RELEX State of Supply Chain 2026: AI Moves From Experimentation to Operational Decision Support Across Planning Functions

The gap between experimenting with AI and actually trusting it to run supply chain operations is narrowing — but it hasn't closed. That's the central finding from RELEX Solutions' newly released State of Supply Chain 2026: Volatility, Trade-Offs & the Rise of AI report, based on a January 2026 survey of 514 retail, manufacturing, wholesale, and supply chain leaders conducted by Researchscape.

The headline: 67% of retail and manufacturing leaders say their confidence in using AI for supply chain decision-making has increased compared to last year. But confidence and autonomy are not the same thing. Only 10% say they would trust AI to make fully independent supply chain decisions. The industry is firmly in the "copilot era" — AI recommends, humans decide.

For logistics operators and supply chain teams, the implications are significant. The companies that figure out how to operationalize AI across planning functions — not just demo it — will own the next decade.

The Numbers That Matter

RELEX's survey surfaces several data points that illustrate where the industry actually stands, as opposed to where vendor marketing suggests it should be:

• 67% of leaders report increased confidence in AI for supply chain decisions year-over-year
• 54% prefer AI to make recommendations while humans finalize decisions
• Only 10% would trust AI to make fully independent decisions
• 47% are using or planning AI-driven inventory and supply optimization
• 41% are applying AI to logistics and routing
• 71% plan to invest in generative and agentic AI over the next 3–5 years
• 60% plan to invest in predictive AI over the same period

These numbers describe an industry that has moved well past the "should we use AI?" debate and into the harder question: how much control do we hand over, and how fast?

Why Volatility Is Forcing the Transition

The report finds that 44% of leaders cite consumer demand volatility as a top challenge over the next three years — making it the single most cited pressure point driving AI investment. This isn't theoretical. Every quarter of missed demand forecasts translates directly to excess inventory, stockouts, or margin erosion.

For retailers, the pressure is acute: 30% cite adapting to sudden consumer demand shifts as a major challenge, reinforcing why demand sensing and responsive replenishment are the AI use cases seeing the fastest adoption.

For manufacturers, the volatility takes a different form: 57% say raw material procurement disruption is the most impacted area of their supply chain, while 34% cite regulatory and compliance pressures as a growing operational concern. AI is increasingly being applied to connect demand signals with procurement and production decisions — essentially closing the loop between what customers want and what factories can actually produce.

The common thread: companies can no longer afford the latency of manual planning processes. When demand shifts happen in days, not quarters, the planning cycle has to keep pace.

From Experimentation to Operational AI: What's Actually Different

The distinction RELEX draws between "experimentation" and "operational decision support" is worth unpacking, because the industry has been throwing around terms like "AI-powered" for years without much precision.

Experimentation looks like: running an AI demand forecast in parallel with existing processes, comparing outputs, and occasionally acting on the AI recommendation when a human decides it looks right. The AI is a second opinion.

Operational decision support looks like: AI-generated recommendations are the primary input for inventory replenishment, allocation, and distribution decisions. Humans review and approve, but the AI output is the starting point — not the exception.

Full autonomy — the 10% endpoint that almost nobody trusts yet — means AI executes decisions without human review. Automated reorder points, dynamic pricing adjustments, and real-time routing changes that happen without a planner touching a dashboard.

The RELEX data shows the industry is migrating from stage one to stage two. The functions leading this transition are demand sensing, inventory optimization, and replenishment — the planning domains with the highest data density and the most quantifiable ROI.

Sustainability as Operational Constraint, Not Aspiration

One finding from the report that deserves more attention: 63% say the importance of sustainability in their supply chain strategy has increased. But this isn't about ESG marketing. Combined with the finding that 34% of manufacturers cite regulatory compliance as a disruption source, the picture is clear — sustainability has become an operational constraint that planning systems must account for.

AI-driven planning is increasingly expected to optimize not just cost and service levels, but also emissions, waste, and regulatory compliance simultaneously. This multi-objective optimization is precisely where manual planning breaks down and AI earns its keep.

The Investment Trajectory: Agentic AI Enters the Planning Stack

Perhaps the most forward-looking data point: 71% of organizations plan to invest in generative and agentic AI for supply chain planning over the next three to five years. This represents a shift from AI as a forecasting tool to AI as an operational agent — capable of not just predicting demand but orchestrating responses across procurement, production, and distribution.

As Dr. Madhav Durbha, Group Vice President of Manufacturing Industry Strategy at RELEX Solutions, noted: "AI is becoming part of everyday supply chain decision-making. As volatility persists, companies are investing in AI-driven forecasting, optimization, and decision support to respond faster and operate with greater confidence, even when conditions change quickly."

The convergence of generative AI (for scenario planning, exception handling, and natural language interaction with planning data) and agentic AI (for autonomous execution of routine planning decisions) represents the next frontier. Companies that build the data infrastructure and operational trust for these capabilities now will be positioned to leapfrog competitors still stuck in the experimentation phase.

What This Means for Logistics Operations

The RELEX findings reinforce a trend that SupplyChainBrain has been tracking across the industry: AI adoption in supply chain is no longer a technology problem — it's an organizational trust problem. The tools exist. The ROI is proven. The bottleneck is getting planning teams, procurement leaders, and operations managers to actually let AI drive.

For logistics operators specifically, the 41% applying AI to logistics and routing represents both opportunity and competitive pressure. If your competitors are using AI to optimize load planning, route selection, and carrier allocation while your team is still running spreadsheet-based processes, the efficiency gap compounds every quarter.

How CXTMS Supports the Experimentation-to-Operations Transition

At CXTMS, we've built our transportation management platform around the principle that AI-ready data is the prerequisite for AI-powered decisions. Our platform integrates shipment data, carrier performance metrics, and real-time visibility feeds into a unified data layer that supports the kind of operational AI decision-making the RELEX report describes.

Whether your organization is at the experimentation stage — running AI recommendations alongside existing planning — or ready to move into operational decision support with AI-driven routing and carrier selection, CXTMS provides the data infrastructure and workflow integration to make the transition practical.

Ready to move AI from experimentation to operations? Request a CXTMS demo and see how integrated logistics data powers confident, AI-assisted decision-making across your supply chain.