AI adoption surges among machine builders, survey finds
Fri, 22nd May 2026 (Today)
IoT Analytics found that 96% of machine builders have begun deploying artificial intelligence in internal operations, based on a survey of 120 senior decision-makers across 22 machine-building subindustries.
More than half of respondents, 55%, had already scaled at least one AI use case across individual operations, sites or the wider business. The figures suggest adoption has moved beyond trial projects for a significant share of the sector, even though deployment remains uneven across machine-building segments.
Cost was the biggest obstacle. Some 54% of machine builders identified high AI costs as a critical barrier, while 43% pointed to insufficient data infrastructure and the same share cited workforce skill gaps.
Those constraints coexist with relatively broad use of AI in both internal processes and customer-facing machine functions. Companies are applying AI in design, maintenance, diagnostics and service activities rather than concentrating on a single area.
Design use
In machine design, AI-based failure prediction was the most widely adopted use case, with 37% of respondents reporting full or partial deployment.
AI-driven component modelling and design for manufacturability followed at 34% each. Their use suggests a focus on improving engineering decisions earlier in the product lifecycle, where machine builders can influence reliability, production efficiency and downstream service demands.
Service focus
Adoption was also pronounced in after-sales and service functions. AI-based predictive maintenance had been deployed by 54% of machine builders, including 18% with full deployment and 36% with partial deployment.
Remote diagnostics was the most adopted individual service use case at 48%, while service workflow automation reached 43%. AI-enhanced augmented reality tools stood at 30%, indicating a smaller but still notable level of use in technician support and field service tasks.
These figures show that many machine builders are directing AI investment toward areas tied to uptime, service efficiency and customer support. For companies that sell and maintain industrial equipment, those functions often provide recurring customer contact and a direct route to operational savings.
Industry split
The survey also highlighted differences between subindustries. Robotics and automation ranked highest for AI adoption, followed by semiconductor manufacturing equipment, construction equipment and mining equipment.
The variation underlines the fragmented nature of machine building. The sector spans manufacturers serving very different end markets, technical requirements and production models, all of which can shape both the value of AI tools and the difficulty of implementing them.
In segments such as robotics and semiconductor equipment, the combination of complex systems, large volumes of operational data and demanding customer requirements may make AI projects easier to justify. Other machine-building segments may face weaker data foundations, lower software intensity or tighter cost constraints.
The findings add to a broader picture of industrial companies adopting AI selectively rather than through uniform transformation programmes. Even where internal use is widespread, many businesses still limit deployment to particular sites, departments or use cases.
That matters because internal experimentation does not always translate into AI features built into machines or software sold to customers. The research suggests some machine builders are making that shift, but progress differs sharply depending on the type of machinery involved and the operational pressures facing each business.
Raghav Kadian, analyst at IoT Analytics, said the market's diversity helps explain the uneven pace of adoption.
"Machine building is not one AI market. It is a set of highly diverse machinery industries with different customers, machines, and business pressures. That is why AI adoption looks very different across the sector. While 96% of surveyed machine builders are already using AI internally in some form, many deployments remain limited to selected use cases or sites. The companies that stand out over the coming years will be those that overcome barriers around cost, data infrastructure, and AI talent, and translate AI into measurable value for both their operations and their customers," he said.