Where Should AI Start in Your Supply Chain?

Choose the decision before the technology

A planner receiving hundreds of alerts does not necessarily need another dashboard. They may need a reliable way to identify which shortages threaten a customer promise and who can respond. The useful starting question is what decision should improve, for whom and under which constraints.

BCG’s 2026 planning research argues that technology generates value through the way it is embedded in planning processes, supported by people, data and governance. Its consultancy perspective is a useful challenge to technology-first programmes, rather than proof that any particular AI product will deliver a return. ^[1]

Separate three different kinds of assistance

SCP would distinguish calculation, interpretation and execution. A rules-based calculation may be sufficient to compare usable stock with order demand. AI could help classify unstructured messages or explain a proposed response. Changing an order or committing expenditure is a separate action requiring the agreed authority.

NIST’s AI RMF treats governance, context, evaluation and risk response as continuing responsibilities. It provides a foundation for defining the intended use and evaluation boundary; it does not prescribe a specific Supply Chain solution. ^[2]

A practical shortlist for a first trial

The following are SCP selection questions. Use them with operational users and the people who own the data, systems and financial measures.

• Is the decision frequent enough to observe, with a clear owner and a meaningful consequence?
• Can the team obtain trustworthy inputs and record the existing decision process?
• Could a simpler rule, process correction or existing system capability address the problem?
• Can proposed actions be reviewed, constrained and reversed where appropriate?
• Can the trial measure errors, missed exceptions, execution cost and business outcomes?

What a bounded example might look like

Illustrative example, not a client result: assess shortage recommendations for one product family at one site. Begin in shadow mode, comparing recommendations with planner decisions without automatically changing orders. Record accepted, rejected and missed cases, including the reasons.

Agree acceptance criteria before testing: for example, held stock must remain unavailable, stale inputs must be visible, and a recommendation must not bypass spending authority. Link those criteria to tests and business-user UAT. Evidence then supports an authorised decision about the next step.

Measure the whole operating change

Useful measures include eligible-order service, false alerts, missed risks, decision time, approved actions completed and total incremental cost. Time released is not automatically a cash saving, and order value is not the same as protected profit.

The first trial should resolve uncertainty. A credible result may be to improve the data, revise the workflow or stop. A Customer Promise Protection discovery workshop can define the decision, baseline and pilot boundary before technology selection.

Explore the industries and services

• Tools and techniques →
• Customer Promise Protection worked example →
• Manufacturing priorities →