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How do I build a data-driven supply chain?
Treat AI as one layer in an operating capability, not a shortcut around fragmented systems. A forecast or recommendation is only as useful as the information behind it, the process that acts on it and the evidence that it improves an outcome.
A practical roadmap moves through six linked stages: set outcomes and governance; select a use case and baseline; establish the data foundation; choose an implementation route; validate the model and connect it to work; and lead adoption while measuring value. Each stage should leave the next one with a clear owner and a decision about whether to proceed.
Set a portfolio, not a collection of pilots
Gartner’s June 11, 2025 survey found that 23% of surveyed supply-chain leaders reported a formal supply-chain AI strategy. The survey covered 120 leaders whose organizations had deployed AI in the previous 12 months, with fieldwork from December 2024 to January 2025; it is not a measure of all supply-chain organizations.
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Gartner recommends a documented strategy and a balanced Run-Grow-Transform portfolio. Run investments target efficiency and cost optimization, including automation and predictive maintenance. Grow initiatives integrate AI into established processes such as sales and operations planning. Transform initiatives may pursue consumer insights or proactive demand shaping. Keep the portfolio connected so that successful projects can share data and architecture rather than becoming isolated systems. As Benjamin Jury, Senior Principal, Research in Gartner’s Supply Chain practice, puts it: “Without a structured approach, organizations risk creating inefficient systems that struggle to scale and adapt to evolving business demands.”
Where should we start with AI in supply chain?
Start with a specific operational decision, not a technology shortlist. A candidate should matter to the business, have sufficiently accessible and reliable data, fit a real workflow, and produce a result that can be compared with a baseline. No universal scoring formula or ROI target is established by the cited sources, so set criteria around your own operating priorities.
| Use-case family | Possible decision to support | Data and baseline to examine |
|---|---|---|
| Demand forecasting and inventory | Forecast demand or inform replenishment and inventory decisions. | Relevant historical sales plus other useful internal and external signals; compare forecasts with the existing approach or a simple model. |
| Procurement and supplier management | Automate procurement tasks, assess supplier performance or identify supplier risk. | Procurement and supplier information; define the current process and a measurable performance baseline before implementation. |
| Transport and logistics | Support logistics or transportation planning and respond to shipment or disruption conditions. | Shipment status and relevant transport information; establish the current decision process and outcome measure. |
| Production and replenishment | Inform production or replenishment decisions, including responses to potential disruption. | Planning, execution and inventory information; agree how the current decision and result will be measured. |
These are application families, not promises that every organization has the data or process maturity to use them. OECD’s 2025 chapter on AI in manufacturing and Gartner’s guidance describe relevant use cases, while Gartner also identifies data completeness and accessibility, unclear vision and resistance to process changes as barriers to adoption.
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Define the baseline before the pilot
Record how the decision is made today and what outcome matters—for example, the organization’s chosen measure of forecast performance, inventory, service, response time or process effort. Specify the population, time period and exclusions so a later comparison is fair. Agree who owns the baseline and who will report the result; a model’s accuracy alone does not show that it changed a decision or improved an operating outcome.
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What data do we need for AI demand forecasting?
There is no single universal data list. Gartner recommends moving beyond historical sales alone and incorporating relevant internal and external information. The right inputs depend on the decision, the organization’s business and which signals are complete, accessible and timely enough to use.
Map sources, owners and meaning
Inventory the systems and data owners that support the planning decision. Depending on the organization, relevant sources may include ERP, CRM, warehouse management (WMS), order management (OMS), transportation management (TMS), procurement, shop-floor devices, planning systems and shipment status. External signals such as weather may also be relevant where they help explain the decision being forecast.
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For each source, establish what its fields mean, how identifiers match across systems, how often information is refreshed and who is responsible for its quality. Ingest and standardize the selected information, catalog it so users can find and interpret it, and apply governance for access and use. Planners and analysts need reliable ways to query and visualize the resulting data; models should sit on top of that foundation rather than conceal its gaps.
Use architecture as a sequence, not a shopping list
AWS’s official “Guidance for Deploying a Supply Chain Data Hub on AWS” illustrates one possible flow: collect source data, ingest it into a data lake, transform and catalog it, make it available for queries and dashboards, then add machine-learning models and network-graph queries. It is an AWS-specific example, not a requirement or neutral ranking of architectures. Choose a design that fits existing ERP, planning and execution systems, governance needs, analytical workloads, scale and the way decisions are made.
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Compare build, buy, customize and partner options against the same business and technical requirements. Deloitte’s 2024 framework relates the choice to competitive advantage and access to unique data: buying can suit standard needs; building may make sense when unique data and internal technical capability support differentiation; customization or a partner can adapt or extend external products.
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| Route | May fit when | Questions to test |
|---|---|---|
| Buy | The need is relatively standard and an external product fits it. | Does it integrate with current systems and workflows? Can the organization govern its data and outputs? How dependent will it become on the vendor? |
| Build | Unique data and internal technical capability can create a meaningful differentiator. | Does the organization have the talent to develop, operate and maintain it? Is the differentiation worth the integration and ongoing ownership burden? |
| Customize | An external product provides a base that needs adaptation to local data or processes. | What changes are needed, who maintains them and how will upgrades or vendor changes affect them? |
| Partner | An external organization can extend capability or help implement a solution. | Which expertise and responsibilities are supplied, how will knowledge transfer work, and how will access, governance and ongoing dependency be handled? |
For any route, check integration fit, security and governance, available talent, time to deploy and vendor dependence alongside competitive differentiation and data access. The cited material does not establish a universally best route, architecture or vendor, and it does not provide a current vendor-by-vendor product comparison.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do we validate models and connect them to decisions?
For forecasting, Gartner recommends using relevant internal and external data, explaining uncertainty and benchmarking AI forecasts against simple models. A more complex model should earn its place by improving a decision or defined outcome, not by appearing sophisticated.
- Set the comparison. Compare the AI output with the current process or an appropriate simple model using the baseline and evaluation period agreed for the use case.
- Make uncertainty usable. Explain forecast uncertainty in a way planners can interpret, and define which exceptions require review rather than presenting an estimate as certainty.
- Assign decision rights. Specify who reviews exceptions, who can act on a recommendation and who remains accountable for the planning decision.
- Put outputs in the workflow. Deliver the forecast or recommendation where the decision is made, then track whether it changed a decision and whether the chosen outcome moved.
Jan Snoeckx, Director Analyst in Gartner’s Supply Chain practice, describes potential benefits this way: “The value of AI-based forecasting includes improved strategic decision making, faster responses to market changes, and enhanced collaboration workflows.” These are potential benefits, not a guaranteed result for every implementation.
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How do we lead adoption and scale responsibly?
Process redesign, employee trust, skills, communication and adoption reporting belong in the implementation plan. If a recommendation changes who does what, when a decision is made or how exceptions are handled, define that change with the people doing the work. Explain what the system can and cannot establish, how uncertainty is represented and when human judgment takes precedence.
Track whether intended users can access and understand outputs, whether the workflow is being followed and whether the agreed business measure changes. Deloitte’s 2024 guidance highlights employee trust and continuous tracking of adoption and value; Gartner also identifies process resistance, unclear vision and data limitations as adoption obstacles.
Gartner predicted in September 2025 that 70% of large organizations would adopt AI-based supply-chain forecasting to predict future demand by 2030. This is a forecast, not a measured adoption rate or a promised outcome for an individual organization. Its strategic implication is to prepare a scalable capability, not to skip validation or pursue AI for its own sake. Expand to another use case only when the current one demonstrates value and the underlying data and workflow can support the next decision without fragmenting the architecture.
How do we measure supply-chain AI ROI?
The reviewed sources do not establish a general, independently comparable ROI percentage for supply-chain AI. Avoid treating a generic savings figure as a forecast for your organization. Build the case from a measured baseline and the outcomes that matter to the operation.
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- Record the starting performance and the period, scope and decision process it represents.
- Choose a small set of business outcomes tied to the use case, such as the relevant service, inventory, response or process-effort measures.
- Track model performance separately from adoption and operational results: a forecast can improve without being used, and use can increase without improving the target outcome.
- Report results over time, explain material changes in scope or operating conditions, and use the findings to decide whether to improve, scale or stop the initiative.
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