When Supply Chains Become Autonomous

By Carol Long, David Simchi-Levi, Andre P. Calmon, and Flavio P. Calmon

December 11, 2025

Recent advancements in generative AI have revolutionized supply chain management, demonstrating that autonomous systems can effectively make logistics and inventory decisions with minimal human oversight. Traditional supply chains, while automated, still rely on human-designed rules and management. In contrast, generative AI systems function autonomously, leveraging advanced reasoning models like GPT-5 and Llama 4 to coordinate multiple functions such as demand forecasting, inventory planning, and replenishment.

Our research conducted in a simulation, which utilized the MIT Beer Distribution Game, showed that AI-driven systems could outperform human teams. For instance, the AI agents cut total supply chain costs by up to 67%, significantly reducing inefficiencies caused by the bullwhip effect—a phenomenon where small demand fluctuations lead to amplified operational chaos.

The benefits of implementing generative AI are clear. Companies can use advanced off-the-shelf models without incurring the costs of building proprietary systems. By deploying these AI agents effectively, organizations can respond rapidly to changes, simulate strategies, and refine decision-making processes in real-time. The autonomy of these systems allows managers to focus on strategic initiatives rather than operational tasks.

To leverage this technology successfully, supply chain executives should begin with an audit of their AI infrastructure, implement pilot projects with limited scopes, and develop the orchestration skills necessary for facilitating effective data flows and policies. This approach not only optimizes supply chain efficiency but also transforms the leadership role from task execution to strategic orchestration, enabling businesses to adapt in a volatile global landscape. The era of autonomous supply chains is not just emerging; it is becoming accessible and essential for modern enterprises.

A groundbreaking study conducted by Mecalux and the MIT Intelligent Logistics Systems Lab reveals a transformative shift in warehouses that are integral to global supply chains. With insights from over 2,000 supply chain professionals across 21 countries, it's evident that artificial intelligence (AI) and machine learning are not merely experimental but critical components of enhanced productivity and accuracy in warehousing operations.

Today, more than 90% of warehouses utilize some form of AI or advanced automation, marking a significant maturation in the sector. Over half of the organizations surveyed operate at advanced automation levels, particularly larger enterprises with intricate logistics networks. AI now supports various daily operations, including order picking, inventory optimization, equipment maintenance, labor planning, and safety monitoring.

According to Javier Carrillo, CEO of Mecalux, intelligent warehouses not only achieve higher volume and accuracy but are also more adaptable, providing companies with resilience and predictability in volatile conditions. Businesses invest 11-30% of their warehouse technology budgets into AI, often seeing a return on investment within two to three years through improved inventory accuracy, throughput, and labor efficiency.

Despite the benefits, challenges remain in scaling AI operations, particularly in integrating people, data, and analytics with existing systems. Yet, companies are witnessing an increase in employee productivity and job satisfaction, with many reporting workforce growth after adopting AI tools.

Looking ahead, 87% of businesses plan to expand their AI initiatives, with a focus on generative AI, which offers significant applications such as automated documentation and warehouse layout optimization. This shift towards integrated AI solutions promises to enhance overall performance and strategic decision-making in warehouses, forging a path for ongoing innovation.

Artificial intelligence (AI) and machine learning (ML) have transformed from experimental tools to essential components in warehouse operations, driving significant enhancements in productivity, accuracy, and workforce dynamics, as revealed by a recent study from Mecalux and the MIT Intelligent Logistics Systems Lab.

The study surveyed over 2,000 supply chain professionals, showing that 60% of warehouses now utilize AI or ML, with nearly 90% exceeding basic automation levels. Around 58% of businesses reported operating at advanced or full automation maturity, significantly improving their performance. Javier Carrillo, CEO of Mecalux, emphasized that these intelligent warehouses excel not only in volume and accuracy but also in adaptability, positioning companies to better navigate market fluctuations.

Investments in AI are proving beneficial, with most organizations allocating 11% to 30% of their tech budgets to these initiatives. Notably, the payback period for such investments is typically just two to three years, as firms experience improved inventory accuracy, labor efficiency, and reduced errors. These advancements reflect a transition from exploratory spending to sustainable capability development in response to cost pressures and customer expectations.

AI is also positively impacting the workforce by enhancing productivity and job satisfaction. Over 75% of respondents noted increased employee performance post-AI implementation, with more than half expanding their teams and creating new roles like AI engineers and process improvement experts.

While challenges remain, including technical expertise and system integration, 87% of companies plan to increase their AI budgets and embark on new AI projects in the near future. The focus is shifting toward decision-making technologies, particularly generative AI, which is recognized for its potential in automated documentation and optimizations, ultimately contributing to smoother and faster operations.

The era of agentic business applications is transforming how organizations operate, with generative AI serving as a pivotal driver of this change. At Convergence 2025, scheduled for December 9-12, we will discuss the transition from systems of record to systems of action, where AI agents interpret signals, uncover patterns, and optimize processes independently. This evolution marks a shift toward autonomous enterprises, characterized by intelligent systems that enhance collaboration and decision-making.

AI's concrete applications in everyday operations include agents that streamline finance, sales, and supply chain workflows. For instance, Dynamics 365 offers features like the Sales Order Agent, which automates order creation and updates, significantly reducing manual entry errors and accelerating processes. Similarly, the Payables Agent automates vendor invoice handling, allowing finance teams to focus on strategic tasks rather than repetitive ones.

Innovations such as the Product Change Management Agent Template leverage AI to streamline workflow management in manufacturing. By cutting approval times and improving error handling, organizations can innovate faster. Other partner-built solutions, like Shop Floor by RSM, enhance production visibility, while the PayFlow Agent by HSO automates vendor payment inquiries, improving efficiency and communication.

These developments illustrate how Dynamics 365 empowers companies to harness AI’s full potential, enabling a transition from mere automation to true autonomy. At Convergence 2025, attendees will see how these agentic applications not only optimize current processes but also pave the way for a more agile and innovative future in business. This represents a critical leap toward an AI-first ecosystem, integrating every aspect of organizations to drive measurable outcomes.