From spark to sentience in the new industrial age

In 1834, an early electric motor quietly signalled the beginning of an energy transformation. But commercialization took time — nearly 50 years passed 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. While it may not have resembled today’s mass manufacturing, it marked a crucial turning point in personal mobility.

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 described as 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 cars have evolved into digital machines that continue to improve even after being sold, the factories that build them have had to evolve as well.

From Automation to Intelligence

Behind every high-tech vehicle lies a manufacturing system undergoing its own transformation. The Fourth Industrial Revolution introduced cyber-physical systems, IoT sensors, and cloud-connected machinery.

Now, the Fifth Industrial Revolution is beginning to emerge — defined by Agentic AI: intelligent systems that not only analyze data but also 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 can detect abnormal vibration patterns, thermal changes, or power fluctuations, and adjust their operation in real time. They can trigger service protocols automatically and adapt to avoid downtime. This marks the shift from descriptive to prescriptive — and ultimately, to self-organizing manufacturing.

The Edge of Insight

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

Modern AI-driven platforms enable engineers and maintenance teams to perform sophisticated data analysis without needing programming expertise. Edge-level systems monitor servo drives, robots, and inverters — learning their behaviour over time, identifying anomalies, and preventing faults before they impact production.

These technologies also safeguard sensitive factory data by keeping it within the local network, ensuring both security and real-time responsiveness. In some cases, the devices themselves now feature onboard AI capabilities, enabling them to diagnose issues independently.

For example, robots can predict joint wear, and servo systems can detect problems in connected mechanical components — such as belts, gears, or ball screws — alerting operators well before a failure occurs.

As one automation expert observed, “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 simply preventing machine failures — it’s managing exponential complexity.

Automotive manufacturers must now produce fossil-fuel, hybrid, and electric vehicles, often on overlapping production lines. The ultimate goal? A single, adaptable line capable of handling all variants seamlessly.

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 design changes, but also to fluctuating regional demand.

This calls for a flexible, layered maintenance strategy that combines predictive, preventive, and corrective methods into 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 continuously monitored, updated, and maintained — just like the factories that build them.

The tools developed for smart production lines are now being extended downstream, enabling lifecycle management for the vehicles themselves.

With global production platforms scaling across dozens of sites, coordination has become key. Solutions must function not only at the component level but also across regions, languages, and varying infrastructure.

Intelligent Systems, Measurable Impact

Case studies illustrate what’s possible:

Global manufacturers have implemented diagnostic systems that detect potential failures in robot joints weeks in advance — triggering service workflows automatically.

Condition-based asset management programs now span multiple countries, requiring only hours to deploy at each new site.

Real-time SCADA systems help tire manufacturers such as Continental AG reduce overhead, protect data, and streamline operations across 18 plants worldwide.

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

What Comes Next

According to McKinsey, 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 throughout the supply chain is now an essential requirement. The factory of the future 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 complexities of multi-drivetrain production and software-defined vehicles, many foundations for this transition have already been laid — quietly and 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 earlier and more collaborative engineering decisions.

Modular and platform-based vehicle architectures have also emerged, balancing product differentiation with production efficiency. Meanwhile, traceability technologies — from advanced barcode tracking to digital twins — help 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 every kilowatt-hour of energy is monitored and optimized.

However, as the industry pivots toward EVs, new challenges arise: battery supply chains, thermal systems, power electronics, and vehicle safety standards all demand new manufacturing expertise.

Workforce training must evolve in parallel, preparing teams to handle high-voltage systems and sensor-heavy platforms. Production lines must remain flexible to accommodate variations in range, charging systems, and region-specific regulations — all while maintaining cost competitiveness.

In this environment, intelligent systems become more than enablers of efficiency — they are strategic assets. They help manage complexity, accelerate decision-making, and ensure continuity across globally distributed networks.

Most importantly, they provide manufacturers with the readiness to adapt — not only to electrification but to whatever comes next.

Source: Mitsubishi Electric Corporation