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A targeted AI approach to maximizing value in fulfillment
5-Minute read
February 2, 2026
Fulfillment—spanning warehousing, inventory management and transportation, including the last mile—is one of the most expensive areas of the supply chain, often accounting for over 40% of total logistics costs.[1] Labor dependency, fuel-intensive operations and fragmented systems create inefficiencies that drag margins and slow response times.
Now, fulfillment is evolving from a manual process into a smart, self-optimizing network—one that moves faster, uses assets more efficiently and improves the customer experience while lowering the cost to serve. The result: reduced costs, greater capacity and a smaller environmental footprint.
Our starting point is the 2x2 supply chain cost categorization framework from Making self-funding supply chains real: Where to start and scale for autonomous, end-to-end growth. The framework maps cost components along two dimensions—their share of total cost in a given domain and the ability of AI and autonomous technologies to reduce those costs, enhance efficiency and improve scalability. In the main report, we apply this lens across four operational domains—planning, procurement, manufacturing and fulfillment—to show where better decisions about AI and autonomy can unlock rapid savings and measurable productivity gains.
Leading companies are concentrating on the following fulfillment cost levers that deliver maximum impact—driving meaningful reductions, scaling effectively and generating immediate savings to fund the next phase of investment.
Warehouse automation
Manual movement, static slotting and disconnected systems slow warehouse operations, creating congestion, idle time, safety risks and higher costs—while capacity goes underused and inventory accuracy declines.
Smarter automation is the way forward. Autonomous Mobile Robots (AMRs) now handle picking, packing and pallet movement with dynamic route planning and human movement detection that improve safety and utilization. Modern Warehouse Management Systems (WMS) add customizable workflows, dynamic slotting and real-time analytics to boost accuracy, productivity and cost performance. Together, these advances deliver up to 15% higher inventory accuracy, up to 22% lower warehousing costs and up to 20% greater productivity.[2]
Amazon provides a clear example. Its AI foundation model, DeepFleet, uses reinforcement learning to optimize robot routing—increasing travel speed by 10%, accelerating order processing and reducing delivery costs.[3] Across more than 9,500 deployed robots, the company has cut picking time by 71% and lowered operational costs by 20%.[4]
Transportation and route optimization
Manual routing, siloed systems and static load-building processes lead to underused capacity, excess miles and slow exception handling in logistics. These inefficiencies drive up costs and keep teams stuck in reactive firefighting instead of proactive optimization.
To break this cycle, companies are deploying AI-enabled routing and load optimization models that dynamically plan routes and carrier assignments based on real-time capacity, demand and constraints. These tools also help improve load fill rates and truck utilization, reducing fuel consumption and operational waste.
Increasing truck fill rates to 97%—up from the current 85 to 90%—with AI-powered route optimization, for instance, can result in a decrease in fuel and logistics costs by 15%.[2] Unilever achieved this with Solvoyo, raising load fill rates by 300 basis points, reducing transportation costs by 5% and lowering CO₂ emissions by 400 basis points.[5] ProvisionAI experienced similar gains, increasing utilization from 90% to 98% and saving up to 8% in related transportation costs.[6]
AI demand sensing and inventory rebalancing
Dynamic inventory rebalancing becomes increasingly difficult amid volatile lead times, unpredictable demand and inconsistent data across network nodes. Complex transfer decisions and data delays often weaken accuracy, leading to stock imbalances, higher carrying costs and service level risks.
To overcome these challenges, companies are adopting Internet of Things (IoT)-enabled inventory platforms that track performance in real time and support dynamic rebalancing. AI-driven demand sensing and agentic AI anticipate fluctuations and optimize replenishment, reducing excess inventory while maintaining high service levels.
The results can be transformative: Companies report a 20% reduction in excess inventory while maintaining 99% service levels.[7] They also see 20 to 30% lower carrying costs, 35 to 45% fewer stockouts and a 40% improvement in forecast accuracy. Automated reordering has cut manual work by 60%, freeing teams to focus on higher value tasks.[8] Customer-centric AI systems extend these gains, lowering fulfillment costs 10 to 15% and lifting inventory returns by up to 25%.d
Intelligent fulfillment in practice
Powered by agentic AI, today’s supply chains are transforming fulfillment from a cost burden to a competitive edge. By adapting in real time to demand, they deliver more precise service while sustaining performance across warehousing, inventory and the last mile.
PUMA India is under increasing pressure to deliver faster, more reliable service in a rapidly expanding market. As consumer expectations rise, the company needed a more agile, scalable and cost-efficient fulfillment network.
To meet this challenge, PUMA India is partnering with Accenture to redesign its end-to-end supply chain using digital twin technology and advanced analytics. The transformation includes reconfiguring fulfillment-center layouts, improving material flow, rebuilding the distribution network across large hubs and regional warehouses and implementing an analytics-powered operating model across both e-commerce and offline channels. Together, these initiatives lay the groundwork for a smarter, more autonomous fulfillment system.
The redesigned network is expected to increase delivery speed by up to 70%, reduce supply-chain costs by up to 10% and double express-delivery capacity for online orders. [9]
Visit Making self-funding supply chains real: Where to start and scale for autonomous, end-to-end growth, for the full view of how fulfillment contributes to an integrated, end-to-end supply chain transformation.
References
[1] What is last-mile delivery cost in ecommerce
[2] Source: Accenture analysis of client engagements
[3] Amazon deploys over 1 million robots and launches new AI foundation model
[4] The AI in supply chain report 2025: Market data, use cases and what’s next
[6] How a major consumer goods company enhanced logistics with AutoO2
[7] Transforming supply chain management with Agentic AI
[9] Accenture and PUMA India Collaborate to Build Next Gen Supply Chain Network
