Execution, not chat: How Agentic AI changes supply chain operations

In the last couple of years, most “AI in the supply chain” solutions have been pretty similar: a “chatbot-like” assistant stacked on top of SOPs, dashboards, and ticket queues. The solutions are certainly useful, especially for companies where knowledge is stored as a distributed system of documents, emails, and tribal knowledge. The solutions can certainly help answer questions, summarize meetings, write messages, and help planners find what they need more quickly.

However, they also completely ignore the most difficult part of the job: execution.

Prabhat Rao Pinnak

What is execution? Execution is what happens after insight. It is the inter-systemic process of converting a signal into a controlled sequence of actions: re-promising dates, re-allocating inventory, opening a supplier claim, placing inventory on hold, moving a load, documenting decisions for audit purposes, and coordinating a clean escalation when a business policy is violated. In most companies, this process is still completely manual across ERP, WMS, TMS, emails, spreadsheets, and human handoffs.

And the delays are completely predictable. The exception sits idle in a queue. The handoff is missed. The exception escalates late. The organization suffers an “execution tax” measured by expedite fees, rework costs, inventory held idle, and service missed.

Ramakrishna Garine

This is the reality that is fueling a fundamental shift in focus, away from assistants and toward agents. According to Gartner, by 2030, half of all cross-functional supply chain management solutions will have agentic AI capabilities. This means systems that not only make decisions but can execute those decisions autonomously throughout the entire ecosystem. The word execute is what is important here. This is what will differentiate having a smart way to talk about problems from having a way to actively shorten the cycle time between detecting a problem and acting upon it.

However, this is not to say that this is a technology to be undertaken without caution. According to Gartner, more than 40% of all agentic AI projects will be abandoned by the end of 2027, as the business value is not clear or as risk controls are immature. This is not a technology to be undertaken lightly; it is, however, a vital reminder that agentic is not something that is bolted onto a technology; it is something that is built.

Defining agentic in the supply chain context

In a supply chain context, agentic should not mean “autonomy without supervision.” What scales is bounded autonomy: the ability to act within explicit rules, permissions, and escalation thresholds.

A production-grade execution agent needs to do four things consistently:

• Situational awareness: The agent needs proactive situational awareness about real-time events, exceptions, queue aging, and SLA risks without waiting for a prompt. This is a necessary condition for a reaction at the time of disruption rather than after a user sees a prompt on a dashboard.
• Constrained decision-making: Supply chain execution is not an optimization problem; it is a decision-making process that involves trade-offs based on rules. The agent needs to reason within critical constraints like service tiers, cost limits, inventory status, capacity limits, and customer commitments. The agent also needs to be able to explain its decision-making process for its chosen action.
• Ability to act: To be truly transformative, an agent must have the authority to act by calling tools and workflows—creating tasks, setting holds, re-promising dates, and routing exceptions—moving beyond a mere recommendation engine.
• Clean escalation: For low confidence and high-impact situations, the agent has to cleanly escalate the problem. This includes sending out a clean decision package explaining what happened, what is impacted, what has been attempted by the agent, options, and a recommendation. Trust is critical in an execution system, and hence traceability is not an option.

The objective is not to eliminate human involvement but to conserve human attention by automating steps already defined by policy. Human judgment should only be required for true escalation. Traceability and repeatability, not casual interaction, are the true foundations for building trust in execution systems.

Putting theory into practice: The exception loop

To illustrate the difference between a chatbot and an execution system, focus on exceptions, as these are the points where operational costs increase. Agentic AI is particularly effective in resolving these exceptions. Here are two examples of how agentic AI can be used to handle supply chain exceptions:

Warehouse management system (WMS): Addressing receiving discrepancies

A damaged pallet shows up. Labels don’t scan. Quantities don’t match the ASN. In many operations, that becomes a dock-side slowdown: someone investigates, someone approves, someone initiates a claim, and the inventory sits in limbo. It’s physically in the building, but it can’t be used. That’s how you end up short even when you “have” inventory.

A bounded agent can take the first steps quickly and consistently. It can place inventory into the right status, route it to inspection or cycle count, accept within tolerance if policy allows, or open a claim with evidence if it doesn’t. And when it needs a supervisor’s approval, it can escalate with the context already assembled instead of creating a scavenger hunt.

Transportation (TMS/control tower): Addressing tender rejections and late pickups

A tender gets rejected or a pickup slips. Advisory AI can summarize the situation and tell you the risk. Execution requires the next moves: re-tender within approved cost and service limits, propose alternatives if SLA risk is rising, update milestones, notify stakeholders, and open the right tasks. When the action crosses policy—premium freight, customer exception, compliance constraints—that’s when it escalates.

Across these two examples the pattern is the same: the value comes from compressing the detect–decide–act loop without letting the system run outside guardrails.

Ontology: The overlooked essential

Here’s where a lot of pilots fall apart. The system can “talk” about the supply chain, but it doesn’t understand the supply chain well enough to act safely.

A supply chain isn’t just data. It’s relationships and constraints: orders are tied to promises, inventory tied to locations and statuses, shipments tied to capacity and customer expectations, exceptions tied to allowed actions and escalation paths. In knowledge engineering, a structured way of representing those concepts and relationships is an ontology. Ontology Web Language (OWL) exists as a standard for expressing that kind of relationship-rich knowledge so software can reason over it.

Practically, this is what prevents “locally correct, globally wrong” automation and why “operational digital twin” platforms like Palantir’s Foundry focus on object relationships, not just dashboards. Without that map, autonomy is brittle; with it, execution can scale.

Takeaway: if you want agentic AI to execute reliably, treat the ontology (your operational truth model) as core infrastructure, not as an afterthought. The model can be smart, but without a shared map of relationships and rules, autonomy won’t scale.

Scaling from pilot to production

If 40% of projects are to be cancelled as per Gartner’s assessment, it won’t be because teams chose the wrong foundation model. It will be because they didn’t build the operating foundation. Here are the production realities most teams underestimate

• Telemetry: You need end-to-end visibility into events, state transitions, outcomes, and overrides. Without that, you can’t measure performance or improve it.
• Safe integration: Agents must act through reliable APIs and workflows, with rollback paths and clear system-of-record ownership. If an agent changes an inventory status or updates a promise date, that action must be auditable and reversible.
• Authority boundaries: Governance must be explicit: what can be auto-executed, what requires approval, and what must always escalate. This is where risk frameworks become useful scaffolding
• Human-on-the-loop operating model: The manager’s role shifts toward system architect and coach. Overrides shouldn’t be treated as failure but as feedback. When humans disagree with the agent, that signal should be used to refine thresholds, playbooks, and ontology.

Measuring what matters

A common pitfall is measuring agentic AI like a simple chatbot, focusing only on usage and satisfaction. While these are okay, they don't prove the actual execution value in a supply chain context. True execution value must be measured with operational metrics, specifically:

• Touchless resolution rate (by lane): How many exceptions did the AI close completely without human intervention?
• Decision latency: How fast was the AI in closing exceptions, including those that were aged?
• Brittleness and boundary issues: How frequently did humans need to override or roll back the AI's decisions?
• Cost-to-serve impact: Did costs change due to factors like expedites, rework, or leakage?
• Service improvement: Was there an enhancement in service metrics like promise reliability, OTIF (On-Time, In-Full), and backlog aging?
• Policy compliance: Did the AI operate within established safety and audit standards?

These measures do more than just justify the return on investment (ROI); they are essential indicators of whether it is safe and prudent to grant the AI expanded autonomy.

Concluding Note

Supply chain leaders should focus on having the right operational model and guardrails to let AI execute work responsibly, not just whether to adopt it. A practical approach involves starting with a high-volume process (e.g., receiving variances) and formalizing its ontology: objects, relationships, allowed actions, and escalation rules. Then, instrument telemetry, define authority boundaries, run shadow mode, and only allow AI to auto-execute low-risk steps. Expand autonomy only when metrics confirm it is superior to manual execution. Companies that build ontology-backed, governed execution will set the competitive standard, while those that stop at advisory assistants will get value but pay an "execution tax."

References

Gartner. (2025a, May 21). Gartner predicts half of supply chain management solutions will include agentic AI capabilities by 2030.  https://www.gartner.com/en/newsroom/press-releases/2025-05-21-gartner-predicts-half-of-supply-chain-management-solutions-will-include-agentic-ai-capabilities-by-2030

Gartner. (2025b, June 25). Gartner predicts over 40 percent of agentic AI projects will be canceled by end of 2027. https://www.gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027

Noy, N. F., & McGuinness, D. L. (2001). Ontology development 101: A guide to creating your first ontology. Stanford Knowledge Systems Laboratory. https://protege.stanford.edu/publications/ontology_development/ontology101.pdf

World Wide Web Consortium. (2004, February 10). OWL Web Ontology Language overview. https://www.w3.org/TR/owl-features/

About the authors

Prabhat Rao Pinnaka is a data and AI-driven product leader building enterprise platforms that improve execution and decision-making across the end-to-end supply chain - from planning and procurement to warehousing, transportation, and customer fulfillment. He leads cross-functional teams delivering analytics and AI-enabled workflow solutions, with a focus on governed automation, digital twins, and responsible deployment at scale. A keynote speaker and peer reviewer for AI, operations research, and supply chain papers, he shares practitioner perspectives on how organizations adopt AI in real workflows

Ramakrishna (Ram) Garine is a supply chain analytics and AI practitioner and Thought leader, focused on disruption planning, simulation, and decision intelligence. His work spans applied machine learning for forecasting and risk, resilience metrics, and practical methods to evaluate mitigation policies such as alternate routing, safety stock, and supplier diversification. Ram contributes to professional communities through speaking, peer review, and research dissemination, and he builds tools and frameworks that make advanced resilience testing accessible to both industry teams and learners.

SC
MR