{"692875":{"#nid":"692875","#data":{"type":"news","title":"If AI Runs the Supply Chain, What Is Left to Manage?","body":[{"value":"\u003Cp\u003E\u003Cem\u003EBy \u003C\/em\u003E\u003Ca href=\u0022https:\/\/www.gatech.edu\/expert\/chris-gaffney\u0022\u003E\u003Cem\u003EChris Gaffney\u003C\/em\u003E\u003C\/a\u003E\u003Cem\u003E, Georgia Tech Supply Chain and Logistics Institute and \u003C\/em\u003E\u003Ca href=\u0022https:\/\/www.linkedin.com\/in\/zaid-duwayri\u0022\u003E\u003Cem\u003EZaid Duwayri\u003C\/em\u003E\u003C\/a\u003E\u003Cem\u003E, supply chain advisor.\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003EDuring Chris\u2019s years at Coca-Cola, one recurring question was 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.\u003C\/p\u003E\u003Cp\u003EThat delay is one reason we are 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.\u003C\/p\u003E\u003Cp\u003EIn the first conversation of our new SCL webinar series, we 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.\u003C\/p\u003E\u003Cp\u003EAs AI takes on more sensing, analysis, and routine decision-making, management does not disappear. It moves up a level, from making individual decisions to designing the objectives, boundaries, and decision systems within which those decisions are made.\u003C\/p\u003E\u003Ch2\u003EThe destination is familiar\u003C\/h2\u003E\u003Cp\u003ESet 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.\u003C\/p\u003E\u003Cp\u003EThe 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.\u003C\/p\u003E\u003Cp\u003EThis 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.\u003C\/p\u003E\u003Ch2\u003EThe time between knowing and acting is shrinking\u003C\/h2\u003E\u003Cp\u003ESupply 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.\u003C\/p\u003E\u003Cp\u003EEach 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.\u003C\/p\u003E\u003Cp\u003EIntegrated 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.\u003C\/p\u003E\u003Cp\u003EIf 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\u2019s schedule? Faster analysis makes those questions more urgent. It does not answer them on its own.\u003C\/p\u003E\u003Ch2\u003EThree systems have to move together\u003C\/h2\u003E\u003Cp\u003EThe 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.\u003C\/p\u003E\u003Cp\u003EAI 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.\u003C\/p\u003E\u003Cp\u003EThe 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.\u003C\/p\u003E\u003Ch2\u003EThree decisions show where management moves\u003C\/h2\u003E\u003Cp\u003EConsider 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\u2019s work then shifts toward setting policy with colleagues, checking assumptions, deciding when to intervene, and learning from results. \u201cException management\u201d alone is too narrow a description; AI will also get better at handling some exceptions.\u003C\/p\u003E\u003Cp\u003ENow 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.\u003C\/p\u003E\u003Cp\u003EA 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.\u003C\/p\u003E\u003Ch2\u003EManaging the decision system is real management\u003C\/h2\u003E\u003Cp\u003EThe 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\u2019s service level while weakening a customer relationship or creating a bottleneck somewhere else.\u003C\/p\u003E\u003Cp\u003EThis work still requires deep supply chain knowledge. As AI takes on more of the analysis, and eventually more of the decisions, leaders may touch fewer individual decisions, but they will carry greater responsibility for the system making them. They need to understand why a recommendation was made, when its assumptions no longer hold, and whether the system is producing the outcomes the organization intended. \u003Cem\u003EYou cannot govern a decision you do not understand.\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003EIt 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.\u003C\/p\u003E\u003Ch2\u003EThe workforce deserves a straight answer\u003C\/h2\u003E\u003Cp\u003EThe 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.\u003C\/p\u003E\u003Cp\u003EA 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.\u003C\/p\u003E\u003Cp\u003EPractitioners have a role as well. Use the tools available within your organization\u2019s 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.\u003C\/p\u003E\u003Ch2\u003EWhere the series goes next\u003C\/h2\u003E\u003Cp\u003EThis first episode focused on the system. AI may help us make better decisions sooner and act on them faster, while the supply chain\u2019s 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.\u003C\/p\u003E\u003Cp\u003EOur 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.\u003C\/p\u003E\u003Ch2\u003ERelated Webinar\u003C\/h2\u003E\u003Cp\u003EThis is a companion article to Part 1 of \u003Cem\u003EThe AI-Managed Supply Chain: What Changes, What Doesn\u0027t, and What Leaders Must Do\u003C\/em\u003E. \u003Ca href=\u0022https:\/\/www.youtube.com\/watch?v=W-emnWIdEzI\u0022\u003EView the related webinar on our YouTube channel.\u003C\/a\u003E\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EAs artificial intelligence takes on more supply chain decisions, what role remains for people? This article explores how future supply chain leaders will create strategy, set priorities, and guide AI-powered systems to deliver smarter, faster, and more responsible business outcomes.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Discover the insights that can help you adapt, build resilience, and keep your organization moving."}],"uid":"27233","created_gmt":"2026-09-29 11:50:09","changed_gmt":"2026-09-29 16:43:38","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2023-08-26T00:00:00-04:00","iso_date":"2023-08-26T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"681284":{"id":"681284","type":"image","title":"What Is Left to Manage as AI Runs More of the Supply Chain?","body":null,"created":"1790691231","gmt_created":"2026-09-29 14:13:51","changed":"1790691231","gmt_changed":"2026-09-29 14:13:51","alt":"A woman in a suit seen from behind looks out over a global logistics hub featuring warehouse automation, digital analytics displays, an airplane in flight, a cargo ship at a port, a semi-truck, and a glowing world map with connected routes.","file":{"fid":"265655","name":"Part1-The-AI-Managed-Supply-Chain_square.jpg","image_path":"\/sites\/default\/files\/2026\/09\/29\/Part1-The-AI-Managed-Supply-Chain_square.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/09\/29\/Part1-The-AI-Managed-Supply-Chain_square.jpg","mime":"image\/jpeg","size":122154,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/09\/29\/Part1-The-AI-Managed-Supply-Chain_square.jpg?itok=d7sK3YmP"}},"674087":{"id":"674087","type":"image","title":"Chris Gaffney","body":"\u003Cp\u003EChris Gaffney\u003C\/p\u003E","created":"1717067903","gmt_created":"2024-05-30 11:18:23","changed":"1771883375","gmt_changed":"2026-02-23 21:49:35","alt":"Chris Gaffney, Managing Director, Georgia Tech Supply Chain and Logistics Institute","file":{"fid":"257557","name":"chris-gaffney_scl.jpg","image_path":"\/sites\/default\/files\/2024\/05\/30\/chris-gaffney_scl.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2024\/05\/30\/chris-gaffney_scl.jpg","mime":"image\/jpeg","size":129544,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2024\/05\/30\/chris-gaffney_scl.jpg?itok=_M0fOBTF"}},"681283":{"id":"681283","type":"image","title":"Zaid Duwayri","body":null,"created":"1790688985","gmt_created":"2026-09-29 13:36:25","changed":"1790688985","gmt_changed":"2026-09-29 13:36:25","alt":"Zaid Duwayri","file":{"fid":"265654","name":"ZaidDuwayri_500px.jpg","image_path":"\/sites\/default\/files\/2026\/09\/29\/ZaidDuwayri_500px.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/09\/29\/ZaidDuwayri_500px.jpg","mime":"image\/jpeg","size":17387,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/09\/29\/ZaidDuwayri_500px.jpg?itok=El53NCq8"}}},"media_ids":["681284","674087","681283"],"related_links":[{"url":"https:\/\/www.youtube.com\/watch?v=W-emnWIdEzI","title":" [Webinar Recording] If AI Runs the Supply Chain, What Is Left to Manage?"},{"url":"https:\/\/www.scl.gatech.edu\/news-events\/newsletters","title":"View past SCL newsletters and join our mailing list"},{"url":"https:\/\/www.scl.gatech.edu\/","title":"Georgia Tech Supply Chain and Logistics Institute"}],"groups":[{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[{"id":"42911","name":"Education"},{"id":"145","name":"Engineering"}],"keywords":[{"id":"194489","name":"scl-spot"},{"id":"167074","name":"Supply Chain"}],"core_research_areas":[{"id":"39461","name":"Manufacturing, Trade, and Logistics"}],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":["info@scl.gatech.edu"],"slides":[],"orientation":[],"userdata":""}}}