RELEX State of Supply Chain 2026: AI Moves From Pilot Projects to Operational Decision-Making

There was a time when AI in supply chain planning meant a PowerPoint slide and a proof-of-concept that never left the lab. That era is over.

RELEX Solutions' State of Supply Chain 2026 report — based on a January 2026 survey of 514 retail, manufacturing, wholesale, and supply chain leaders — paints a clear picture: AI has crossed the threshold from experimentation to operational deployment. Sixty-seven percent of respondents say their confidence in using AI for supply chain decision-making has increased over the past year. Nearly half are already using or planning to use AI-driven inventory and supply optimization.

But the most interesting number isn't the adoption rate. It's the trust gap.

The Numbers Behind the Shift

RELEX's research surfaced some counterintuitive findings about how organizations are actually deploying AI:

• 54% prefer AI to make recommendations while humans make the final call
• Only 10% say they would trust AI to make fully independent supply chain decisions
• 71% are planning to invest in generative and agentic AI over the next three to five years
• 60% are investing in predictive AI over the same horizon
• 44% cite consumer demand volatility as a top supply chain challenge over the next three years

That last stat explains a lot. When demand is stable and predictable, the status quo works fine. When it swings week to week — think tariff front-loading, seasonal spikes, or sudden category shifts — the gap between human intuition and machine-generated forecasts becomes impossible to ignore.

Three AI Use Cases That Are Already at Scale

RELEX's data points to three categories where AI is furthest along in live operations:

Demand forecasting (54%) — Machine learning models trained on point-of-sale data, promotion calendars, and external signals (weather, macroeconomic indicators) are producing more accurate forward estimates than traditional statistical methods. Retailers like World Market and Gordon Food Service are now running daily intraday recalculations across hundreds of stores and distribution centers.

Replenishment automation (48%) — Automated reorder point calculation with dynamic safety stock adjustment is replacing calendar-based purchasing. Gordon Food Service's deployment across 185 stores uses RELEX to reconcile store-level sales velocity with DC inventory in near-real time, reducing both stockouts and spoilage.

Inventory optimization (43%) — AI-driven category management is helping retailers like Lowe's reduce SKUs while improving in-stock rates. Lowe's has been explicit about using AI to unify forecasting, replenishment, and allocation — targeting inventory reductions as a margin play, not just an operational efficiency play.

Why This Matters for Logistics Operators

Here's the connection that often gets lost: AI-driven planning upstream creates downstream obligations.

When a retailer's forecasting system calls for 15% more volume at a specific DC on Tuesday, that signal needs to propagate into transportation tendering, carrier appointment scheduling, and dock capacity planning within hours — not the next business day. A TMS that only knows what to ship after the purchase order arrives is increasingly a liability.

This is the operational argument for modern TMS platforms like CXTMS. The value isn't just in executing shipments efficiently. It's in being the layer that takes AI-generated demand signals and turns them into executable logistics plans — automated tendering, carrier selection, mode optimization, and appointment booking that responds to planning system output without manual intervention.

The Trust Gap Is the Opportunity

That 10% number — the share of companies willing to let AI make fully independent decisions — is both a warning and a data point.

Fully autonomous supply chain decision-making at scale is not here yet, and anyone telling you otherwise is selling something. What is here: AI that dramatically compresses the time between signal and response, reduces the cognitive load on planners, and surfaces exceptions that human review would miss.

The freight forwarders and logistics operators who will win over the next three to five years are not the ones replacing their planners with AI. They're the ones building the connective tissue between AI-driven planning systems and day-of-execution transportation management. CXTMS is built to be exactly that layer — the operational engine that acts on AI intelligence at the speed the business requires.

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