AI supply chain planning: faster decisions need clearer accountability

PwC's "From plans to decisions" examines how supply chain planning can move from fixed review cycles toward continuous monitoring and exception handling. The opportunity is to shorten the time between detecting a disruption, evaluating alternatives and taking an appropriate action. Its most useful point is that autonomy needs boundaries. PwC recommends starting with a small number of feasible, commercially safe workflows, with clear rules for what an agent can execute and when a person must intervene. More data and faster recommendations do not help much if demand, supply, procurement and finance still disagree about who owns the decision. The article also reframes the planner's role: less time reconciling data, more time evaluating trade-offs and bringing business context to exceptions. Decision-critical data, explicit ownership and the ability to question AI outputs matter more than model sophistication alone. PwC proposes a 90-day starting path: assess actual planning maturity, improve workflows and decision rights, then build the data, process and oversight foundations for AI-assisted decisions and constrained agent pilots. Dataleo's view: continuous exception handling can complement S&OP, not replace the cross-functional choices it exists to support. APS and IBP platforms remain part of the planning infrastructure, but their value depends on how teams use them. Start with a decision that matters, define its owner and limits, and measure the outcome rather than the number of agents deployed. AI accelerates; people remain accountable for decisions.