Why Electrification Alone Isn't Enough: The AI Layer That Will Define the Next Decade of Industrial Vehicles

Electrification shows no signs of slowing down. Instead, it’s accelerating as rising fuel costs, sustainability pressures and operational demands increasingly position electric platforms ahead of diesel-powered fleets on total cost of ownership (TCO). It’s a turning point for the industry.

However, the economics of that turning point aren’t solely defined by these drivers. Presenting the keynote session at The Future of Electrification 2026 virtual conference, Dr. Michelle Dickinson argued that this shift, and the next competitive advantage, lies in combining electrification with AI. She called on original equipment manufacturers (OEMs) to innovate beyond replacing diesel and internal combustion engines (ICE) with electric powertrains, as straightforward electrification won’t achieve the transformation industries seek.

In the next few years, electrification’s market leaders won’t be defined by cleaner machines alone, but by machines that continuously learn, improve and optimize throughout their operational life.

Electrification's Next Chapter Is Intelligence

Industry conversations often frame electrification and AI as a single technology trend, but this is a mistake. Instead, AI should be regarded as a progression, undergoing its own transformation in three waves expected to impact the industry over the next two or three years. The inability to separate these three waves typically leads to stalled or failed pilot programs.

Three Waves of AI Progression

The first wave involves ‘AI in the machine,’ which many OEMs already deploy to optimize each machine during its duty cycles. This refers to integrating AI with systems such as battery management, thermal optimization and predictive maintenance capabilities to maximize uptime and longevity for individual vehicles or equipment.

The second wave now introduces ‘AI around the machine’ by leveraging AI to optimize operations. This includes the capabilities to orchestrate an entire fleet and optimize its charging practices. Another example would be OEMs introducing ‘digital twins’ of their products, which can improve initial design and prototyping stages or monitor and represent the data gathered from machines operating in real-world environments. Although some OEMs already leverage AI at this level, it’s not expected to become a common practice for another 24 months.

The third wave of AI progression, ‘AI as the machine,’ will eventually bring autonomous decision-making and self-management. While that once seemed a decade away, it’s not unrealistic to shorten this timeline to 36 months. Today’s decisions already impact companies’ preparedness for these increasingly autonomous systems.

With the second wave underway and the third approaching quickly, the intelligence layer connecting assets, infrastructure and operations is OEMs’ greatest opportunity for readiness and growth over the next few years. The real question isn't whether electrification works, is affordable, or is cleaner. It's whether machines can become smarter. And unlike ICE platforms, electric machines integrated with AI will continually optimize themselves even after being deployed.

Why AI Makes Electrification Economically Compelling

Electric platforms already hold the economic edge in TCO over ICE machines. However, that advantage hasn’t widened enough to produce the needed industry transformation yet. AI changes that equation, positioning operational intelligence as the strongest business case for industries to electrify.

Recently, a Swedish study demonstrated this paradigm shift by comparing a freight operation’s fleet performance using diesel-powered platforms versus electric trucks, with and without AI optimization. After more than 200 trucks completed 38,000 shipments to more than 500 locations, the results definitively showed the impact of using AI to optimize fleet orchestration, like planning routes and charging schedules. Implementing ‘AI around the machine’ for these fleet-level tasks significantly outperformed the electrified-only fleet on payload utilization (85% to 57%), mileage coverage (54% to 30%) and TCO (13% to 3%).

Industry conversations about electrification often assume that the biggest barriers to broader transformation are battery sizes and the availability of charging infrastructure. However, the AI-optimized fleet experiment demonstrated how smarter decision-making often creates greater value than additional hardware. Moreover, the results identified that future competitive advantages may come less from the machines themselves and more from the intelligence that orchestrates their usage.

The Companies That Learn Fast Will Win

OEMs seeking competitive advantages must bring their AI-driven technologies out of the lab and deploy them in real-world environments to inform continuous improvement. Whereas OEMs typically prioritize performance, availability, and reliability, AI development requires opportunities for learning. Tests and simulations demonstrating flawless performance in lab conditions cannot account for unknown variables, such as birds disrupting a highly calibrated camera by using it as a warm perch.

Every deployment experiences its own issues that only occur once it’s operational in real-world environments. The final product must incorporate solutions to these issues and perform its intended functions for the machine to be viable. Therefore, the OEMs that accelerate deployment, learning and iteration will adapt products faster than their competitors and gain considerable advantages as a result. Innovation has always involved learning from reality before competitors do, but that has never been as literal or important as it is today.

Building for an Autonomous Future Requires Trust

The development of agentic AI keeps accelerating faster than many expected. But despite the exciting new capabilities, this pace also introduces new considerations and responsibilities OEMs must prepare for. As industrial machines and equipment continually move toward self-scheduling maintenance, autonomous charging decisions, dynamic route optimization and operational decision-making, their cybersecurity, governance and understanding of consent become increasingly critical.

Connected machines introduce entirely new risk considerations. Many OEMs previously encountered similar cybersecurity challenges when developing vehicles and equipment connected to the Internet of Things (IoT). However, AI substantially amplifies the risks. Security can no longer be viewed as an add-on feature or an afterthought, and trust, transparency, and safeguards must be designed into systems from the earliest design stages.

Customers will eventually view these elevated security requirements as a baseline requirement for any purchasing decisions. As a result, the future will not belong to the companies that maximize AI use but to those that deploy AI quickly without compromising safety and reliability.

AI Should Amplify Human Expertise, Not Replace It

Electrification is no longer concerned solely with swapping powertrains. As the second and third waves of AI progress begin to impact industries, OEMs and suppliers that use AI to amplify engineering expertise and operational knowledge will find the greatest success.

The winners of the next decade will be those that build electric machines that also learn.

While the next phase will focus on creating intelligent systems that continuously improve performance, productivity and uptime, OEMs cannot forget that human judgment remains essential. Whereas AI excels at pattern recognition and optimization, humans excel at context, intuition, problem-solving, and adapting to unexpected conditions. Both will be necessary for the future workforce.