1. A Fast Growing Market

The global AI market in supply chain is experiencing high annual growth: +25% expected as early as 2026 (Global Market Insights).

But this momentum is not just driven by enthusiasm for the technology: it is explained by a massive and measurable ROI. Companies that have already adopted AI experience on average +61% revenue growth compared to their competitors (IBM).

Yet only 10% of organizations actually use AI strategically (Gartner). Most therefore still do not fully leverage these technologies, leaving wide room for those who want to get ahead.

2. Top 3 AI Investments in Supply Chain

Decision-making in planning stands among the top three investment priorities for supply chain leaders, alongside workforce and edge computing.

The reason is simple: variability is exploding — volatile demand, transport disruptions, environmental constraints, shortages… Companies now aim not just to digitize, but to make their operations more intelligent, sustainable, and robust.

3. Major Applications of AI in Transport

• Optimization of transport plans and routing: Decision-making AI provides optimal transport plans in seconds, integrating the most complex business constraints (LTL/FTL, first-mile/middle-mile/last-mile, weekly planning, multi-depot, multi-echelon & cross-docking…). Result: fewer vehicles, fewer kilometers, lower CO2 emissions, better quality of service.

• 3D loading optimization: AI solvers automate and optimize loading plans to eliminate capacity approximations, maximize filling rates, and reduce linear meters. Result: fewer empty trips, fewer leftovers at docks, increased storage space, more sales.

• Automated analysis of transport documents: OCR (Optical Character Recognition) and NLP (Natural Language Processing) algorithms automatically extract data from CMRs or delivery notes, reducing errors and automating administrative workflows.

• Predictive maintenance: AI identifies weak signals (vibrations, temperature, mileage, history) to anticipate failures, ensuring better fleet availability and eliminating unplanned downtime.

4. Major Applications of AI in Warehouses

• Optimization of batching and picking paths: AI optimizes order preparation and increases picker productivity by reducing unnecessary movements, intelligently grouping orders, and assigning them optimally.

• Automated inventory using embedded cameras: Vision systems, combined with AI, enable real-time inventory tracking and reduce manual tasks prone to errors.

• Slotting: Decision-making AI determines the optimal location of each product in your warehouse based on demand, picking frequency, product families… Result: smoother flows, reduced preparation time, improved quality of work.

• Improved safety through computer vision: AI-based computer vision detects risky behaviors and improves monitoring of sensitive areas and collision prevention.

5. What is the Impact of Decision-Making AI in Supply Chain?

Decision-making AI is now the most powerful AI lever in supply chain operations. Its benefits are significant:

• +30% performance gains
• -20% CO2 emissions
• Planning time divided by 5

With highly advanced decision-support features, it enables better resource allocation, more precise modeling of business constraints, and end-to-end synchronization of the supply chain.

AI is no longer an emerging technology: it is becoming a standard for supply chain operations. Companies that adopt it now strengthen their agility, resilience, and operational performance.