Unified commerce logistics is absorbing more than a 20% rise in global fulfillment and transport costs over three years, even as expectations for fast, flexible delivery harden. A new benchmark of over 400 specialty retailers shows that those redesigning journeys end to end and wiring AI into fulfillment are beginning to contain that pressure.
Logistics Costs Climb as Journeys Fragment
Global logistics and fulfillment spending has risen sharply for many retailers, increasing by more than a fifth in three years as service promises expand from basic delivery to rapid shipping, flexible pickup, and easy returns. Same‑day and next‑day options are increasingly common in mature markets, yet the economics behind them are tightening.
Customer journeys are also fragmenting across touchpoints. The benchmark from Manhattan Associates and Incisiv reports that more than 66% of shoppers now use at least two channels before placing an order, shifting between marketplaces, brand sites, social platforms, messaging apps, and physical stores. Traditional planning that assumes a single dominant channel or stable store‑centric flows no longer reflects how demand appears in the network.
The study frames this shift squarely as a supply chain design issue. It analyzes more than 330 capabilities spanning shopping, checkout, fulfillment, and service, and finds the widest performance gaps in how inventory intelligence, orchestration logic, and physical assets are combined. Organizations that still run stores, warehouses, and digital channels as disconnected systems face higher last‑mile spending, extra buffers, and frequent manual interventions to honor promises.
Leading performers are treating the supply chain as a primary lever for commercial performance. They use real‑time inventory data to decide where to fulfill from, how to allocate limited stock between channels, and when to flex transport or labor capacity. That translates into more precise order sourcing, fewer split shipments, and tighter alignment between customer‑facing options and what the network can reliably deliver.
One prominent move is the use of physical stores as fulfillment hubs. Leading U.S. retailers in the benchmark have cut last‑mile costs by about 31% by treating store networks as pre‑owned logistics assets rather than only sales locations. Ship‑from‑store, click‑and‑collect, and curbside pickup convert fixed real estate and store labor into a distributed grid of micro‑fulfillment points, shortening delivery distances and enabling more localized inventory pooling.
The benchmark also notes that 53% of retailers have strengthened last‑mile coordination and expanded operational infrastructure to support faster delivery demands. That investment ranges from revised carrier mixes and regional nodes to enhanced routing tools, but the financial impact depends on how well these assets are orchestrated in daily decisions.
From Visibility To Agentic Fulfillment
The research points to a structural change in how logistics decisions are made. Real‑time visibility and dynamic cross‑channel allocation correlate with roughly 50% higher inventory turns for leading U.S. retailers compared with peers that still rely on static rules or delayed data. This improvement reflects a move from periodic planning to continuous orchestration, driven by live signals from stores, distribution centers, carriers, and customer channels.
Many organizations have already invested in control towers and basic track‑and‑trace capability. The benchmark describes a next phase in which software agents support logistics teams by monitoring network conditions, predicting likely disruptions, and triggering standard responses. Manhattan Associates highlights agentic AI as a frontier capability that anticipates issues and addresses them before they reach the customer, for example by re‑routing orders, adjusting carrier selection, or proposing alternative pickup locations.
Industry reports across sectors show a similar pattern, with AI agents being piloted to detect demand surges, supplier delays, or weather‑related risk and to initiate pre‑approved actions. In retail logistics, this can reduce premium freight usage, stabilize on‑time performance, and narrow the gap between promised and actual delivery dates. It also requires clear guardrails around explainability, escalation thresholds, and how financial and ESG considerations are embedded into automated choices.
The benchmark makes clear that technology alone does not close the gap. A significant share of organizations that upgraded last‑mile capacity still fall back on manual playbooks when conditions change. Those that embed AI outputs into planning and execution cadences can rebalance cost, speed, and service more frequently, adjusting sourcing points or carrier assignments based on current lane performance rather than annual assumptions.
Recent trade data indicates that parcel volumes in many regions continue to grow faster than store traffic, while cities tighten emissions rules and labor markets remain constrained. Networks that stay rigid or heavily centralized will find it harder to absorb this volume and regulatory pressure without further cost escalation, even if front‑end customer experiences improve.
Where Unified Commerce Design Goes Next
The benchmark evidence points toward a practical next step: align network design, AI investment, and role definitions around a single orchestration model rather than separate channel programs. Organizations that map how orders flow across stores, warehouses, carriers, and digital touchpoints can set clearer rules for when stores act as hubs, where agentic AI can intervene, and which decisions remain firmly human. That level of clarity often reveals unused capacity, redundant safety stocks, and under‑utilized data that can be brought into service of both cost control and more dependable delivery promises.
