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1、Global insights from supply chain leaders on AIs economic and workforce impact 2025The state of AI in warehousing2 I MIT Intelligent Logistics Systems Lab Mecalux StudyIntroductionKey findingsTheme 1:Current state of AI/ML adoptionAI adoption in warehousing reaches a new level of maturity The expand

2、ing footprint of AI in warehouse operations AI becomes part of the daily workflow A tool for operational complexity An enabler of high-impact valueTheme 2:AI/ML investment and ROIAI investments move from pilot budgets to proven returns Budget allocation and ROI from AI AI investment drivers Payback

3、periods reflect the scope of transformationTheme 3:Current AI/ML implementation challenges and enablersOvercoming challenges to accelerate AI adoption in warehouses Main barriers Internal capabilities for AI implementation Resources to speed up AI adoptionTheme 4:AI/ML impact on the workforcePeople

4、at the centre of warehouse transformation AI raises skills across the board AI creates new rolesTheme 5:Future AI/ML implementation outlook and prioritiesThe next wave:From prediction to decision Expanding capabilities and investmentContentsTheme 6:Dominant methods and technologiesFrom prediction to

5、 generation:How AI methods create value The technologies powering todays smart warehouses Integration:The foundation of effective AISynthesis across themes Key insights and implications for AI in warehousing RecommendationsCross-sectional analysisRegional nuances and differences The state of warehou

6、se automation across countries AI/ML budget allocation by country Barriers to AI and automation adoption vary by regionIndustry-specific differencesClosing remarksSurvey methodology MIT Intelligent Logistics Systems Lab Mecalux Study I 3Year:2025Participants:2,000+experienced supply chain and wareho