Saturday, 5 September 2026
Artificial intelligence is spreading rapidly across manufacturing, but some of the companies deploying it most aggressively are discovering that the best place to start is not with the technology. Instead, manufacturers are identifying costly operational problems, simplifying the underlying process and involving factory workers before deciding whether AI is necessary at all. That was one of the clearest conclusions from a manufacturing panel at Ai4 2026 in Las Vegas, where executives discussed applications ranging from equipment maintenance and computer vision to production logistics and internal software development. Rather than describing a future dominated by fully autonomous factories, the discussion showed how AI is being introduced incrementally into existing industrial processes where downtime, quality problems, inventory, maintenance and inefficient workflows can be measured financially.
One example comes from Viaduct, the Silicon Valley AI company acquired by Japan’s Sumitomo Rubber Industries in 2025. Viaduct CEO David Hallac described the deployment of an AI-assisted maintenance system at Sumitomo Rubber’s Miyazaki tyre factory in Japan, where equipment failures could disrupt production while maintenance technicians searched historical records for solutions. The system brings together years of maintenance information and recommends potential repairs when problems occur, effectively turning accumulated factory knowledge into a resource that technicians can access during maintenance work. Viaduct says the implementation has reduced repair time significantly, while the broader significance extends beyond a single factory because Sumitomo Rubber sees predictive maintenance and AI-driven asset management as capabilities that can eventually be deployed across wider manufacturing operations.
The industrial value of such systems comes partly from preserving knowledge that traditionally remains inside experienced employees’ heads. Manufacturing plants frequently depend on technicians who have spent decades learning how individual machines behave, which sounds indicate developing problems and which sequence of events usually precedes a failure. Some of that information exists in maintenance systems, but much of it can be contained in shorthand notes, fragmented databases or individual experience. AI potentially changes that relationship by combining maintenance histories, sensor information, manuals, images, repair records and employee knowledge into systems that can identify recurring patterns. A technician receiving an alert can then be shown not simply that a component may fail, but which previous cases displayed similar behaviour and what actions resolved them.
That type of evidence is important because industrial workers are unlikely to trust a system simply because an algorithm tells them something is wrong. An experienced technician may reasonably ignore a warning if a machine appears to be operating normally. A system becomes more persuasive when it can demonstrate that several comparable machines showed the same combination of signals before a particular failure occurred. The technology then supplements the technician’s judgement rather than asking the employee to surrender that judgement to a black box.
Kaspar Companies, a family-owned Texas industrial group with businesses including truck beds, outdoor products and precious metals, offered a different example. The company has been experimenting rapidly with internally developed applications, but Peter Robinson said the organisation initially made the mistake of looking for places to deploy AI rather than beginning with operational problems. The company responded by reconnecting its AI programme with its existing continuous-improvement system. Factory teams identify a specific problem, examine the existing workflow and remove unnecessary steps before considering a technological solution. Employees performing the work participate directly in that process, meaning the people eventually expected to use the application are involved before it is developed.
One application involves computer vision at Kaspar’s Bedrock truck-bed operation. Different truck beds require different hardware packages when they are delivered to distributors for installation. Missing or incorrect components can cause problems after the original truck bed has already been removed. The company developed a camera-based system that identifies the truck bed and checks whether the correct hardware has been included before it leaves production. Another project began with excess finished inventory. Customers order combinations of truck beds that need to fit onto outbound trucks, but different product dimensions made it difficult to determine the optimal load in advance. Kaspar developed a system that helps optimise the combination before production and shipment.
An unexpected commercial benefit emerged from that second project. The same system could identify situations where additional truck beds could fit onto an order without requiring another vehicle. Customer-service employees could then offer customers the opportunity to add products to the shipment, turning a project initially intended to reduce inventory into a potential sales tool. That example illustrates why the return from manufacturing AI does not always appear where companies initially expect it. A project designed to reduce waste might increase revenue, while predictive maintenance could improve quality as well as reduce downtime. Better production information can also influence warranty costs, customer satisfaction or inventory requirements elsewhere in the organisation.
It also demonstrates why manufacturers need to remain close to factory operations when evaluating results. Some secondary benefits are difficult to predict from an initial business case and may only become visible after operators begin using the system. AI development therefore cannot be separated entirely from the physical environment in which the technology operates. Engineers need feedback from the people running machines, repairing equipment, loading trucks and inspecting products. The panel repeatedly returned to the idea that the most successful projects tend to be those built around an obvious operational pain point rather than around a desire to showcase AI.
The discussion also challenged the assumption that every industrial problem requires artificial intelligence. Kaspar said some of its most valuable internally developed solutions do not contain AI in their final operation, even though AI tools may have helped developers build them faster. The relevant question is therefore not whether a manufacturer can attach AI to a process, but what is the simplest reliable technology capable of solving the problem. That distinction is becoming increasingly important as generative AI makes software development faster and cheaper.
Manufacturers can potentially build specialised internal tools that previously would have required considerably more engineering resources. AI therefore has two roles in industrial transformation: it can become part of the application operating on the factory floor, or it can simply help engineers develop conventional software much more quickly. This could alter the economics of industrial software because manufacturers have historically purchased broad enterprise systems and adapted their processes around them partly because developing specialised applications internally was expensive. AI-assisted development lowers that barrier and may make it easier for small technical teams to create tools around individual production, inventory, quality or maintenance problems.
Kaspar’s approach illustrates this change. Rather than assembling a large AI department, the company has maintained a small development team and pushed it to experiment rapidly. Robinson said dozens of projects have been attempted, while acknowledging that a significant proportion have not generated measurable value. The failures are treated partly as learning exercises, with unsuccessful applications stopped rather than continually receiving resources simply because development has already begun. That willingness to abandon projects could become one of the more important disciplines in corporate AI adoption.
The technology is developing so quickly that an application which is difficult or uneconomic today may become straightforward several months later. Manufacturers therefore need to distinguish between problems worth solving immediately and projects that should be paused until the technology improves or the business is ready to adopt them. Speed still matters once the correct problem has been identified, but the panel’s message was that companies should spend considerable effort understanding the problem before development begins and then move quickly during execution.
Workforce strategy is equally important. Fear that AI will eliminate manufacturing jobs can undermine adoption before a system reaches the factory floor. Kaspar has attempted to connect its AI strategy with an existing policy of moving employees affected by productivity improvements into other roles rather than treating operational improvement primarily as a headcount-reduction programme. Employees can therefore see automation as part of continuous improvement and capacity expansion rather than an immediate threat to employment. Whether every manufacturer can adopt that approach will depend on its economics, workforce and competitive pressures, but the broader lesson remains that operators are more likely to support systems they helped design and that solve problems they experience personally.
This human factor may explain why the idea of the fully autonomous smart factory remains less important in practice than hundreds of smaller improvements. AI can help technicians repair equipment faster, identify defects, optimise shipments, retrieve production information and build software, but these applications still depend heavily on people understanding the process and deciding what outcome is valuable. Technology imposed from a central office without sufficient understanding of factory work is more likely to encounter resistance than a system developed together with the people expected to use it.
Robotics could extend that transformation further. Robinson expects physical automation and humanoid robots to become a much larger part of the manufacturing discussion as AI capabilities improve. That would move AI from analysing and coordinating industrial processes towards physically performing more tasks inside factories, potentially creating another major investment cycle in production facilities. The implications extend beyond manufacturing technology itself because factories adopting more robotics, computer vision, predictive maintenance and AI-driven production systems will require different digital infrastructure, connectivity, power capacity and technical skills.
Industrial property capable of supporting advanced automation may therefore become increasingly differentiated from older facilities that lack the electrical, data and physical infrastructure required for modern production. The manufacturing AI market may also become increasingly specialised, with broad horizontal AI platforms consolidating around a relatively small group of large technology providers while specialist companies concentrate on individual industrial problems. Manufacturers may consequently rely on common underlying models while building or purchasing specialised applications for maintenance, quality control, production planning, logistics and other operational functions.
The strongest lesson from the panel, however, was considerably simpler. AI should not become the starting point for industrial transformation. Manufacturers first need to understand where production is losing time, money, capacity or quality. They then need to simplify the process, involve the people performing the work and choose the simplest technology capable of solving the problem. Sometimes that solution will involve sophisticated predictive models, computer vision or AI agents. Sometimes AI will merely help engineers build ordinary software faster. And sometimes the correct answer will contain no artificial intelligence at all.
For manufacturers under pressure to demonstrate that their AI investments produce real economic value, that distinction could become increasingly important. The smartest factory may ultimately not be the one using the most AI. It may be the one that has become best at identifying exactly where AI is worth using.
Source: CIJ.World Research & Analysis Team
