2025人工智能在仓储领域中的应用状况研究报告

Global insights from supply chain leaderson AI’s economic and workforce impact – 2025 Contents Theme 6: Dominant methodsand technologies Introduction Key findings From prediction to generation:How AI methods create value•The technologies powering today’ssmart warehouses•Integration: The foundation ofeffective AI Theme 1: Current state of AI/MLadoption •The expanding footprint of AI inwarehouse operations•AI becomes part of the daily workflow•A tool for operational complexity•An enabler of high-impact value Synthesis across themes •Key insights and implications for AI inwarehousing•Recommendations Theme 2: AI/ML investment and ROI Cross-sectional analysis AI investments move from pilot budgetsto proven returns Regional nuances and differences •The state of warehouse automationacross countries•AI/ML budget allocation by country•Barriers to AI and automation adoptionvary by region •Budget allocation and ROI from AI•AI investment drivers•Payback periods reflect the scope oftransformation Theme 3: Current AI/ML implementationchallenges and enablers Industry-specific differences Closing remarks Survey methodology Overcoming challenges to accelerateAI adoption in warehouses •Main barriers•Internal capabilities for AIimplementation•Resources to speed up AI adoption Theme 4: AI/ML impact on theworkforce People at the centre of warehousetransformation•AI raises skills across the board•AI creates new roles Theme 5: Future AI/ML implementationoutlook and prioritiesThe next wave: From prediction todecision•Expanding capabilities and investment Aboutthe survey Year: 2025 Participants:2,000+ experienced supply chainand warehousing professionals. Reach: 21 countries across Europe, North America,Asia-Pacific and Latin America. Industry sectors:Agriculture, automotive,chemicals, construction, consumer goods,e-commerce & retail, energy, food & beverage,government, logistics, manufacturing, pharma,technology, textiles and transport. Company size: Respondents representedcompanies ranging from 100 to over 5,000employees, with the largest share coming fromorganisations with 1,000–4,999 employees. Revenue: Most respondents (32%) representcompanies with annual revenues between$251–999 million, followed by 26% in the$51–250 million range, 19% above $1 billion, 18%between $10–50 million, and 5% under $10 million. IntroductionA data-driven view of AI adoption inwarehouse operations The MIT Intelligent Logistics Systems Lab and Mecalux jointly fielded a global survey ofmore than 2,000 experienced supply chain and warehousing professionals to assess thecurrent state, investment patterns, challenges, workforce effects and outlook for artificialintelligence and machine learning (AI/ML) in warehouse operations. To ensure the findingsreflected organisations with meaningful levels of warehouse activity and technologyadoption, the survey focused on companies with at least 100 employees. Our aim was toestablish a contemporary, evidence-based baseline for AI adoption in warehousing andlogistics. The survey spanned six thematic blocks: current state of adoption; investmentpriorities and return on investment (ROI) considerations; implementation challenges;workforce impacts; future implementation outlook and priorities; and dominant methodsand technologies, as well as questions about respondent demographics. The data reveal a sector in transformation. More than four out of five organisationsincreased AI/ML use in the past year, and most expect budgets to rise further. The typicalpayback period — just two to three years — shows that AI is delivering tangible returns, notspeculative value. Generative AI is emerging as the next frontier in logistics, speeding upprocess design, documentation and decision-making. The human side of automation isalso evolving in positive ways: productivity and job satisfaction are rising together, drivenby new roles, training and upskilling. Warehousing is evolving from automation to intelligence, where data and algorithmscomplement human expertise. The next competitive edge will belong to those who treat AInot as a project, but as an embedded, measurable capability that turns insight into actionfaster. Theme 4:AI/ML impacton the workforce Key findings • Positive workforce trends dominate:productivity up for77.5%oforganisations, job satisfaction up for75.4%, training requirements up for76.4%and workforce size increasedfor55.8%. Theme 1:Current state of AI/MLadoption • AI is creating — not replacing — jobs:AI/ML engineers (60.1%), automationspecialists (58%), process-improvement experts (51.9%) anddata scientists (40%). •Nearly 9 out of 10warehouses nowoperate at automation levels beyondbasic processes. •57.5%of organisations operateat advanced or full automationmaturity; only11.7%remain largelymanual. Theme 5:Future AI/MLimplementation outlookand priorities •Full automation is most commonamong larger firms with higherrevenue, more sites and moreemployees. •92.1%of firms are i