By Chris Gaffney, Managing Director of the Georgia Tech Supply Chain and Logistics Institute and a former Vice President of Global Strategic Supply Chain at The Coca-Cola Company and Zaid Duwayri, supply chain advisor.

During my years at Coca-Cola, we used to ask how long it took for new information to make its way into a supply chain decision. At one point, the answer was measured in weeks. A lot could happen between learning that conditions had changed and doing something about it.

That delay is one reason I am interested in what AI might do for supply chains. Digital tools can shorten the time between sensing a change and deciding how to respond. Robotics and other physical technologies can shorten the time between a decision and its execution. But a faster decision is only valuable if the organization has decided what a good decision looks like.

In the first conversation of our new SCL webinar series, Zaid Duwayri and I asked a deliberately provocative question: If AI runs more of the supply chain, what is left to manage? Our answer begins with a familiar idea. The purpose of the supply chain has not changed. The way we organize people, processes, and decisions around that purpose may change considerably.

The destination is familiar

Set AI aside for a moment. What would we want from an excellent supply chain? It would sense demand and changes in the environment early, anticipate what might happen next, coordinate supply with demand, make decisions across functions, execute reliably, adapt when conditions change, and learn from the results. We have been working toward that kind of system for decades.

The measures are familiar too: service, cost, cash, growth, resilience, and responsibility. Better technology may help us improve several of these at once, but it will not make every tradeoff disappear. A machine still has finite capacity. Inventory still uses cash. A product moving from Atlanta to Dallas still takes time. Suppliers and customers remain people and organizations with their own constraints.

This is the point of starting with the supply chain rather than with an AI tool. We need to know what we are trying to improve before we choose which decisions to automate. A better forecast has limited value if a supplier cannot respond, a plant cannot add capacity, or a customer allocation rule has never been agreed upon.

The time between knowing and acting is shrinking

Supply chain technology has advanced in waves. Enterprise systems connected information. Sensors improved our ability to see what was happening. Forecasting, simulation, and optimization improved our ability to anticipate and evaluate choices. Generative AI has made some of that information and analysis easier to access. Agents are beginning to coordinate steps in a workflow, while robotics and computer vision extend automation into physical operations.

Each wave removes some friction, but adoption is uneven. I remember when RFID was going to change everything quickly. It took much longer to reach useful applications than many expected. Even today, some companies have not fully realized the value of earlier investments in data, planning, and integration. That matters when we talk about what is possible with AI and what a company can operate reliably at scale. Those are different questions.

Integrated business planning offers a practical example. For years, companies organized decisions around a monthly calendar: collect forecasts, reconcile supply and demand, meet, approve a plan, then act. Some can now pull in a new promotion or supply disruption, recalculate options, and circulate an updated plan much faster. The opportunity is to respond while the information is still useful. Whether the resulting action can happen immediately depends on capacity, partners, and clearly assigned decision rights.

If an allocation decision is needed today, who decides which customer receives the limited product? Who tells the other customer? What happens to margins, future demand, and the supplier’s schedule? Faster analysis makes those questions more urgent. It does not answer them on its own.

Three systems have to move together

The framework we used in the webinar separates the physical system, the management system, and the intelligence system. The physical system includes suppliers, plants, inventory, warehouses, transportation, and customers. The management system includes policies, planning routines, metrics, roles, and decision rights. The intelligence system includes people, data, models, and increasingly AI agents that help sense, interpret, recommend, and act.

AI is moving fastest into the intelligence system. That puts pressure on the management system. A company cannot take a much faster, more capable intelligence layer and assume that a meeting calendar, escalation path, and organizational structure designed years ago will still be the best way to use it. It may also discover that some physical design choices, such as the amount or location of inventory, deserve a fresh look.

The physical system can change, but it does not vanish. A decision that looks sound in a model still has to work in a plant, on a truck, and with partners whose systems may be less mature. This is why management design and technology adoption have to advance together.

Three decisions show where management moves

Consider a replenishment decision. Today, a planner often reviews a forecast, judges the inventory position, and approves an order. With better demand sensing and connected systems, routine decisions within defined limits may happen much faster. The planner’s work then shifts toward setting policy with colleagues, checking assumptions, deciding when to intervene, and learning from results. “Exception management” alone is too narrow a description; AI will also get better at handling some exceptions.

Now consider a supplier disruption. A system might identify the problem earlier, compare alternative sources, revise production and transportation plans, and show the effect on customers. The cheapest alternative might carry a labor practice concern, or a preferred supplier may be unable to move as quickly as the model assumes. We must decide which outcomes and obligations matter before delegating the choice. The system needs those values expressed clearly, and someone must remain accountable for the outcome.

A fulfillment center creates a different test. Sensors, computer vision, and mobile robots could help a team detect congestion, adjust work releases, and route equipment around a problem. Here, a digital decision can put a person in the path of a physical machine or push a shift past a safe workload. Autonomy should be bounded by operational and safety rules, with people able to intervene. These are scenarios to design and test, not claims that most facilities already run this way.

Managing the decision system is real management

The center of management work moves from touching every routine choice toward designing and governing how choices get made. Leaders have to establish objectives, define which decisions a system may take on its own, decide where approval is required, and monitor whether the results match the intent. They also have to look beyond the first result. A quick allocation might protect this week’s service level while weakening a customer relationship or creating a bottleneck somewhere else.

This work requires supply chain knowledge. If the people responsible for governance cannot explain why a recommendation was made, they cannot reliably improve the rules or recognize when the model has learned the wrong lesson.

You cannot govern a decision you do not understand.

As discussed in companion webinar

It also raises a development question. Many experienced supply chain leaders built judgment by making thousands of everyday decisions and living with the results. If software takes over more of those decisions, how will the next generation develop that judgment? Companies will need to make learning part of the operating model: give people access to the reasoning behind recommendations, let them test scenarios, and review outcomes with those who know the operation.

The workforce deserves a straight answer

The anxiety around AI is reasonable. At industry conferences, professionals hear what technology may do but often hear little about how their work will change. Leaders may not have a complete answer, and some jobs will change more than others. Silence leaves employees to draw their own conclusions.

A leadership team can say what it knows now. Is the current goal to help people do their jobs better, to automate particular tasks, or both? What work is under review? What training and opportunities will be available? Those answers can evolve. An honest account of the work underway is more useful than a confident promise no one can support.

Practitioners have a role as well. Use the tools available within your organization’s rules, ask for training, and bring your knowledge of the process into the design conversation. The people who understand why a decision works in the real world will be essential to building systems that work there too.

Where the series goes next

This first episode focused on the system. AI may help us make better decisions sooner and act on them faster, while the supply chain's purpose and physical limits remain. Managing that future means setting the objectives and boundaries, developing the people, and learning from what the system actually does.

Our next conversation turns to strategy. If competitors can buy access to similar AI tools, where will supply chain advantage come from? We think the answer will have much to do with design, judgment, and the ability to make these technologies work in a specific business. We would welcome the examples and questions you want us to examine.

Related Webinar

This is a companion article to Part 1 of The AI-Managed Supply Chain: What Changes, What Doesn't, and What Leaders Must Do. View the related webinar on our YouTube channel.