Gartner expects more than 40% of agentic AI projects to be cancelled by the end of 2027. Here is how supply chain leaders can tell, before they scale, which of theirs will make it.

Agentic AI in supply chain is a class of software in which AI agents plan, decide, and execute operational actions, such as reorders, stock transfers, and supplier escalations, within defined limits, instead of only producing forecasts for a planner to act on. Gartner named agentic AI among its top supply chain technology trends for 2026, and the category is moving from pilot to budget line quickly. The attrition is about to start.

Spending on supply chain management software with agentic capabilities will rise from under $2 billion in 2025 to $53 billion by 2030, according to Gartner's April 2026 forecast. Yet Gartner also predicts that over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. The projects that survive will not be the ones with the best models. They will be the ones that answered five operating questions before they scaled.

Why Most Agentic AI Evaluations Ask the Wrong Question

Most evaluations of supply chain AI agents ask whether the agent works, when the question that predicts survival is whether the decision it owns is worth automating at all. A demo proves an agent can generate a purchase order. It does not prove the order was a decision worth taking off a planner's desk, that the savings will outrun the integration bill, or that anyone knows what happens when the agent is wrong.

The evidence points the same way. BCG's June 2026 analysis of AI-first supply chains found that 44% of companies are deploying AI in supply chain management, more than in finance, HR, or procurement, but that most remain stuck on narrow use cases and copilot-style tools that deliver marginal gains. Adoption is not the constraint. Selection is.

Gartner's own diagnosis is blunter. "Many use cases positioned as agentic today don't require agentic implementations," said Anushree Verma, Senior Director Analyst at Gartner. That suggests a meaningful share of cancellations will come not from agents that failed, but from agents deployed where a rules engine or a better report would have done the job at a fraction of the cost.

Five Questions That Separate Surviving Agentic AI Projects From Cancelled Ones

The five questions below map directly to the three causes Gartner names for cancellation, plus the market problem it calls agent washing. A project that cannot answer all five clearly is not ready to scale, however well the pilot performed.

1. Does this decision actually need an agent?

An agent earns its cost only where decisions are frequent, involve trade-offs across several systems, and gain value when made hours earlier. BCG's guidance is to start where decision density and value intersect: ambiguous trade-offs that span multiple systems and benefit from continuous reassessment. Daily store-level replenishment across thousands of SKU-location pairs qualifies. A quarterly supplier review usually does not.

A good answer names the decision, how often it happens, and what a slower or worse decision costs today. A weak answer describes the technology.

2. Which metric moves, and by how much, before go-live?

Unclear business value is the most common reason projects die at budget review, and it is almost always a pre-launch failure. A surviving project commits to one operational metric, such as fill rate, expedite spend, inventory days, or planner hours per week, with a baseline measured before the agent is switched on.

The upside is real when the target is specific. In BCG's example of a global consumer goods company, agent-supported replenishment raised fill rates and in-stock levels while administration costs fell 40% to 60%. BCG also projects working capital reductions of up to 30% for some organizations. Those numbers were attached to named workflows, not to "AI adoption."

3. What can the agent do without asking, and who owns the rollback?

Inadequate risk controls usually mean nobody defined the agent's authority. Before scale, every agent needs a written boundary: the actions it can execute alone, the thresholds that trigger human approval, and the person accountable for reversing a bad decision.

This is also where value leaks. An agent that can recommend but must wait two days for sign-off is a slower dashboard. Heizen has argued that approval latency, not model accuracy, is the real bottleneck in supply chain AI, which makes governance design and the value case the same conversation. Gartner's April 2026 forecast makes the point from the other side, advising leaders to set appropriate levels of human-in-the-loop, particularly in early deployments.

4. What does it cost at full scale, not in the pilot?

Pilots hide costs. A pilot running on one region, one category, and a cleaned data extract does not pay for ERP and WMS integration, exception handling, monitoring, model usage at full transaction volume, or the planner time spent reviewing outputs. Those are the escalating costs Gartner warns about.

A good answer is a fully loaded run-rate estimate at target scope, set against the metric from Question 2. If the business case only clears at pilot scale, it does not clear.

5. Is it actually an agent?

Gartner estimates that of the thousands of vendors claiming agentic AI, only about 130 offer genuine agentic capabilities. The rest are what it calls agent washing: AI assistants, chatbots, or RPA tools rebranded as agents.

Three tests cut through the label. Does the system act inside operational systems, or only produce text? Does it retain state across sessions, so it remembers prior decisions and supplier history? Does it reason through exceptions, or follow a fixed script that breaks on the first edge case? Heizen covered the memory test in detail in Stateless "Agents" Are the New Vaporware.

How Heizen Sequences Agentic AI Deployments

Heizen treats agentic AI as a sequencing problem before it is a software problem: decision first, metric second, authority third, technology last. Heizen is an AI-native software delivery company that builds supply chain systems for enterprise CPG and manufacturing companies, and that order reflects what tends to break in production rather than in demos.

In practice, an engagement starts by ranking candidate decisions on frequency and cost of delay, then baselining the target metric, then writing the authority boundary with the operations owner who will be accountable for it. Only then is the agent built, usually as a decision layer over the existing ERP rather than a replacement for it. Structuring the work as outcome-based sprints ties spend to the metric the agent is meant to move, which keeps Question 4 honest. The AI in Supply Chain Planning whitepaper sets out how that sequence applies to planning workflows.

The 2027 Cancellations Are a Selection Problem, Not a Technology Verdict

Gartner's forecast is not a verdict on agentic AI. The same April 2026 research expects 60% of enterprises using supply chain management software to adopt agentic AI features by 2030, up from 5% in 2025. Both numbers will be true at once. The cancellations will mostly fall on projects that automated the wrong decision, never named the metric, or skipped the authority design. The supply chain organizations that come out ahead will be the ones that asked these five questions while the pilot still looked like a success.