制造业中的人工智能机遇

2025 Report 01 •The AI opportunity in manufacturing0402 •Adoption, maturity and trust0603 •Advancements for businesses and inividuals0904 •Barriers to wider adoption1205 •Investing in AI, access the future1506 •Four changes to unlock your success1807 •Research methodology2108 •About TeamViewer22 Table of contents (Clickable links) The AI opportunityin manufacturing Chapter 1 The AI opportunityin manufacturing Mei Dent, Chief Product and Technology Officerat TeamViewerencourages these individuals toexercise caution: “Artificial Intelligence (AI) has the power to revolutionisemanufacturing by enhancing efficiency, productivity, andinnovation. For example, by optimising production lines andenabling workers to grow into higher-value roles.” “However, missteps in implementation – such as a lack ofcomprehensive investment, narrow adoption strategies thataren’t tailored to users’ needs, and a failure to account for securityconsiderations – will limit its promise and lead to risk. To staycompetitive, manufacturers must embrace AI’s opportunities whileprioritising a clear understanding of the surrounding challenges. “In the following pages of the AI Opportunity in Manufacturingreport, we investigate the latest uses and perceptions of AI,whether manufacturers are harnessing its full potential, barriersto widespread adoption, and how to overcome these to unlockall of AI’s capabilities. “Read on to discover the attitudes of 1,400 global decisionmakers, including 105 in the manufacturing sector, towardsArtificial Intelligence.” Adoption, maturityand trust more accessible to the IT industry and professionals. Meanwhile,the manufacturing sector, and operational technology decisionmakers (OTDM) working within it, required multimodal capabilities– from image and audio to video processing – which existing toolscouldn’t cater for. This disconnect – between the needs of OTDMsand the capabilities of the tools available to them – made adoptionslower and more complex, reducing the likelihood of personnelseeing themselves as AI experts. This is changing, thanks topurpose-built AI models which are delivering proven outcomes,but it will take time for the manufacturing sector to be on parwith more technologically advanced industries such as ITand healthcare. Adoption, maturity, and trust AI adoption in the manufacturing industry has accelerated over thepast year. This upsurge has been driven by the need to enhanceefficiency, build resilience, and cut costs to stay competitive in theface of global supply chain disruptions and labour shortages. Today,78% of manufacturing decision makers use AI in their jobs at leastweekly, with nearly a third (30%) relying on it daily – a dramaticincrease from just a year ago when only 46% used AI weekly and amere 8% daily. Young workers are expected to lead this charge, especially inmanufacturing. 87% believe younger people have a stronger graspof AI technology, compared to a 75% average across all industries.This is thanks to younger generations having greater exposure todigital technologies and AI concepts, which contrasts with themore traditional skillsets prevalent in the manufacturing sector. As adoption grows, so do perceptions of maturity and confidence.72% of respondents consider their organisations’ AI adoption to bemature. Meanwhile, two-thirds (67%) feel personally competentwhen it comes to using AI. However, fewer manufacturing decisionmakers would call themselves proficient or expert (28%) than theaverage respondent across industries (39%). This reflects thediffering stages and speeds of digitalisation in various sectors. AI adoption in the manufacturingindustry has accelerated overthe past year. This upsurgehas been driven by the needto enhance efficiency, buildresilience, and cut costs. While digital transformation has been a topic of conversation formore than a decade, many industries, including manufacturing,are still relatively early in their digitalisation journeys. This is due toa historical reliance on mechanical and process-driven expertise,rather than advanced digital skills, as well as the complexity ofintegrating innovations, such as AI, with legacy systems. Compounding this, early AI tools lent themselves to text-basedtasks, such as creating content and structuring data, making them Chapter 2 Today, AI is most used within manufacturing for customer supportautomation (28%), data analysis (23%), process automation(19%), supply chain optimisation (19%), as well as more advancedapplications such as inspiration and ideation (19%). This reflectsthe capabilities of AI tools, such as generative design and predictiveanalytics, to support manufacturers in designing innovativeproducts and identifying novel solutions to optimise complexproduction processes. Sophisticated uses of AI also extend to how much manufacturerstrust it to handle with complex tasks. When asked about theirconfidence in AI’s capabilities, respondents indicated trust in AIforforecasting future