Agentic AI and Supply Chain: Intelligent Agents to Streamline Supplier Collaboration and Secure Supply Flows

In a context where supply chains are under increasing pressure, companies have no choice but to be more responsive, more reliable, and more transparent in their interactions with suppliers. Yet a large part of operational management remains highly manual: chasing updates via email, tracking in Excel, copying and pasting supplier confirmations into the ERP…

Over the past few years, artificial intelligence has gradually made its way into the supply chain. But now, a more sophisticated wave is marking a turning point: agentic AI. Unlike "traditional" AI tools (such as OCR or rule-based engines), agentic AI is based on autonomous agents, capable of understanding a context, acting proactively, and interacting with both humans and systems.

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Applied to supply management, this technology already offers tangible, accessible use cases. Let’s take a closer look.

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Agentic AI: What Are We Talking About Exactly?

Agentic AI refers to intelligent systems capable of making local decisions (agent by agent), while remaining integrated within a broader ecosystem. Each agent can perceive its environment, process unstructured information (texts, documents, emails…), decide on actions, and execute them — either autonomously or in collaboration.

Unlike OCR tools or data capture technologies that merely extract information, agentic AI can:

• understand a business context,
• interact with a supplier via email or API,
• trigger a reminder when a delivery date isn't confirmed,
• or suggest a prioritized action for the user.

In other words, we move from AI as a tool to AI as a colleague working alongside supply teams.

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Three Usage Levels: From Passive AI to Decision-Making AI

Not all agentic AI is created equal. Today, we can distinguish three levels of maturity and operational impact — which may coexist within the same solution:

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1. Passive AI – Contextual Extraction and Understanding

This is the starting point. The agent reads supplier emails, automatically extracts key data (references, quantities, dates...), and formats it into structured outputs (xls, csv…) to ease integration into business tools (ERP, management platforms, etc.).

For example, after reading an email received by the supply planner, the AI agent identifies that a supplier confirmed a different date from the one originally scheduled. It automatically generates an alert or a structured file containing the original date, the new date, and the deviation. This avoids manual re-entry of unstructured data into structured formats.

Companies like Kavida, Turian, Nordon, and Didero focus mainly on this approach: smart reading, structuring, and classification — sometimes with semi-automated validation interfaces.

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2. Proactive AI – Tracking and Follow-Up Agents

At this stage, the agent doesn't just wait for information; it actively follows up, sends confirmation requests, tracks responses, and issues alerts when no reply is received.

Let’s take the case of a supplier who hasn't responded to a confirmation request within 48 hours. The agent automatically sends a reminder, updates the status in the platform, and notifies the user if no response comes after 72 hours.

Solutions like Sam by FourKites, and soon Winddle, with their natively business-oriented vision, offer this kind of conversational automation combined with structured flow management.

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3. Recommendation AI – Operational Decision Support

At the most advanced level, agentic AI supports decision-making by cross-referencing data sources to identify risks or suggest action plans. It acts as a copilot, prioritizing tasks, raising smart alerts, or recommending trade-offs.

For example, the AI agent identifies multiple critical delays from the same supplier. It may suggest grouping shipments, prioritizing certain products, or reallocating logistics resources to reduce downstream impact.

However, this use case relies on structured, up-to-date business data: order criticality, customer delivery constraints, inventory levels, transport visibility, etc.

Without this solid foundation, the AI can only make rough assumptions — leading to irrelevant recommendations. In short, this intelligence only works if the AI is deeply integrated into business systems and operational processes. That’s the key difference between a promising technology and a truly useful tool.

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Key Success Factors to Unlock the Value of Agentic AI

The impact of agentic AI depends not just on the tech, but on how it's embedded in the organization and tools.

Here are the essential levers:

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AI Embedded in Business Context — Not in a Silo

Even a powerful AI won’t deliver much if it’s not connected to clear operational workflows. It’s not a standalone chatbot or analysis widget. Real gains emerge when the AI is embedded into a supply management platform, connected to the ERP, planning systems, and logistics constraints.

An AI agent may trigger a follow-up and process the response (or lack thereof 😉), but only the operational tool knows if the order is critical. Without that connection, recommendations lose relevance.

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AI as Copilot — Not a Replacement

The goal isn't to replace supply teams, but to offload repetitive, low-value tasks, allowing teams to focus on analysis, anticipation, and handling true exceptions.

Instead of manually following up with 50 suppliers, the supply planner only handles the 4 cases where the AI couldn’t get a response or detected an anomaly. They also benefit from a real-time view of order progress, statuses, key dates, required and missing documents — and receive personalized alerts for operations requiring attention or intervention.

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AI That Can Handle Real-World Complexity

Unstructured language, typos in emails, shifting timelines, multiple contacts… Reality is messier than a clean dataset.

Agentic AI must be able to handle ambiguity, adapt to varied formats, and ask for human input when needed.

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Conclusion: Agentic AI Is a Practical Lever to Modernize Supplier Management

We’re no longer talking about a distant promise — real use cases are already here, deployable, and measurable.

Agentic AI enables more reliable data, time savings, and improved supplier collaboration — as long as it’s designed as a business accelerator, not a shiny tech gadget.

At Winddle, this vision drives our roadmap: enhancing our supply management platform with agentic AI that is useful, embedded, and concrete — to free up the full potential of supply teams in an increasingly demanding environment.

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