Automotive automation: AI, digital twins and the real limits of scale
AI-assisted robotics, digital twins and condition monitoring are reshaping automotive automation, but cost, safety, variability and ROI still define what OEMs can deploy at scale, says MTC’s Mike Wilson
Automotive manufacturing shop floors that look superficially familiar are quietly becoming far more data-rich and computationally capable. The familiar structures and operations – assembly lines, articulated arms, conveyors – belie significant shifts in how lines are designed, how equipment is programmed and how operations are managed. These changes are evolutionary rather than revolutionary, yet they add up. As one industry observer put it, “there’s an awful lot of stuff that’s in the background that will change it going forward.” That observation, from Mike Wilson, Chief Automation Officer at the Manufacturing Technology Centre in the UK, captures the central tension facing OEMs and suppliers alike: new capabilities are arriving, but pragmatic constraints – cost, safety, variability and the need for clear payback – still determine what actually gets installed at scale.
AI in the background: Programming, optimisation and actionable insights
AI integration into robotics and machine control is already real, even when it is not advertised on the side of a machine. “AI is already being used,” Wilson said, and he went on to explain that it is often embedded invisibly in two principal ways. First, it is making machines easier to program and optimise algorithms, assisting with motion profiles, trajectory planning and process parameter tuning, reducing the amount of specialist labour required to commission or reconfigure cells. Second, AI acts as an analytics layer on top of the mass of sensor data modern automation produces. “AI is able to take that data in and then help us analyse it and present actionable insights,” Wilson observed, highlighting how the key value of AI in factories is turning huge volumes of telemetry into useful, prioritised actions for factory managers.
While the industry has “used simulation for many years… I don't think we've really got full use of digital twins yet, but that’s going to be coming through
The practical effect is that AI lowers the barriers to entry for more sophisticated automation. Where ten years ago a plant would need an army of integrators to get a new robot cell running, now tools can suggest optimal settings and point to issues before they become failures. More fundamentally, AI is enabling some applications that historically proved hard to automate. Where variability, part compliance or tight tolerances previously defeated automation, increased sensing, vision and algorithmic adaptation now make a subset of those tasks tractable. However, Wilson cautioned against inflated expectations as many AI projects do not deliver long-term value, and automotive teams remain focused on incremental, proven improvements that demonstrably pay off in uptime, throughput or lifecycle cost.
If AI is the intelligence, digital twins and simulation are the maps that guide deployment. Simulation has long been used in design and layout, but the fidelity and ambition of digital models are increasing. Wilson noted that while the industry has “used simulation for many years… I don't think we've really got full use of digital twins yet, but that’s going to be coming through.” When applied well, digital twins allow teams to validate reachability, sequencing and material flows before committing steel and cabling. They let planners test “what-if” scenarios, optimise layouts for ergonomic and throughput trade-offs, and compress design cycles through automated optimisation routines. The payoff is tangible: fewer commissioning surprises, faster ramp-up and better ability to predict maintenance windows or the operational consequences of a layout change.
“The more flexibility you add to a facility, the more expensive it is...
Foundations first: Why data readiness determines digital payback
Nevertheless, the ability to exploit digital twins depends on foundational readiness. Not every plant is instrumented or networked sufficiently to feed a live twin. Where instrumentation, data governance and connectivity are mature, digital tools yield the greatest returns, and the combination of simulation plus AI speeds design iterations. Wilson’s view that digital techniques will accelerate both design and operation – “I think that's one of those technologies where it's now reasonably well proven…they'll start to use those technologies to help them design the lines and prove the lines out and then achieve better performance going forward” – points to a future where digital and physical systems operate in tighter feedback loops.
The promise of software-driven flexibility often come up against the economics of physical tooling. The notion of truly modular manufacturing – small, agile factories that can be reconfigured rapidly to serve local demand – has been attractive for years. But Wilson argued that the cost and complexity of such approaches mean they remain niche. “The more flexibility you add to a facility, the more expensive it is,” he said, recalling examples from the industry’s past where servo-driven tooling enabled multi-model lines but proved prohibitively costly. He noted that while modularity will increase over time, most OEMs will prefer a middle path: keep high-volume core processes relatively fixed while building modularity where it yields clear ROI, such as late-stage configuration cells or intralogistics modules. The ARRIVAL experiment – small, flexible factories built around modular automation – was singled out as an instructive attempt that “hasn't really taken off,” and Wilson suggested that few others have fully pursued that path because the capital and complexity burden is substantial.
Nowhere are the limits of automation more visible than in trim and final assembly. These areas remain heavily manual for several reasons: parts are often soft, compliant or highly variable; operations occur in constrained spaces; and the risk of damaging expensive interior components is high. Wilson explained that automotive historically solved variability well in body-in-white operations, which made those areas amenable to automation. Trim and final, however, still contain many tasks where repeatability is hard to achieve. “It’s less apparent in automotive purely because automotive has been very good at reducing variability… When you look at some other industry sectors, for example, food, it's very difficult for them to achieve that kind of repeatability,” he said, stressing that automotive’s comparative advantage in standardisation has not erased the fundamental challenges of soft goods and wiring looms.
When they put in a new production line, the older robots might be taken off some of the more critical applications and moved to other applications where the performance wasn't quite as critical
The practical industry response has therefore been targeted and complementary rather than wholesale replacement of human labour. Companies invest heavily in intralogistics, sequencing and automated delivery of variants to line side; they deploy assistive devices and collaborative tooling to reduce manual strain; and they apply vision, force sensing and AI selectively to tasks where reliability can be proven. As Wilson observed, “there's a lot of work happening in that space” of line-side delivery and logistics, and the balance – between what should remain manual and what can realistically be automated – tilts toward pragmatic, incremental change.
Extending automation lifecycles through data
Capital constraints are another important force shaping decisions. With major investments tied up in electrification and other strategic programs, many manufacturers are reluctant to fund widespread capital replacement. Instead, they extend the life of existing robots through redeployment, retrofitting and better lifecycle management. Wilson described a common pattern: “When they put in a new production line, the older robots might be taken off some of the more critical applications and moved to other applications where the performance wasn't quite as critical.” This reuse strategy, combined with targeted upgrades – new controllers, vision systems, end-of-arm tooling – lets plants extract more value from installed assets without the expense of full replacements.
Digital monitoring underpins much of this lifecycle extension. Improved telemetry – cycle times, current draw, positional accuracy, motion profiles – allows maintenance teams to spot degradation before it causes downtime. “You can start monitoring the performance of the robots on an individual basis… and using AI to analyse it and present it as actionable insight,” Wilson noted. That transition from time-based maintenance to condition-based maintenance reduces unplanned stoppages and optimises the use of constrained maintenance budgets. But he also stressed that data alone is insufficient; plants need governance and analytics to convert telemetry into prioritised maintenance actions. Where those elements are in place, older robots can be kept in service longer with confidence; where they are not, the data may exist, but its value is unrealised.
One of the most headline-grabbing topics in recent months – the rise of humanoid robots – received a cautious verdict in Wilson’s view. He expressed scepticism about humanoids’ immediate role in mainstream automotive production. Battery life, safety and cost remain limiting factors. “Safety to me is the biggest problem as yet that has not been addressed at all,” he said, pointing out that many current humanoids are not inherently safe in the way industrial collaborative devices can be certified. “If you take the power off a humanoid, it falls over,” he observed, and because a falling machine the size of a human would present an unacceptable hazard, most deployments have been fenced off. That isolation defeats the promise of humanoids as collaborative teammates and raises the question of why a general-purpose, expensive humanoid would be chosen over a specialized, reliable, and cheaper industrial solution. “If you wanted to automate that task, you could probably have done it more cheaply using more traditional automation,” Wilson concluded.
He did, however, allow room for niche use cases. If costs fall significantly and safety and autonomy improve, humanoids could make economic sense in certain unstructured tasks, in field service settings or where a single general-purpose platform replaces many specialized devices. For now, though, industrial robots, AMRs and task-specific automation remain the practical backbone of factories. “The robots themselves are inherently flexible,” Wilson said, adding a note of balance: “It's the things around the robots that actually make the systems less adaptable.” Tooling, fixtures and the physical layout – not the arms – are often the points of rigidity.
A pragmatic future for automotive automation
Taken together, the picture that emerges is one of pragmatic incrementalism. The most impactful opportunities are not single grand breakthroughs but combinations: AI-assisted programming that reduces integration time, digital twins that lower commissioning risk, condition monitoring that lengthens asset life and modular intralogistics that reduce human touchpoints and improve sequencing. OEMs are less willing to fund speculative platform bets; they prefer proven technologies with clear ROI. “People invest more proven technologies rather than developing technologies necessarily,” Wilson observed, reflecting a broader shift in capital discipline and risk appetite.
That pragmatism does not mean the future is dull. It means that progress will likely be measurable and cumulative. Digital foundations – instrumentation, network infrastructure, data governance – are the critical enablers. Once they are in place, AI and simulation can be applied where they return the most value: reducing downtime, speeding changeovers, enabling targeted automation in previously challenging tasks and improving design cycles for new production lines. The result will be factories that look much the same to a casual observer but operate with higher agility, lower unplanned downtime and better-informed decision-making.
In Wilson’s words, automation in automotive manufacturing’s next phase will be defined by many background changes that “will change it going forward.” Those background changes – AI quietly optimising trajectories, digital twins proving layouts before steel is cut, analytics spotting wear before failure – are the engines of steady improvement. The industry’s role now is to be disciplined about where to apply them, to prioritise upgrades that improve lifecycle economics, and to combine human craftsmanship with targeted automation where it makes sense. The path forward is not a single disruptive leap but a series of calibrated steps that together reshape how vehicles are made.
