AI in Supply Chain Management

Artificial intelligence does more than make supply chain management faster: it changes who plans, who controls operations, and where decision-making ends and execution begins. The linear supply chain becomes a network that corrects itself across locations, functions, and company boundaries. The pace of change is reflected in the BVL-Trendstudie 2025/2026: AI use in logistics and supply chain management has more than tripled since 2023.

Most companies currently use AI as an assistant: it summarizes analyses, creates reports, and explains deviations. This helps individual employees but changes neither the process nor the role of the function.

The real leap begins when people and AI agents share the work of planning and control. The roles are clearly defined: people set goals, define rules and boundaries, and decide on exceptions. Agents observe, plan, act, and coordinate with one another – beyond their own department and across suppliers and customers.

The key question is not whether AI is being used, but how much work it actually takes on. Five stages provide a useful framework:

The move from stage 3 to stage 4 is primarily a technical challenge. The move to stage 5 is an organizational one. This is where individual tools can become a new operating model.

Supply chain management comprises not one but three layers of work. AI reaches each at a different pace and under different conditions. Overlook this, and your timelines will be based on the wrong assumptions.

The bottleneck is rarely the AI model. It lies in the data and in how precisely planning and control rules are defined today.

AI has an impact across the supply chain, from planning to physical execution. Depending on the maturity of each area, the following use cases range from assistance to autonomous agents.

Not every use case is worth pursuing immediately. The best way to prioritize is to consider how much of the work can be captured in rules alongside a demonstrable business case. Start with what can be governed by rules and creates value.

The journey toward an autonomous supply chain is not one large-scale program. It consists of three building blocks. Each stands on its own, and all three work together.

Operations must grow alongside the agents. A Supply Chain Excellence unit maintains agents and rule sets, safeguards quality and compliance, and turns recurring patterns into new standards. Delay this capability, and you risk accumulating agents that no one develops further.

AI in supply chain management does not simply reduce headcount. It changes where expertise is needed. Demand falls sharply in operational scheduling, moderately in planning, and rises in the enabling teams that develop agents and maintain rule sets. Some of the capacity freed up moves into capabilities that many supply chain organizations do not yet have.

The traditional distinction between operational, tactical, and strategic work begins to dissolve. Three ways of working take its place:

• The orchestration role. It sets goals, thresholds, and priorities across multiple value streams and agent chains without intervening in individual transactions.
• The specialist role. It works where standard logic ends: network design, complex capacity decisions, and critical supplier relationships. It also shapes the rules agents follow and approves the decisions and processes agents have prepared.
• The supported business role. Plants, sales, and service handle standard needs themselves, following rules set by supply chain management.

This work converges in the control tower. Rather than just reporting, it becomes the place where exceptions are decided and rules are refined.

An agentic organization does not require a disruptive rebuild. Two initiatives run in parallel: high-impact use cases on the existing system landscape and the development of a scalable agentic platform. The first delivers value quickly; the second sustains the model over time.

• Supply chain is our core business, not just a use case. We define process rules based on domain expertise and best practices, not on a system manual.
• Data and digitalization expertise. Our Data & AI Practice provides the technical depth an agentic operating model needs, from data products to the orchestration layer.
• Vendor independence. valantic does not sell agents or planning software. Our recommendations on building, partnering, or buying are technology-agnostic.
• Implementation without handoff gaps. Planning, production, logistics, IT, and compliance must work together in an agentic model. We connect domain expertise with technical implementation so that rules, data, and agents fit together.

AI use in logistics and supply chain management has more than tripled since 2023 (BVL-Trendstudie 2025/2026).

This is precisely where our approach comes in.

An agentic model is only as strong as its weakest dimension. The key question is not which maturity level you aspire to, but which dimension of your supply chain management currently lags furthest behind. We assess this with you, dimension by dimension, as a basis for your first decision rather than a ready-made program.

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