimate goal? A single, adaptable line capable of handling all variants seamlessly. And regardless of the drivetrain, today’s vehicles are increasingly electronic. That means more wiring, more software, and tighter integration across components.
Production systems must adapt in real-time—not just to changes in design, but to the way demand shifts across regions. This calls for a flexible, layered maintenance strategy: combining predictive, preventive, and corrective methods in a unified approach.

Looking Beyond the Factory Walls
As Mobility as a Service (MaaS) gains momentum, vehicle uptime becomes an economic imperative. Fleets of autonomous or electric vehicles must be monitored, feature updates automatically shared or enabled after purchase, vehicles maintained and repaired predictively—just like the factories that build them. The tools developed for smart production lines are now migrating downstream, enabling lifecycle management for the vehicles themselves.

And with global platforms scaling across dozens of sites, coordination becomes key. Solutions must work not only at the component level, but also across regions, languages, and infrastructure differences.

Intelligent Systems, Measurable Impact
Case studies are a great place to learn as they show what’s possible, for example:

However, in each case intelligent automation isn’t just a technical upgrade—it’s a business continuity strategy.

What Comes Next
McKinsey has noted that industrial automation is approaching a tipping point, where maturity, affordability, and necessity converge. But what separates leaders from laggards is no longer just technology—it’s the ability to scale intelligence across the entire value chain.

In modern automotive manufacturing, achieving carbon neutrality across the entire supply chain is an essential requirement. The future factory won’t just follow a program. It will follow intent. Self-organizing systems powered by Agentic AI will dynamically reconfigure operations in response to goals, constraints, and real-world feedback. That’s the promise of the Fifth Industrial Revolution.

A New Kind of Readiness
Even as automotive manufacturers navigate the complexity of multi-drivetrain production and software-defined vehicles, many of the foundations for this transition have already been laid—quietly, steadily—over the past two decades of digital transformation.
The shift from physical to digital vehicle models has enabled virtual testing, faster iteration, and more efficient early-stage development. Co-design with suppliers using 3D CAD data has become standard, allowing engineering decisions to be made earlier and more collaboratively. Modular and platform-based vehicle architectures have emerged in response to rising model diversity, helping balance product differentiation with production efficiency.

Meanwhile, traceability technologies—from advanced barcode tracking to digital twins—are helping manufacturers ensure quality and compliance across increasingly complex assemblies. These same systems are now being extended to support zero-emission manufacturing goals, where every gram of material and kilowatt-hour of energy is monitored and optimized.

But as the industry pivots to EVs, new layers of challenge appear: battery supply chains, thermal systems, power electronics, and vehicle safety standards all require new manufacturing expertise. Workforce training must evolve in parallel, preparing teams to handle high-voltage

systems and sensor-heavy platforms. Production lines must flex to accommodate variations in range, charging, and region-specific regulatory features—all while keeping costs competitive. In this environment, intelligent systems become more than just enablers of efficiency—they are strategic assets. They help manage complexity, enable faster decision-making, and ensure continuity across a globally distributed network. Most importantly, they provide manufacturers with the readiness to adapt—not just to electrification, but to whatever comes next.

For more insightful articles and related content, be sure to check out Mitsubishi Electric’s Art of Manufacturing (AoM) magazine.

www.mitsubishielectric.com