Tire Builders

Tire builders today mostly operate machines to build tires rather than doing everything by hand ^[1]. For example, modern factories use specialized equipment that automatically winds rubber plies and treads onto a drum — a job a worker once did with pedals and rollers. In practice a worker sets up the materials and starts the machine, instead of hand-cranking the roller. (BLS confirms that Tire Builders “operate machines” to do the assembly ^[1].) Quality‐control is where AI is gaining foothold.

Companies now use camera-based systems and AI to check tires. One German company’s 3D scanner (TireTech) can automatically inspect up to 360 tires per hour to sort them for retreading or recycling ^{[2] [2]}. Likewise, Michelin uses an AI vision system (called IRIS) to highlight possible defects on each tire.

However, Michelin still has people visually check every tire: the AI flags issues, then a human makes the final call ^[3]. In short, machines already handle much of the starting/rolling and winding steps, while AI tools are beginning to help with inspecting. But even with AI, humans remain in charge of final quality decisions ^{[3] [2]}.

The rate of AI adoption in tire building depends on cost, need, and trust. Big tire companies have started using AI because it can pay off: for example, Michelin reports that its AI projects now save over 50 million euros a year ^[3]. This high return motivates investment in new tools.

On the other hand, building a fully automated tire line is expensive, so smaller plants or retread shops may move slowly. If labor is relatively cheap, companies might keep manual steps. Socially and ethically, the industry stresses that AI is a helper, not a replacement.

Michelin’s leaders emphasize “AI will act as a copilot, combining its capabilities with human skills” so that human know-how stays at the center ^{[3] [3]}. Workers are still needed for tricky judgments – for instance, deciding if a scar or puncture is fixable. Overall, businesses press for efficiency and safety (AI can improve consistency), but they also value trained workers’ judgment.

This co-operative approach means adoption will likely grow gradually – tools assist people rather than take over entirely ^{[3] [3]}.