A more precise plan does not guarantee a better business outcome. When teams cannot identify the signals that matter, weigh their options, and act in time, more data and analysis only add work without making the supply chain more responsive.
The 2026 Gartner® Supply Chain Planning Summit in London brought together team development, planning performance, AI, and transformation. The agenda linked inventory management and sales and operations execution (S&OE) with autonomous planning, AI literacy, and organizational change. Across these topics, the message that stood out was that planning leaders need to look beyond plan quality and take greater responsibility for how the business responds.
Measure the response, not just the planning process
A faster planning cycle matters only if it helps the business respond more effectively. Decision velocity is not just speed; it is speed with judgment. Acting too early can be as damaging as acting too late. The goal is to move at the right moment, with enough insight to choose a response and enough time to carry it out.
That changes how leaders should assess performance. Forecast accuracy still matters, but it is not enough. Teams also need to track how quickly they respond to an issue, why decisions change, and whether those actions produce the expected result. Those measures expose delays caused by repeated reviews or conflicting priorities, even when the planning process appears to run smoothly.
Let business conditions trigger action
Inventory management and S&OE need to separate issues that can wait for a scheduled review from those that require immediate action. Decision maps help by laying out the choices an organization needs to make and tying each one to a business outcome, not just another sequence of tasks.
Measurable triggers then define when those choices need attention. A threshold for margin erosion or inventory cost should spell out the economic impact, the person responsible, and the default response if no one steps in. That gives teams a consistent way to handle exceptions instead of letting the meeting calendar decide when a serious issue gets addressed.
Use AI to clarify trade-offs, not manufacture certainty
AI in planning should do more than produce a more precise estimate. A forecast may show what is likely to happen, but planners also need to understand their options and the consequences of each one. Used well, AI helps teams test trade-offs, explain assumptions, and compare alternatives so they can make better decisions.
The distinction between plan-grade and decision-grade data matters here. Plan-grade data emphasizes completeness. Decision-grade data emphasizes relevance to a specific choice. For leaders evaluating automation and new technologies, the real test is simple: does the application make the decision clearer and support action? Processing more data, on its own, does not prove value.
Develop planners who can challenge recommendations
Future-ready teams need more than confidence in AI. They need the judgment to question it. As agents take over routine analysis, planners must be able to test recommendations, validate the underlying information, and recognize when an issue needs closer review. AI literacy becomes durable only when those skills are built into day-to-day work, not treated as a one-time training exercise.
That shift also calls for a new look at roles and oversight. Reversible decisions should not face the same scrutiny as choices that are hard to unwind, and planners need clear boundaries around what they can implement themselves. When authority matches skill, people spend less time maintaining spreadsheets and more time making decisions that require judgment.
Treat adoption as a behavioral change
Agreement in a meeting does not mean a new approach will hold under pressure. Other functions may still override decisions or fall back on unofficial spreadsheets, leaving the organization with new technology but the same habits. Transformation must address behavior after implementation, not just deployment itself.
Learning loops help teams examine why assumptions proved wrong, why a decision changed, and what should happen differently next time. Capturing those lessons spreads knowledge beyond the individuals involved. Just as important, psychologically safe feedback gives people room to challenge an approach without making them less willing to act in the future. The Morrisons session made that link between AI implementation and day-to-day behavior especially clear.
How Morrisons connects AI with practical interventions
Morrisons, a major British supermarket chain and one of the U.K.'s largest fresh food manufacturers, is working with Blue Yonder to implement applications for fresh phantom inventory detection and forecast health analysis. Phantom inventory detection continuously monitors products and will autonomously intervene to recover from stockouts.
The forecast health agent helps planners investigate the causes of waste, markdowns, and poor availability. Morrisons is supporting the rollout considering the mindset, reducing firefighting, and governance, including strict guardrails and weekly AI authentication sessions. That combination gives the technology the oversight it needs to be used effectively. Blue Yonder’s broader approach connects planning and execution across functions and partners through an interoperable, AI-powered platform and unified data cloud.
Make business outcomes the executive mandate
Executives should hold planning transformation accountable for improvements in service, cost, inventory, and margin. A completed rollout or a faster planning cycle is not the end goal. Leadership must set priorities and resolve trade-offs that individual functions cannot settle on their own, so planning teams are not forced to keep escalating conflicts.
Investment in technology, talent, and process change should follow those priorities, with a clear view of the operational improvement each initiative is meant to deliver. Executives should expect proof that the new approach is being used and that it is changing results before they expand it further. The job of leadership is to move planning beyond forecast ownership and meeting management toward accountability for business outcomes, with the authority and resources to match.