2026 and the knowledge capture race reshaping auto production

As 2026, automotive producers around the world are already peering off into the distance to try and predict what’s coming. And 2025 was full of signals. Across automotive manufacturing plants in Europe and North America, tens of thousands of automotive workers are heading for retirement. More than 300 technicians at Toyota Motor Manufacturing UK's Burnaston facility alone are approaching retirement.

At countless other sites, the story repeats itself with numbing predictability. Engineers who commissioned production lines in the 1980s, who fine-tuned processes through decades of iteration, who hold entire manufacturing philosophies in their heads rather than in manuals, are leaving. And with them goes something that, without intervention - may be irreplaceable.

2025: The year the factory floor outpaced the C-suite

The year 2025 delivered an uncomfortable truth for automotive production. Success belonged not to those with the most sophisticated digital architecture but to manufacturers willing to deploy pragmatic solutions at speed.

On one side, the accelerating march towards electrification and digitisation demands entirely new competencies in high-voltage engineering, robotics, and software integration. On the other, manufacturers develop sophisticated systems to extract, encode, and transfer the tacit knowledge that veterans carry, transforming institutional memory into digital intelligence accessible to the next generation.

The stakes could scarcely be higher. By year's end, expect manufacturers to report either breakthrough successes in knowledge transfer programmes or sobering acknowledgments of expertise permanently lost. The companies that crack this challenge will likely gain competitive advantages measured not in months but years over rivals who fail to act decisively.

Encoding the unwritten manual

At October's Automotive Manufacturing North America conference (AMNA), industry leaders articulated strategies that will likely define 2026's approach to workforce development. The shift to electric vehicles and digital manufacturing creates not merely a skills gap but a knowledge chasm. Legacy manufacturers face what experts term open heart surgery on brownfield sites, attempting to transform century-old facilities into flexible EV production hubs without stopping output.

Conference speakers advocated upskilling programmes, design thinking, catalyst teams, and approaches to capture retiring experts' knowledge into models and large language models to close gaps. This represents a fundamental shift in thinking. Workers edify the artificial intelligence systems rather than the reverse. The technology serves as a vessel for human expertise, not its replacement.

Stephen Heirene, industry consultant from Rockwell Automation, emphasises this philosophy when discussing Toyota's hybrid apprenticeship programme developed in partnership with his company and Derby College. "Training must reflect real world applications," Heirene states. "It doesn't help learners if equipment is decades out of date. The upgrade ensured that the tactile experience resembled what they will encounter in working factories."

Toyota's programme combines two years of classroom training on control systems and simulation software with behavioural competencies, recently upgrading to current Rockwell equipment. The investment reflects an understanding that bridging legacy manufacturing with technologies like electrification and autonomous vehicles requires hands-on experience with modern systems whilst simultaneously capturing the diagnostic intuition that develops over years of intimate familiarity with production processes.

Watch for announcements throughout 2026 of similar partnerships between manufacturers, automation suppliers, and educational institutions. The Toyota-Rockwell model provides a template, but expect variations tailored to regional labour markets and specific production technologies. Manufacturers operating multiple facilities may establish internal academies that standardise knowledge transfer across sites, creating what amounts to corporate universities focused exclusively on manufacturing competencies. The question is not whether others will follow Toyota's lead but how quickly they can scale similar programmes before the retirement wave crests.

Battery production's mounting complexity

The complexity extends beyond installing new equipment. Battery pack assembly alone presents formidable technical challenges that highlight why knowledge transfer proves so critical. Riddhi Padariya, an industrial automation expert with experience at Tesla, describes bottlenecks that will likely persist into 2026 and beyond as manufacturers scale production.

First comes the logistical puzzle of delivering millions of battery cells weekly to assembly stations in vertically integrated factories without damage. Then the technical challenge of cell sorting to prevent mixed variants on conveyors. These issues were present at Tesla, and as producers scale, they will be persistent throughout 2026. "We had to figure out a way to divert the numerous battery cells coming into the battery pack lines from the battery manufacturing part of the factory," Padariya explains.

When it comes to batteries, the precision required in thermal management and potting processes presents perhaps the greatest challenge. "We basically want to prevent electrolytes coming out and shorting positive and negative terminals of the battery," Padariya notes. "Hence it could do major damage if that happens on the road." The solution demands fundamental rethinking of factory layouts to allow thermal processing without pausing production lines.

Padariya observes that curing time optimisation improves through iterative learning. "When we lay out all the structure for the multiple stations where the modules need to travel after they have been potted or cured, through iterations and frequent revamping of stations where the module would get processed and would get staged, that is definitely going better." She notes far fewer problems at Tesla's Austin factory compared to earlier facilities.

Yet Western markets currently produce lower volumes than China, making battery pack assembly manageable at present rates but problematic for scaling to match internal combustion engine production volumes in the millions. As 2026 progresses, this scaling challenge will test whether manufacturers can apply captured knowledge fast enough to accelerate the learning curve.

Rockwell's Stephen Heirene on bringing future mobility to factory floor training

Rockwell Automation's Stephen Heirene explains how the company is helping automotive manufacturers bridge the gap between legacy systems and digital transformation through training academies that prepare engineers for the realities of modern production.

Expect battery production bottlenecks to generate headlines throughout 2026 as legacy manufacturers attempt rapid scaling. Those who successfully transfer expertise from pilot lines to volume production will likely announce capacity expansions by mid-year. Those who struggle may quietly delay electrification targets, blaming supply chain constraints rather than admitting internal capability gaps.

The difference between winners and laggards will increasingly hinge not on capital investment but on how effectively they deploy experienced engineers to mentor new teams whilst simultaneously encoding their problem-solving approaches into training systems. BMW's recent factory launch at Debrecen provides an early test case. Others will follow, and their success rates will reveal which manufacturers truly mastered the knowledge transfer challenge.

The resistance factor and the incentive answer

Technology and training programmes alone prove insufficient without addressing human concerns. Digital transformation in manufacturing consistently encounters what researchers delicately term change management challenges and what plant managers more bluntly describe as pushback. AMNA conference participants highlighted strategies to overcome this through training, clear incentives, leadership alignment, and practical demonstrations of benefit. What manufacturers call the what's in it for you approach.

Panels from Stellantis, General Motors, and Bosch emphasised mixing digital tools with lean practices, noting that digital tools increase the speed of learning and reduce variation, but the human problem-solving layer remains essential. This represents a departure from earlier waves of automation that often treated workers as obstacles to be engineered around rather than assets to be enhanced.

The distinction matters as 2026 unfolds. Manufacturers who centralise the human element in digitisation and automation strategies will likely outperform competitors who view technology deployment as purely an engineering challenge. The conference drove home that digital transformation in manufacturing relies heavily on effectively upskilling the workforce and capturing tacit knowledge through structured programmes.

Look for a bifurcation in the industry as the year progresses. Plants where leadership successfully demonstrates tangible benefits of digital tools will likely report accelerating adoption rates and rising productivity metrics. Facilities where implementation feels imposed rather than collaborative may experience the opposite: passive resistance, workarounds that undermine digital systems, and ultimately, disappointing returns on technology investments.

AMS Talks 2025-2026: The Human & The Machine in Carmaking

In our final livestream of 2025, we explored how automotive manufacturers are abandoning top-down digitalisation for agile, shop-floor solutions. What we discovered across factory visits is striking: transforming brownfield sites into EV hubs succeeds only when you empower people, not just deploy technology. What awaits in 2026?

The financial results announced in late 2026 will probably reveal which approach prevailed. Expect analysts to begin correlating workforce satisfaction scores with digital transformation success rates, recognising what shop floor veterans have long known: that technology only works when people want it to work. Manufacturers who crack the incentive puzzle will find their digital investments compounding - while those who ignore the human equation will find expensive systems underutilised and knowledge transfer programmes failing quietly.

Structural advantages and disadvantages

The urgency intensifies when viewed through the lens of global competition. Legacy manufacturers face structural disadvantages against new entrants, particularly from China. The edge enjoyed by companies like Nio and BYD stems not from superior robotics but from collapsed development processes and rigorous design-for-manufacturing principles. Where legacy producers operate on five to seven year model cycles, Chinese manufacturers reportedly deliver new EV designs in approximately half that time. Two factors enable this velocity: vertical integration and digital-first agility.

This creates an uncomfortable reality for Western manufacturers. They must simultaneously transform brownfield sites whilst training workers in new technologies whilst competing against companies built from scratch around digital manufacturing principles. The battery production scaling challenges Padariya describes underscore this structural imbalance. Chinese manufacturers benefit from purpose-built facilities designed around EV production, whilst Western rivals attempt to retrofit existing plants without stopping current model production.

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Yet Western manufacturers possess an asset Chinese competitors cannot easily replicate: decades of accumulated manufacturing wisdom in quality control, supply chain management, and continuous improvement methodologies. The challenge lies in encoding this expertise fast enough to combine legacy knowledge with digital capabilities, creating hybrid systems that leverage both experience and innovation.

By the fourth quarter of 2026, expect market share data to reveal whether Western manufacturers successfully deployed their knowledge advantage or whether speed trumped experience. Chinese manufacturers will likely announce aggressive expansion into European markets, forcing legacy producers to demonstrate that their brownfield transformations can match greenfield efficiency. Suppliers caught between these competing forces may choose sides, with automation providers either partnering with Western manufacturers on knowledge transfer programmes or supplying turnkey solutions to Chinese competitors building new capacity.

The partnerships announced throughout 2026 will signal where industry insiders believe the competitive advantage lies. Watch particularly for joint ventures between Western and Asian manufacturers, which may indicate that neither side believes it can succeed alone. The knowledge possessed by retiring Western technicians may become a tradable asset, with experienced engineers commanding premium consulting rates to transfer expertise to emerging manufacturers globally.

The preservation imperative

As 2026 progresses, the window for capturing institutional knowledge narrows. The 300 technicians approaching retirement at Burnaston represent not merely a staffing challenge but a knowledge crisis replicated across the industry. Their expertise in troubleshooting production anomalies, optimising processes through experience rather than algorithm, understanding the subtle interplay between equipment and material, this proves difficult to replace simply by hiring recent graduates, however well trained.

Yet the industry now possesses tools previous generations lacked. Large language models can absorb and query vast bodies of procedural knowledge. Digital twins allow experienced workers to demonstrate problem-solving approaches in virtual environments that capture their decision-making logic. Catalyst teams can work alongside retiring experts to document not just what they do but why they do it, preserving the reasoning that transforms information into wisdom.

The manufacturers who navigate 2026 successfully will be those who recognise that digitisation and workforce development are not competing priorities but complementary necessities. That technology serves as an enabler whilst culture, collaboration, and empowerment determine whether transformation succeeds or stalls. That the race is not between human and machine but against time itself, to capture and transfer knowledge before it disperses into retirement.

The automotive industry has weathered numerous transformations over its century-long history. The shift to electric vehicles and digital manufacturing may prove the most profound yet, not because the technology is more complex but because it arrives at the precise moment when the industry's accumulated wisdom approaches its expiration date. How manufacturers respond to this dual challenge throughout 2026, embracing new capabilities whilst systematically preserving hard-won expertise, will determine which companies lead the next era of automotive production and which become cautionary tales of knowledge lost.

The difference between success and failure may ultimately rest on a simple recognition: that the smartest factories in 2026 will be those that treat their retiring workers not as problems to be solved but as libraries to be preserved, not through passive documentation but through active encoding into the very systems that will train their replacements. The technology exists. The methodology develops. Only time remains in question.