How AI-Driven Autonomy Is Reinventing Supply Chains

As disruptions grow in frequency and complexity, traditional supply chain models are reaching their limits. Forward-looking leaders are now prioritizing long-term transformation to build resilience and agility.

Accenture’s new global study reveals a decisive shift: Executives are looking at artificial intelligence-driven autonomous supply chains as the next strategic lever. The research surveyed 1,000 C-suite executives across North America, South America, Europe and Asia Pacific, including chief operations, supply chain, digital and technology officers from 10 industries. 

In the past, supply chains were largely optimized for cost. Then came the pandemic, geopolitical rifts, climate shocks and labor shortages, all forcing companies into a reactive loop of triage and recovery. Resilience has become the new mantra.

This shift isn’t just conceptual. The study finds that two-thirds (66%) of global companies plan to make significant advances in supply chain autonomy over the next decade, and among them, nearly 40% are aspiring to achieve a higher degree of autonomy where the system handles most operational decisions.  

What Autonomy Really Means

Autonomous supply chains extend well beyond basic automation. Autonomy exists on a spectrum, with supply chains evolving through distinct stages:

• Human-driven (0%-25%): Processes are primarily manual or dependent on legacy IT systems.
• Automation (25%-50%): Routine tasks are automated, but key decisions are still made by humans.
• Augmented decisions (50%-75%): AI provides insights and recommendations, while humans validate and finalize decisions.
• Full autonomy (75%+): AI systems independently plan and execute decisions without human intervention.

Survey findings indicate that most companies are in the early phases of adopting autonomous capabilities. The majority remain in the human-driven stage, with only a few progressing toward augmented decision-making.

Currently, about 25% of respondents have started their autonomy journeys, with a median maturity level of just 16% across supply chain functions. This median is projected to increase to 42% within the next five to ten years.

In the report, supply chain activities of similar tasks have been grouped into nine clusters. Here’s a breakdown of how leading organizations are projected to advance toward autonomous operations in the coming five years:

• Quality and production control. Currently at 25%, expected to rise to 56%, leveraging AI and IoT for real-time process optimization and precision.
• Make. From 19% to 46%, moving toward greater automation in manufacturing.
• Customer and field support. Likely to increase from 18% to 46%, with AI-driven chatbots and platforms enhancing customer service.
• Design, develop and strategic purchasing. From 18% to 53%, utilizing AI and blockchain to improve supplier selection and contract management.
• Alert, risk and improvement. Rising from 16% to 40%, applying advanced analytics for risk identification and mitigation.
• Move. Advancing from 14% to 36%, with autonomous vehicles and drones increasingly deployed for transportation.
• Plan and schedule. Increasing from 15% to 38%, supported by predictive analytics for improved demand forecasting.
• Operational purchasing. Expanding from 9% to 37%, automating more procurement processes.
• Set-up, maintenance and changeover. Growing from 8% to 21%, enhancing efficiency in maintenance and changeover tasks.

While most clusters still operate at lower levels of autonomy, momentum is clearly building. Clusters like quality and production control, make, and customer and field support are already advancing rapidly, driven by innovations such as robotic assembly lines, AI-based inspections, and AI-supported self-service platforms. Quality and production control is poised to lead in autonomy maturity, fueled by the rise of lights-out factories that deliver precision, speed, and customization. Close behind are make and customer support, where automation is already leading to significant efficiency and service gains.

Planning, logistics and maintenance-focused clusters are catching up, with emerging use cases such as autonomous warehouse robots, intelligent scheduling systems and predictive analytics accelerating adoption in move, plan and schedule, and set-up, maintenance and changeover. Autonomy in these clusters is projected to grow by more than double, signaling a shift from experimentation to scaling.

Even earlier-stage functions such as operational purchasing and risk alerts show clear upward trajectories, with adoption expected to more than double. Across the board, this signals a broader transformation, from fragmented pilots to strategic, integrated deployment of autonomous capabilities across the supply chain.

Stakes and Returns Are High

The shift to autonomous supply chains is no longer just a play on efficiency; it has become a strategic imperative. Leading organizations are recognizing that autonomous supply chains promise value on the following four fronts:

Building operational resilience. Autonomous supply chains allow companies to respond faster and more effectively to constant market disruptions. Executives anticipate a 27% reduction in order lead times, coupled with a 25% improvement in labor productivity. Most significantly, disruption recovery times are expected to decrease by nearly 60%, a critical advantage in an era defined by volatility and unpredictability.

Financial gains. As autonomous capabilities scale, leaders surveyed expect to see a 5% increase in earnings before interest and taxes (EBIT) and a 7% improvement in return on capital employed (ROCE). 

Sustainability and circularity. Nearly four in 10 companies (39%) believe autonomous operations will drive significant improvements in circular supply chains. By enabling smarter reuse, better recycling and enhanced resource efficiency, autonomy contributes meaningfully to environmental, social and governance (ESG) commitments.

Improving reliability and speed. Improving customer satisfaction through reliable and timely fulfillment is another strategic benefit. Respondents expect a 5% improvement in on-time delivery performance. In addition, 86% foresee cost reductions, 76% expect greater efficiency, 77% believe agility will increase, and 63% anticipate faster processes throughout the supply chain.

To realize this vision of autonomous supply chains will require deliberate investment. On average, companies estimate they need to allocate 0.9% of their annual revenue to develop the necessary capabilities, a figure increasingly seen not as optional, but as essential to remain competitive in a rapidly evolving global landscape.

But not without obstacles: Presently, autonomy remains an aspiration more than a reality. Only 4% of firms surveyed aim to achieve full autonomy. Key barriers include poor data quality and integration, cybersecurity and data privacy risks and gaps in process maturity. Interestingly, employee resistance is not a major blocker; trust in AI decisions is.

To build that trust, companies must start small, pilot autonomous systems in critical areas, ensure transparency in decision-making, and upskill the workforce to collaborate with AI.

Just the Beginning

Autonomy isn’t a moonshot; it’s a structured, multi-year transformation that’s already underway. While resilience helped organizations survive disruption, autonomy will help them thrive in a world that demands speed, precision, and adaptability.

What sets the leaders apart on this journey? Three critical actions are emerging as differentiators:

Laying a solid digital foundation. Leading organizations are building a secure, standardized digital core that ensures data quality, integration, and governance across the supply chain. This strong foundation enables real-time visibility and decision-making.

For instance, a global high-tech company built a decision intelligence system to address manual and fragmented inventory management. Previously reliant on inconsistent data, the company modernized its digital infrastructure to automatically diagnose shortages, optimize replenishment, and write back decisions into source systems. As a result, it now orchestrates thousands of decisions that were once manual, boosting labor productivity and responsiveness.

Strategic investment in AI-enabling technologies. Instead of waiting for perfect solutions, these companies start with focused pilots, learn fast, and scale proven technologies that enhance automation, prediction, and optimization.

A consumer goods company, for example, introduced an AI-powered batch health monitoring system in one of its Indian factories. This innovation reduced cost per ton by predicting optimal batch performance based on quality, cycle time and utility use. In logistics, it cut average dispatch distances by 15% and boosted truck utilization by 10%. Procurement has also been transformed using AI-based forecasters and optimizers, as well as global tenders and backward integration in sourcing palm oil, resulting in significant material and cost savings.

Redefining the human-technology partnership. Autonomy doesn’t eliminate people; it redefines their role. Companies are redesigning workflows so that humans move from routine execution to strategic guidance, supported by AI-powered systems.

The path forward is clear: companies that invest early are likely to create a virtuous cycle of innovation and growth. Those that delay risk falling into a vicious cycle of stagnation, held back by legacy systems, talent gaps, and an inability to keep pace with the next generation of supply chain performance.

Kris Timmermans is global supply chain and operations lead, and Max Blanchet is global supply chain and operations strategy lead, at Accenture.