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Sage’s supply-chain framework gives predictive, generative, and agentic AI different jobs: anticipate what may happen, help people make sense of information, and advance bounded tasks. It puts connected, reliable data beneath all three. That is a useful way to assess proposed AI workflows, but it is Sage’s framework and guidance—not proof that every business needs the same tools or will automatically save money.
What are the three kinds of AI in a supply chain?
The practical distinction is what the system does, not what a vendor calls it. Predictive AI estimates future conditions; generative AI produces or explains information; agentic AI uses information and tools to move a task toward an objective within defined rules and permissions.
| Type | Primary job | Supply-chain examples |
|---|---|---|
| Predictive AI | Estimate likely future outcomes | Forecast demand or inventory needs; estimate supplier performance; anticipate equipment failures. |
| Generative AI | Interpret, summarize, or create information | Summarize shipment exceptions, answer questions, draft reports or communications, or help users understand a forecast. |
| Agentic AI | Progress a bounded, multi-step task using available information and tools | Identify delayed shipments and affected orders, compare possible responses, and prepare or initiate an approved follow-up. |
These capabilities can work together. A predictive system may flag a likely shortage; a generative interface can explain the forecast and summarize the affected orders; an agent may then compare alternatives or carry out a permitted next step. Sage describes these roles in its overview of AI in supply chain management.
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A generative system can make operational information easier to interrogate: for example, it might summarize late shipments and list the orders they affect. That does not mean it produced the underlying demand forecast or changed the orders. An agent goes further by using tools and permissions to advance work—perhaps checking alternatives and preparing an approved supplier follow-up.
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The boundary is consequential. Generating a recommendation is not the same as executing it. Whether a system may send a message, change an order, or commit to a customer should depend on the workflow’s risk and the authority explicitly granted. Sage discusses this distinction in its explanation of generative AI versus agentic AI.
Why connected data is the foundation
An AI workflow is only as useful as the information and context it can access. Sage argues that ERP can connect the operational and financial context behind decisions, including purchasing, inventory, production, sales, finance, customer orders, supplier information, and costs. The precise data required depends on the task; related documents and communications may also provide useful context.
For example, a system asked to assess a late shipment needs more than a delay notice if it is expected to identify affected customer orders or compare responses. It may need current order, inventory, supplier, cost, and delivery information. If these sources are disconnected, stale, poorly governed, or inaccessible under the system’s permissions, a fluent summary or fast action can still be wrong.
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How to adopt AI without handing over too much authority
Sage’s recommended path is to begin with a defined workflow and expand system authority only as results and controls prove workable. A practical version looks like this:
- Choose one repeatable workflow. Pick an information-heavy task or recurring exception, name an owner, and decide what outcome matters—for example, faster exception response or fewer missed service commitments.
- Check the data and access. Identify the records and documents the task depends on. Confirm they are current and reliable, and that the system has only the access needed to do its work.
- Set review and escalation rules. Specify what the AI may summarize, recommend, prepare, or execute. Keep explicit human approval for consequential choices such as supplier changes, expensive expedites, and customer commitments.
- Establish a baseline. Record current performance before rollout, using a metric tied to the workflow: response time, inventory, service, cost, or another relevant measure.
- Measure in operation, then adjust authority. Compare outcomes with the baseline and inspect whether controls hold up in real use. Expand autonomy only when the workflow’s results and safeguards justify it.
This is Sage’s guidance, not a universal deployment sequence or a guarantee of savings. The core discipline is to define the task, data, authority, and measure before allowing a system to act.
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How to judge the claims and figures
Numbers cited around supply-chain AI describe different things: adoption surveys, vendor-reported findings, third-party comparisons, and forecasts are not interchangeable evidence. Sage’s articles and reports relay the following figures with different qualifications:
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|---|---|---|
| 53% already use AI to anticipate and mitigate supply-chain disruptions; another 31% are testing or piloting it | Reported organizational use and experimentation | Sage attributes these figures to PwC’s 2025 Digital Trends in Operations survey in its supply-chain AI article. They describe survey responses, not measured causal impact. |
| 15% lower logistics costs, 35% lower inventory, and 65% higher service levels | Comparisons associated with AI-enabled supply chains | ERP Today’s account of Sage’s first article relays these as McKinsey & Company figures. The year and exact underlying report are not stated in that account, so they should not be treated here as a fully checked primary result. |
| 60% of supply-chain disruptions resolved without human intervention by 2031 | A future prediction | Sage’s account of agentic AI in the supply chain attributes this to a Gartner forecast. The forecast year is not stated in the cited Sage excerpt; this is a prediction, not a measured current outcome. |
| 50% of brands entered 2026 lacking confidence in their response to disruptions; 10% reported AI live in supply-chain workflows | Reported confidence and deployment status | Sage reports these results from its 2026 State of Supply Chain Report in an article dated October 2, 2026: Sage’s 2026 supply-chain AI article. |
These figures cannot by themselves show that AI caused a particular reduction in costs or inventory, or that an organization’s deployment will produce the same result. Before comparing vendors or business cases, check what was measured, who reported it, the date and population, and whether the claim concerns adoption, a forecast, or an outcome.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to compare when evaluating supply-chain AI
Compare systems against a specific workflow rather than choosing by label. The useful questions follow from the task and the controls it requires:
Quick Recap
- Capability: Does it forecast, explain or generate information, or take action? What steps does it actually perform?
- Data access: Which ERP and operational sources can it use, how current is the information, and how are it and related documents governed?
- Permissions and review: What actions can it take, which require approval, and where are escalation boundaries set?
- Accountability: Can people understand why it raised an exception or chose an action, and is there an audit trail of recommendations, approvals, and changes?
- Implementation: What integrations and data preparation are needed to support the intended workflow?
- Evidence of value: Which workflow-specific outcome will be measured against a baseline, and who owns that measurement?
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