The AI Supply Chain Performance Gap

AI-enabled supply chain leaders are cutting expedited orders by up to 59% through better planning and execution decisions

Executive summary

• A new supply chain performance gap is emerging between organizations that redesign the supply chain around artificial intelligence (AI) and those that simply layer it onto existing activities. The next competitive advantage will be measured in supply chain performance
• Leading organizations are using AI to reinvent work, not just automate tasks. By redesigning end-to-end supply chain processes and operating models, they are creating capacity for higher-value work and structural advantages in working capital, service reliability, operating leverage, and resilience.
• Plan-to-deliver demonstrates the scale of the opportunity. By redesigning the end-to-end process, AI World Class organizations reduce expedited orders by up to 59%, forecast error by up to 52% and inventory carrying cost by up to 41%, while shortening pick-to-ship cycle times by up to 38%.¹
• The value compounds throughout the process. Each stage reduces the work the next one has to absorb. Sharper demand signals reduce inventory needs, better inventory positioning reduces warehouse complexity and more efficient warehouse execution reduces the need for expedited freight.
• The shift is already underway. Organizations are redesigning demand planning, optimizing inventory using AI-enabled signals and automating expedite-related fulfillment tasks.

Bottom line: The first enterprise AI adoption wave was primarily about experimentation and deployment. The next wave will be defined by performance as organizations redesign the supply chain around AI-enabled execution to create sustainable business advantage. The winners will be the organizations that redesign work first.

¹ AI World Class Supply Chain benchmark modeling based on peer median and AI World Class performance scenarios, The Hackett Group^®, 2026

A new supply chain performance gap is emerging between organizations that redesign work around AI and those that simply automate existing supply chain activities. AI World Class benchmarks show where the gap is widest and where supply chain leaders can close it.

Benchmark modeling indicates that organizations redesigning the supply chain around AI can reduce forecast error by up to 52%, cut expedited orders by up to 59% and increase on-time in-full (OTIF) performance into the high 90% range. These figures describe a modeled future state rather than current median practice.

The distance between that median and the modeled state is widest for organizations still early in supply chain modernization, but it does not disappear for the rest. Even organizations already operating at Digital World Class^® levels of standardization and automation have substantial room to improve. AI World Class measures how far the work itself can be redesigned and whatever level of automation is already in place.

As tariff exposure, demand volatility and margin pressure mount, the supply chain is increasingly expected to deliver more than efficient execution. Chief supply chain officers are being asked to release working capital, improve service reliability, strengthen supplier performance and absorb disruption they do not control – all without proportional increases in resources.

Yet, most supply chain AI business cases remain focused on forecast accuracy rather than broader process transformation. This creates a growing collection of planning tools and point solutions that improve isolated decisions but do little to change how the network performs. As AI investments accelerate, few organizations can clearly identify which process changes will create the greatest value, what level of performance improvement is achievable or where AI investment should be prioritized.

How AI value compounds across plan-to-deliver²

While many organizations focus on automating individual supply chain activities, AI World Class reveals a larger opportunity: redesigning the entire end-to-end process around AI. Improvements across planning, inventory management, manufacturing, warehousing, transportation and returns compound. Better decisions upstream reduce work downstream, improving service, working capital and cost throughout the process. The gains land at both ends of the process. AI World Class modeling shows process costs across plan-to-deliver can decline by 12% to 17%, while staffing requirements decline by 15% to 24%.³

^2,3AI World Class Supply Chain benchmark modeling based on peer median and AI World Class performance scenarios, The Hackett Group^®, 2026

The opportunity extends across the supply chain

The impact begins at the front of the chain. AI World Class organizations use demand sensing, external signal data and predictive analytics to reduce forecast error by up to 52%.⁴ Better demand signals change what the rest of the chain has to absorb before a single unit is positioned.

As demand signals sharpen, inventory can be produced and positioned more precisely rather than held as buffer stock. Modeling shows inventory carrying cost declines by up to 41% and obsolete inventory declines by up to 36%,⁵ improving working capital and addressing the margin pressure created by carrying and obsolescence costs.

The impact becomes even more visible in the warehouse. Better inventory positioning produces cleaner warehouse work, with pick-to-ship cycle time up to 38% shorter and dock-to-stock up to 45% shorter.⁶ These are among the largest cycle-time improvements in the benchmark set, and they reflect both lower warehouse complexity and redesigned execution workflows.

Faster fulfillment then reduces the need to recover through freight spend. Expedited orders decline by up to 59% and shipment-to-delivery cycle time declines by up to 25%.⁷ That decline points to less downstream recovery work, and the pattern carries through to returns, where processing accelerates by up to 40%. The customer sees the results in improved OTIF, which rises into the high 90% range.⁸

This is not simply an efficiency story. It is a redesign of the planner’s job. At peer median, planners spend their weeks reconciling data, chasing exceptions and expediting shipments. As that work becomes increasingly automated, capacity is shifted toward more strategic work. The result is a supply chain that ties up less cash and absorbs disruption instead of passing it on.

^4,5,6,7,8, AI World Class Supply Chain benchmark modeling based on peer median and AI World Class performance scenarios, The Hackett Group^®, 2026

AI transformation has moved beyond individual use cases

How AI World Class quantifies opportunity

Organizations create value from AI in different ways – from targeted productivity improvements to end-to-end process transformation and operating model reinvention. The challenge for supply chain leaders is determining which opportunities will create the greatest value, what level of change is required, and what performance improvements are realistically achievable. The opportunity extends beyond organizations that have yet to modernize the supply chain. Even highly automated organizations can create additional value by redesigning work around AI-enabled execution.

AI World Class benchmarks answer those questions by quantifying opportunities at the process, subprocess and workstep level. The analysis is grounded in 30 years of benchmark data, process intelligence and transformation experience drawn from 98% of the Dow Jones Global Titans, 97% of the Dow Jones Industrials and 90% of the Fortune 100.

Unlike generic AI business cases, AI World Class benchmarks begin by validating current workflows, automation levels, planning practices and operating environments before modeling future-state performance. AI World Class extends The Hackett Group’s Digital World Class^® methodology to model the next generation of performance achievable through AI-enabled execution. This ensures opportunities are grounded in how work is actually performed rather than theoretical assumptions.

The outcome is a fact-based view of where AI can create supply chain value, what level of performance improvement is achievable, and where investment should be prioritized across planning, inventory, manufacturing, warehousing, transportation and returns processes. The result is greater confidence in where to focus transformation efforts to release working capital, improve service reliability and concentrate investment on the opportunities with the greatest business impact.