AI Adoption in Machine Building 2026

• Where and how are machine builders adopting AI across design, own operations/production, and after-sales?
• Which AI use cases are being prioritized across machine categories and lifecycle phases?
• How mature is AI deployment in machine building today, and for which use cases are companies in planning, piloting, or scaling phases?
• Which machine types show the strongest AI adoption?
• What challenges are machine builders facing in scaling AI adoption?
• Which trends are shaping the future of machine building?
• Who are the leading adopters of AI in machine building today?
• What is the maturity of AI deployment in the different machinery sectors today?

ABB Robotics

Applied Materials

Atlas Copco

Bühler

Caterpillar

DMG MORI

Daikin

ENGEL

Emerson Automation Solutions

GANUC

Grundfos

HOMAG (Dürr Group)

Heidelberger Druckmaschinen

Heller

Hermle

John Deere

KONE

Kion Group

Komatsu

Mazak

Rolls-Royce

SMS group

Sandvik Coromant

Saurer (Jinsheng Group)

Siemens Energy

Tetra Pak

The AI Adoption in Machine Building Report 2026 is part of IoT Analytics’ ongoing coverage of industrial technology topics. The findings are based on a dedicated survey of industry participants, expert interviews, and first-hand insights gathered from leading trade fairs. The report explores how machine builders are adopting AI across design, production, and after-sales, highlights key use cases, and profiles the technologies and machinery companies driving this shift.

Overview: Economic Weight and Market Context

In 2024, production output for the industry reached approximately €3.26 trillion. To put that into perspective, the sector’s total output is equivalent to 76% of Germany’s gross domestic product, which was roughly €4.33 trillion in the same year. China continues to lead production, accounting for about one-third of the global total.

Current State of AI Deployment

AI has moved past the experimental phase and is now a standard tool for the majority of the sector. The industry is successfully moving beyond simple proofs-of-concept; over half of surveyed companies have already scaled AI solutions across their operations or their entire enterprise. Deployment is currently most advanced in the Asia-Pacific region, followed by North America and Europe.

Operational Priorities and Barriers

• Machine builders are mostly using AI to find specific efficiency gains and address labor shortages. Internal quality control and defect detection are the top priorities for over 90% of respondents. In engineering, about nine out of ten companies prioritize design automation, specifically to manage the massive volumes of data generated during simulation phases. On the production floor, predictive maintenance is the most common use case, currently deployed at more than half of all surveyed manufacturing facilities.
• Scaling these tools remains difficult for many organizations. Over half of the industry points to high upfront costs as a critical barrier. Additionally, about four out of ten companies are currently struggling with a lack of internal software talent and insufficient data infrastructure. For larger firms, poor data quality is the most frequent obstacle, while smaller companies are more likely to be slowed down by the costs of integrating AI with legacy systems.

2026 Technical Shift: Edge Intelligence to Autonomous Agents

The report identifies a move away from fragmented data workflows toward an integrated digital thread. Key shifts in machine architecture include:

• Moving Intelligence to Hardware: Builders are increasingly embedding AI acceleration directly into machine controllers for real-time, low-latency decision-making.
• 3D Machine Vision: There is a clear transition from traditional 2D checks to 3D laser-based scanning systems that compare physical components directly to digital models.
• Engineering Automation: Generative tools are now entering standard workflows to help automate CAD generation and simplify complex robot programming through natural-language interfaces.
• Emerging AI Agents: Early concepts for “AI agents” are being tested. These systems can query technical documentation and telemetry data autonomously to troubleshoot problems or trigger service tickets without an operator.