10 TIPS
Giving AI a complete, real-world picture of operations—the actual decisions, constraints, exceptions, and workarounds that define how work gets done—is what enables it to identify risks and drive resilient supply chain outcomes. Mapping AI to Resilience
1 BUILD RESILIENCE ON REAL VISIBILITY INTO PROCESS, MATERIAL & DECISION FLOWS
8 KEEP BUSINESS USERS IN THE LOOP AI that surfaces an issue but doesn’t trigger a clear next action has limited value. Systems need to guide decisions in real time, before disruptions escalate, and deliver those prompts in language that frontline and functional teams can act on immediately. Ensuring AI outputs are meaningful at the point of decision and meet business users where they are, not just visible in a dashboard, drives execution. 9 OPTIMIZE ACROSS END- TO-END PROCESSES Resilience doesn’t break in one silo—it often breaks in the handoffs between them. AI must connect decisions across order management, warehousing, and logistics to prevent breakdowns at the seams. An end-to-end view of operations, linking decisions across functions, prevents fragmentation from becoming a systemic vulnerability.
Supply chains run on thousands of daily processes, but there’s often a gap between how these workflows are designed and how they actually play out. Organizations can’t use AI to respond to disruptions if they don’t understand how operations truly happen on the ground. When AI has the correct operational context it needs, it can seamlessly identify, alert, and provide solutions to potential disruptions before they occur.
2 GET A VIEW OF HIDDEN WORKFLOWS Capture and connect the full decision flow—across emails, spreadsheets, and documents—and bring it into a shared, system-level view. Without that visibility, automation remains limited and key decisions stay locked in silos. 3 TREAT AI AS A DECISION ENGINE AI should help guide decisions. Its value comes from understanding how work actually gets done and using that operational context to recommend next steps. That requires grounding AI in the data that reflects real decision making, including the constraints and judgement calls that never make it into a formal system. 4 FEED AI REAL CONSTRAINTS AI decisions are only as good as the inputs behind them. Capacity limits, pricing rules, and timing constraints define what’s possible. Without this context, AI recommendations
real users and measurable outcomes. Ground pilots in real processes where failures already occur. This approach reflects real-world variability, and provides the best foundation for scaling AI across the organization. 7 CLOSE THE PLAN-TO-REALITY GAP Supply chain plans rarely match execution. Track where operations deviate: lane changes, cost overruns, and delays. Understanding these variances—not just flagging them—allows AI to recommend meaningful corrective action. AI can only improve resilience when it is continuously learning from the difference between what was planned and what actually happened.
may be technically coherent but operationally unworkable. Connecting AI to the real constraints that govern daily execution allows it to make reliable decisions under real-world conditions. 5 PRIORITIZE HIGH-IMPACT PROCESS FAILURES Expedited orders or missed shipments expose where resilience fails and where AI can drive the greatest operational and financial impact. Focusing early efforts here builds credibility with operational teams and accelerates the path to measurable results. 6 DON’T LET PILOTS BECOME EXPERIMENTS Tie every pilot to a real business problem with
10 DESIGN FOR SCALE FROM DAY ONE
To support resilience at scale, organizations need a foundation that can expand across business units, geographies, and systems, without rebuilding from scratch each time. That means establishing common data standards, governance practices, and integration patterns early, so momentum from early wins can translate into enterprise-wide capability.
SOURCE: CJ SCHETTLER, SUPPLY CHAIN GTM, NORTH AMERICA, CELONIS
12 Inbound Logistics • July 2026
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