人工智慧在機器製造的應用：2026 年

本報告調查了人工智慧在機械製造業的應用現狀，總結了按機器類別和生命週期等不同類別分類的應用、應用的推進因素和挑戰，以及主要製造商的案例研究。

說明要點

• 機器製造商在設計、內部營運和生產以及售後服務中，在哪些方面以及如何將人工智慧融入其中？
• 在不同的機器類別和生命週期階段中，哪些人工智慧應用案例會被優先考慮？
• 目前人工智慧在機械製造業的應用成熟度如何？企業在哪些應用情境中處於規劃、試點或規模化階段？
• 人工智慧在哪種類型的機器中最為先進？
• 機械製造商在擴大人工智慧應用方面面臨哪些挑戰？
• 哪些趨勢正在塑造機械製造業的未來？
• 目前哪些公司在機械製造業人工智慧應用方面處於領先地位？
• 目前人工智慧在各機械產業的應用成熟度如何？

提及的公司

• ABB Robotics
• Applied Materials
• Atlas Copco
• Buhler
• Caterpillar
• DMG MORI
• Daikin
• ENGEL
• Emerson Automation Solutions
• GANUC
• Grundfos
• HOMAG (Durr 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

概述：經濟規模和市場背景

2024年，該產業的產值約為3.26兆歐元。相較之下，該產業的總產值相當於德國同年GDP的76%，德國GDP約4.33兆歐元。中國持續維持生產領先地位，約佔全球總產量的三分之一。

人工智慧實施現狀

人工智慧已超越實驗階段，成為絕大多數產業的標準工具。該行業已超越簡單的概念驗證（PoC）階段，超過一半的受訪公司已在其所有營運環節或公司範圍內全面部署人工智慧解決方案。目前，亞太地區在人工智慧應用方面最為先進，其次是北美和歐洲。

營運重點和障礙

• 機械製造商主要利用人工智慧來提高特定領域的效率並解決人手不足。超過90%的受訪者將內部品管和缺陷檢測視為首要任務。在工程領域，約90%的公司優先考慮設計自動化，尤其是在管理模擬階段產生的大量資料方面。在生產領域，預測性維護是最常見的應用場景，目前在超過一半的受訪製造企業中都有實施。
• 對許多組織而言，擴大這些工具的應用規模仍面臨挑戰。超過一半的業內人士認為高昂的初始成本是主要障礙。此外，約40%的公司目前正苦於缺乏內部軟體人才和資料基礎設施不足。對於大型企業而言，資料品質不佳是最常見的障礙；而對於中小企業而言，將人工智慧與舊有系統整合的成本往往是應用的主要障礙。

目錄

第1章執行摘要

第2章：引言

• 引言：章節概述和要點
• 什麼是機械製造？有哪些類型的機械？
• 機械製造的主要生命週期階段
• 機械製造業的經濟重要性
• 機械製造商在當今市場面臨的挑戰
• 未來願景：面向客戶的AI驅動型自動化資料流
• 機械製造企業組織管理中的優先事項（第二部分）
• 案例研究：迪爾公司積極投資人工智慧，涵蓋其整個價值鏈（共4部分）
• 本報告主要基於對機械製造商的調查。

第3章 分析師觀點：人工智慧的關鍵趨勢與挑戰

• 分析師觀點：人工智慧的關鍵趨勢和挑戰：章節總結和要點
• 趨勢
• 任務

第4章概述：人工智慧在機械製造業的應用

• 將人工智慧引入機器製造：章節概述和要點
• 人工智慧在機器製造的應用（第二部分）
• 通用技術採納與人工智慧的作用（三部分系列）
• 人工智慧應用案例優先排序（第二部分）
• 人工智慧技術應用障礙（共四部分）
• 機器製造商在實施人工智慧方面面臨許多障礙

第5章：人工智慧在機械設計與工程的應用

• 人工智慧在機械設計與工程的應用：章節概述與要點
• 人工智慧對機械設計的影響（三部曲）
• 人工智慧在機械設計的應用現況（第三部分）
• 生成式人工智慧在機械設計的應用現況（三篇系列文章）
• 範例：利用 Krones 的人工智慧加速機器設計和配置（共 2 部分）
• 例如：設計和工程領域中生成式人工智慧解決方案的提供趨勢。

第6章：人工智慧在機器製造的應用

• 人工智慧在機器製造中的應用：章節概述和要點
• 人工智慧對製造業重大挑戰的影響（第二部分）
• 人工智慧在製造業的應用現況（三篇系列文章）
• 例如：DMG MORI 在工廠設定中使用了人工智慧。

第7章：人工智慧在售後服務、服務和智慧機器的應用

• 售後服務、服務以及智慧機器中的人工智慧：章節概述和要點
• 人工智慧與智慧機器的典型應用
• 人工智慧領導企業對智慧人工智慧產品和現場服務的看法。
• 人工智慧在售後服務、服務和智慧機器中的作用
• 售後服務中人工智慧應用現狀
• 人工智慧在維護和供應鏈中的實施階段（第二部分）

第8章：人工智慧在機械產業的應用先驅

• 人工智慧在機械產業應用的先驅：章節概要和要點
• 人工智慧在機器製造領域應用的先驅：概述（兩部分）
• 受訪者對機械製造商中人工智慧實施領先公司的評價
• 人工智慧先鋒1：ABB
• 人工智慧實施先鋒2：應用材料公司
• 人工智慧應用先驅者之三：Caterpillar
• 人工智慧應用先驅者之四：小松
• 人工智慧應用領域的五位先驅：Kone

第9章：人工智慧在特定機械領域的詳細分析

• 人工智慧在特定機械領域的深入分析：章節概述和要點
• 詳細分析 1：工具機產業（第四部分）
• 詳細分析 2：機器人技術（9 個單元）
• 詳細分析3：工程機械和礦山機械（三部分系列）

第10章：調查方法與市場定義

A 121-page report on how machinery companies are adopting AI across design, manufacturing, and smart machines.

Questions answered

• 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?

Companies mentioned

• ABB Robotics
• Applied Materials
• Atlas Copco
• Buhler
• Caterpillar
• DMG MORI
• Daikin
• ENGEL
• Emerson Automation Solutions
• GANUC
• Grundfos
• HOMAG (Durr 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

About the report

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.

Table of Contents

1. Executive summary

2. Introduction

• Introduction: Chapter overview and key take aways
• Machine building: What it is and what types of machines exist
• Key lifecycle phases in machine building
• Economic relevance of the machine builder industry
• The challenges machinery companies observe in today’s market
• The future vision: One Al- enabled automated data flow for customers
• Organizational priorities for machinery companies (2 parts)
• Case study: Deere & Co. is strongly investing in AI across the value chain (4 parts)
• This report is mostly based on a survey of machinery companies

3. Analyst view: Key Al- related trends & challenges

• Analyst view: Key Al- related trends & challenges: Chapter overview and key takeaways
• Trend 1
• Trend 2
• Trend 3
• Trend 4
• Trend 5
• Challenge 1
• Challenge 2
• Challenge 3
• Challenge 4

4. Overview: AI adoption in machinery

• Al adoption in machine building: Chapter overview and key takeaways
• Al adoption in machine building (2 parts)
• General technology adoption and role of AI (3 parts)
• Prioritization of AI use cases (2 parts)
• Barriers when adopting AI technologies (4 parts)
• Commentary by machinery companies on selected AI adoption barriers

5. AI in machine design and engineering

• Al in machine design and engineering: Chapter overview and key takeaways
• Al impact in machine design (3 parts)
• Current stage of AI adoption in machine design (3 parts)
• Current stage of Gen AI adoption in machine design (3 parts)
• Example: Krones uses AI to accelerate machine design & configuration (2 parts)
• Example: How vendors are offering Gen AI solutions in design & engineering

6. AI in machine manufacturing

• Al in machine manufacturing: Chapter overview and key takeaways
• Al impact on key manufacturing challenges (2 parts)
• Current stage of AI adoption in manufacturing (3 parts)
• Example: DMG MORI using AI on the factory floor

7. AI in after sales, service and smart machines

• Al in after sales, service and smart machines: Chapter overview and key takeaways
• Typical AI use cases in conjunction with smart machines
• How leading AI companies think about smart AI products & field service
• Role of AI in after sales, service and smart machines
• Current stage of AI adoption in after sales and service
• Stage of AI adoption in maintenance & supply chain (2 parts)

8. Leading AI adopters across machinery industries

• Leading AI adopters across machinery industries: Chapter overview and key takeaways
• Leading adopters of AI in machine builders: Overview (2 parts)
• Leading adopters of AI in machine builders according to respondents
• Leading AI adopter 1: ABB
• Leading AI adopter 2: Applied Materials
• Leading AI adopter 3: Caterpillar
• Leading AI adopter 4: Komatsu
• Leading AI adopter 5: Kone

9. Deep-dive: AI in selected machinery industries

• Deep-dive: AI in selected machinery industries: Chapter overview and key takeaways
• Deep-dive 1: Machine tool industry (4 parts)
• Deep-dive 2: Robotics (9 parts)
• Deep-dive 3: Construction/Mining machinery (3 parts)

10. Methodology & market definitions

• General research methodology
• Methodology for scoring leading AI adopters across machinery industries
• Survey respondents split