Better AI Value Starts With Better Decision-Making
Editor’s note: This is the second article in a series about AI and supply chains. The first article, “AI Doesn’t Run the Supply Chain, Decisions Do,” can be read here.
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Every supply chain outcome can be traced back through a series of decisions.
Inventory does not become excessive on its own. Service does not deteriorate independently of planning, sourcing, production or fulfillment choices. Transportation costs are shaped by decisions about inventory, capacity, mode, routing, timing and customer commitments — often made well before a shipment moves.
Some decisions are routine and repeated thousands of times; others occur infrequently but carry significant operational or financial consequences. What matters is not simply how quickly those decisions are made, but whether the organization is consistently making the right decisions with the information available.
A Decision-First Approach
This is where the value of AI becomes more specific. Rather than asking what a technology can automate or which use cases an organization can deploy, a decision-first approach examines (1) where decisions materially affect performance and (2) what prevents those decisions from being better today.
The constraint may be incomplete information, timing, complexity, inconsistent business rules, competing priorities or the number of alternatives a person can reasonably evaluate. AI may address some of those constraints. In other cases, the problem has little to do with technology.
Supply chains are particularly dependent on this distinction because their decisions are interconnected. Demand planners determine expected requirements, sourcing teams balance supplier capabilities against cost and risk, manufacturing leaders allocate production capacity, inventory teams determine where and how much product to hold, and logistics teams make decisions about movement, storage and fulfillment.
Each decision creates conditions for the next. A decision that appears optimal within one function can create cost, delay, excess inventory or service problems somewhere else:
Demand forecasting is an example. Improving forecast accuracy can provide planners with better information, but the forecast itself does not create the operational result. Someone or something must decide whether the new information warrants a change to production, inventory, purchasing or capacity. The value of the improved forecast therefore depends not only on its accuracy, but on whether the organization can translate that information into a better decision at the right time.
Supplier risk works the same way. AI may identify changes in supplier performance, financial conditions, lead times, geopolitical exposure, or other risk indicators earlier than existing processes.
That additional warning time matters only if the organization has defined how the information should affect sourcing decisions. A risk identified earlier but acted on in the standard response time has provided visibility without materially improving the response.
Transportation is another instance. A system can evaluate routes, modes, carriers, capacity, cost, service requirements and disruption signals to recommend a different transportation plan.
Whether that recommendation improves performance depends on the operating environment around it. Inventory availability may have changed. Customer commitment may take priority over transportation cost. Production may be delayed. Capacity may no longer be available. The recommendation is one input into a decision that exists within a larger supply chain.
These examples point to a larger issue. Organizations using comparable technologies can experience very different results because the technology operates within different decision environments.
Business rules may be inconsistent, decision ownership may be unclear, functions may optimize against competing objectives, or information may arrive too late to influence the outcome. In those conditions, adding AI can improve individual activity without improving end-to-end performance.
Performance-Based Decisions
A decision-first approach changes the sequence. Rather than starting with an inventory of technologies or potential AI use cases, organizations begin with the decisions that materially affect performance.
They examine where those decisions occur, who or what makes them, what information is required, which constraints matter, how frequently the decisions are made, what happens when they are delayed or wrong, and how one decision affects another. Only then does the question become whether AI, automation, optimization, analytics or another capability can improve the decision.
Measurement should follow the same logic. Model accuracy, adoption, processing time and productivity can be useful measures, but they do not establish whether a better business outcome occurred. If AI supports an inventory decision, the organization should be able to connect that decision to such measures as service, working capital, stockouts, obsolescence or expedite costs.
If it supports transportation decisions, the relevant outcomes may include cost, service reliability, utilization, dwell or exceptions. The measures will vary, but the principle remains the same: the technology should be evaluated in relation to the decision and the resulting business performance.
Being Deliberate
This does not mean every decision should remain human or that AI should only provide recommendations. Some decisions are well suited to automation, particularly when objectives, constraints, authority and exception thresholds are clearly defined.
Other decisions require judgment because they involve competing priorities, incomplete information, unusual conditions or consequences that extend beyond a single function.
As AI systems take on more responsibility for initiating and executing actions, understanding those boundaries becomes even more important. In a recent Inside Supply Management® article, “Redesigning Supply Chains for Autonomous AI Agents,” author Lu Zhang, CPSM, discussed how autonomous AI makes the same distinction between systems that recommend actions and those authorized to execute them.
The opportunity, then, is not simply to put AI into more supply chain processes. It is to become more deliberate about the decisions those processes exist to support. AI can provide better information, evaluate more alternatives, recognize patterns, accelerate analysis and execute defined decisions.
But none of those capabilities answers the first question an organization should ask: Which decisions matter most to our performance, and what would making them better change? That is where AI investment should begin.
(Image credit: Getty Images/Eva Almqvist)