Artificial intelligence (AI) and machine learning (ML) are no longer experimental tools but core drivers of productivity, accuracy, and workforce evolution in the warehouse, according to a recent study from Mecalux and the MIT Intelligent Logistics Systems Lab at MIT’s Center for Transportation and Logistics.
The study of more than 2,000 supply chain and warehousing professionals found that 60% of warehouses have implemented some form of AI or ML, and that nearly 90% are operating at automation levels that are “beyond basic.” A majority of respondents—nearly 58%—describe their organizations as operating at advanced or full automation maturity, with nearly 31% saying they operate at a moderate level of automation. Just under 12% of respondents said their facilities utilize basic automation or are fully manual.
“The data show that intelligent warehouses outperform not only in volume and accuracy, but in adaptability,” Javier Carrillo, CEO of Mecalux, said in a November statement announcing the research results. “As peak season approaches, companies that have invested in AI aren’t just faster—they’re more resilient, more predictable, and better positioned to navigate volatility.”
The study also found that AI investments are paying off more quickly than many expected. Most businesses now dedicate between 11% and 30% of their warehouse technology budgets to AI and machine-learning initiatives, and the typical payback period is just two to three years, for example.
“These returns stem from measurable gains in inventory accuracy, throughput, labor efficiency, and error reduction,” the researchers wrote. “They also reinforce a shift from exploratory spending to long-term capability building. Cost savings, customer expectations, labor shortages, sustainability goals, and competitive pressure all drive these investments, demonstrating that AI’s value extends far beyond automation alone.”
That includes adding value to the workforce: The study found that AI is contributing to higher productivity, greater job satisfaction, and expanded workforce opportunities. More than three-quarters of respondents reported a rise in employee productivity and satisfaction after implementing AI tools, and more than half reported growing the size of their workforce. New roles are emerging across the board, including AI/ML engineers, automation specialists, process-improvement experts, and data scientists.
Despite the progress, organizations face challenges as they scale AI across their operations. According to the research, the leading barriers are technical expertise, system integration, data quality, and implementation cost—which reflects the underlying work required to connect advanced tools with legacy systems.
“The hard part now is the last mile: integrating people, data, and analytics seamlessly into existing systems,” Dr. Matthias Winkenbach, director of the MIT ILS Lab, said in the statement.
Looking ahead, nearly every company surveyed plans to scale up its use of AI over the next two to three years: 87% said they expect to increase their AI budgets and 92% said they are currently implementing or planning new AI projects.
The authors said the next frontier will center on decision making technologies—especially generative AI, which respondents said can help with automated documentation, warehouse-layout optimization, process-flow design, and code generation for automation systems.
“Traditional machine learning is great at predicting problems, but generative AI actually helps you engineer the solution,” Winkenbach said. “That’s why companies see it as the biggest value generator in the warehouse today. Ultimately, the measurable gains from automation are productivity wins, making existing systems work smoother, faster, and with fewer disruptions.”
