{"688363":{"#nid":"688363","#data":{"type":"news","title":"Putting Points on the Board with AI in Supply Chain","body":[{"value":"\u003Cp\u003E\u003Cem\u003EBy Chris Gaffney, Managing Director of the Georgia Tech Supply Chain and Logistics Institute, Supply Chain Advisor, and former executive at Frito\u2011Lay, AJC International, and Coca\u2011Cola, and Michael Barnett, Founder and Principal of Synaptic SC, former global leader of Supply Chain AI at BCG, and former executive at Aera Technology and Koch Industries.\u003C\/em\u003E\u003C\/p\u003E\u003Cp\u003EEntering 2026, one thing is clear: staying on the sidelines is no longer a viable option. We both agree that 2025 was the last year when being \u201cbehind\u201d on AI adoption could be rationalized. In 2026, leaders cannot stay in the foxhole. They need to move forward, doing so in a way that reduces the risk of failure.\u003C\/p\u003E\u003Cp\u003EThe past two years have been full of promise for AI in supply chain: we have seen impressive pilots, compelling research findings, and no shortage of claims about what agents and large language models can do. At the same time, many supply chain leaders are frustrated; there has been significant activity and investment in centralized capabilities without meaningful results in the supply chain. Too many efforts stall. Too many pilots never scale. Many organizations feel they have kissed a lot of frogs and are still waiting for something that works reliably.\u003C\/p\u003E\u003Cp\u003EThe question for 2026 is no longer whether to engage with AI, but how to do so in a way that consistently delivers results. This is the year to put points on the board through disciplined, repeatable progress rather than moonshots.\u003C\/p\u003E\u003Ch2\u003ETwo Principles Separate Progress from Experimentation\u003C\/h2\u003E\u003Cp\u003EAcross our work and conversations with supply chain leaders, organizations that are driving tangible results tend to follow two principles, sometimes explicitly, sometimes intuitively:\u003C\/p\u003E\u003Ch3\u003E1. Leverage GenAI Where It Adds Differential Value\u003C\/h3\u003E\u003Cp\u003ELarge language models are exceptionally strong at working with language. They summarize, explain, code, and translate intent into logic. This makes them powerful tools for accelerating development, analysis, and communication.\u003C\/p\u003E\u003Cp\u003EMuch of supply chain execution, however, depends on precision. Planning rates, forecasts, production schedules, routing logic, and inventory policies rely on structured data, mathematical relationships, and deterministic logic. In these environments, hallucinations or probabilistic answers are not just inconvenient. They can be operationally disruptive.\u003C\/p\u003E\u003Cp\u003EMany early failures stem from applying LLMs where deterministic logic is required, rather than using them to support the creation, maintenance, and monitoring of that logic. In practice, GenAI is most effective upstream, helping teams build analytics faster, surface issues earlier, and lower the friction of development and maintenance.\u003C\/p\u003E\u003Ch3\u003E2. Design with People in the Loop\u003C\/h3\u003E\u003Cp\u003EThis is not only a philosophical stance. It reflects technical reality. While \u003Ca href=\u0022https:\/\/research.gatech.edu\/age-autonomous-supply-chains-here\u0022\u003Erecent research\u003C\/a\u003E shows that collections of agents can outperform humans in controlled settings, production supply chains are not laboratories. They are complex, interconnected processes and organizations that operate in a dynamic, ever-changing environment. In contrast to AI that augments workers, fully autonomous systems introduce risks\u2014technical, organizational, and reputational\u2014that erode the incremental value relative to the increased costs to develop and maintain them.\u003C\/p\u003E\u003Cp\u003EHuman-in-the-loop is not a concession. It is a design principle.\u003C\/p\u003E\u003Ch2\u003EFrom Ideation to Error-Proofed Execution\u003C\/h2\u003E\u003Cp\u003EMost supply chain organizations are not short on AI use cases. What they lack are clear, high\u2011probability paths to value creation.\u003C\/p\u003E\u003Cp\u003EA familiar pattern plays out: organizations rush into pilots without a clear view of where AI adds value. Results are mixed and hard to interpret. When early efforts disappoint, leaders become more cautious, not because they doubt AI\u2019s potential, but because they are wary of repeating visible failures.\u003C\/p\u003E\u003Cp\u003EOne executive described this dynamic as being \u0022tired of kissing frogs.\u0022 After aggressively leaning into new technologies early, the organization became skeptical, insisting on external proof and peer validation before investing further.\u003C\/p\u003E\u003Cp\u003EThe more productive question is no longer \u0022What is the most advanced thing we can try?\u0022 but instead: \u0022What can we do today that has a high probability of working, scaling, and building our capabilities?\u0022\u003C\/p\u003E\u003Ch2\u003EHow to Put Points on the Board in 2026\u003C\/h2\u003E\u003Cp\u003EAcross our experimentation and advisory work, two areas consistently emerge where GenAI is already delivering value.\u003C\/p\u003E\u003Ch3\u003EEnterprise Productivity: The Safest On-Ramp\u003C\/h3\u003E\u003Cp\u003EThe most reliable progress comes from improving everyday productivity.\u003C\/p\u003E\u003Cp\u003EMost organizations take a restrictive approach, limiting AI access to a small group or tightly controlled pilots led by centralized technical teams, only to realize they were slowing learning and adoption across the enterprise. In one large retailer, leadership initially centralized AI use due to security and governance concerns. Over time, they shifted to enterprise licensing that centralized risk management while allowing broader employee access within guardrails.\u003C\/p\u003E\u003Cp\u003EThe result was not chaos or \u0022shadow IT.\u0022 It was productivity: meeting summaries, analysis support, presentation development, and faster access to internal knowledge.\u003C\/p\u003E\u003Cp\u003EThese gains may sound modest, but they matter. Giving people five to ten hours per week back changes how employees experience AI. It becomes a tool that helps them do their jobs better, not a signal that their jobs are being automated away.\u003C\/p\u003E\u003Cp\u003EFor leaders, this means actively enabling access to approved tools, supporting skill development, and encouraging experimentation within clear boundaries. This is one of the most straightforward ways to quickly and visibly put points on the board.\u003C\/p\u003E\u003Ch3\u003EDecision Intelligence: Rewiring the Operating Model\u003C\/h3\u003E\u003Cp\u003EAdvanced analytics, optimization, and planning systems predate GenAI. What is new is not the math, but rather the speed, accessibility, and maintainability of building and sustaining advanced analytics solutions.\u003C\/p\u003E\u003Cp\u003EGenAI acts as an accelerator. It reduces the friction of writing code, standing up, monitoring logic, and explaining results. It brings advanced capabilities closer to the business, rather than confining them to a small central team.\u003C\/p\u003E\u003Cp\u003EA concrete example comes from production planning. Planned production rates are often set during commissioning or early ramp up and then reused for long periods. Over time, changes in labor mix, maintenance practices, or product complexity cause actual throughput to drift. Plans continue to run, but they quietly degrade.\u003C\/p\u003E\u003Cp\u003EIn effective implementations, GenAI does not update the planning system autonomously. Instead, it operates adjacent to it. It helps teams build monitoring logic that compares planned versus actual performance, surfaces statistically meaningful drift, and generates candidate adjustments with supporting context. Planners review and approve changes before they are re-ingested into the APS.\u003C\/p\u003E\u003Cp\u003EThe system of record remains intact. Human accountability is preserved. What improves is the speed, frequency, and quality of assumption hygiene, enabling earlier detection of problems before they cascade into service, cost, or inventory issues.\u003C\/p\u003E\u003Ch2\u003EAvoid Kissing Frogs: Technology and Organizational Choices\u003C\/h2\u003E\u003Cp\u003EMany organizations \u201ckiss frogs\u201d not because the new technology is flawed, but because they are not ready to adopt it.\u003C\/p\u003E\u003Cp\u003ETo avoid this fate, successful efforts often include the following elements:\u003C\/p\u003E\u003Col\u003E\u003Cli\u003E\u003Cstrong\u003ELeverage existing, approved AI platforms rather than onboarding new technologies\u003C\/strong\u003E\u003Cul\u003E\u003Cli\u003EAccelerates time to value\u003C\/li\u003E\u003Cli\u003EHelps define the true limitations of your current technology stack to guide future platform selection\u003C\/li\u003E\u003C\/ul\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EMaximize the value of current systems (e.g., APS, production scheduling software) instead of chasing new applications\u003C\/strong\u003E\u003Cul\u003E\u003Cli\u003EExisting, complex supply chain software often under-delivers on its promised value\u003C\/li\u003E\u003Cli\u003EAI agents and workflows are highly effective at improving master data quality and ensuring planning parameters are accurate\u003C\/li\u003E\u003C\/ul\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EFoster ideation and solution development with internal teams, while using third parties to accelerate capability building\u2014not to replace it\u003C\/strong\u003E\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EMake progress visible by sharing early wins, curating employee-driven experiments, and scaling what works\u003C\/strong\u003E\u003C\/li\u003E\u003C\/ol\u003E\u003Cp\u003EChange management is not an option; it must be designed into every aspect of an AI program from the start. When organizations invest heavily in advanced capabilities at the top while doing little to equip everyday employees, the message received is often, \u0022This is happening to you, not for you.\u0022 That perception creates resistance, fear, and organizational drag.\u003C\/p\u003E\u003Cp\u003EEffective leaders communicate a clear vision for how new capabilities will augment, not replace, their teams, so that scarce human intellect is applied where it adds the most value.\u003C\/p\u003E\u003Ch2\u003EKey Actions to Win in 2026\u003C\/h2\u003E\u003Cp\u003EThe principles are clear. The opportunity is real. The question now is execution.\u003C\/p\u003E\u003Cp\u003EIf 2026 is the year to put points on the board, supply chain leaders must move from experimentation to engineered progress. That begins with clarity.\u003C\/p\u003E\u003Ch3\u003E1. Define a Multi-Year AI Value Vision\u003C\/h3\u003E\u003Cp\u003EDevelop a concrete view of how AI will create value in your organization over the next several years. Not a collection of pilots. Not a list of tools. A clear articulation of where and how AI will improve productivity, strengthen decision quality, and increase operational reliability.\u003C\/p\u003E\u003Cp\u003EThat vision should:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EClarify where AI will augment human decision-making versus automate tasks\u003C\/li\u003E\u003Cli\u003EIdentify the business outcomes you expect to improve (service, cost, inventory, resilience, productivity)\u003C\/li\u003E\u003Cli\u003EGuide decisions on organizational design, platform selection, governance, and partnerships\u003C\/li\u003E\u003Cli\u003EEstablish sequencing - what you will enable now versus what must wait\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EWithout a defined direction, AI efforts default to software deployment. With it, technology becomes a lever for measurable operational improvement.\u003C\/p\u003E\u003Ch3\u003E2. Enable Broad, Responsible Access\u003C\/h3\u003E\u003Cp\u003ECapability development accelerates when access is not unnecessarily constrained. Ensure that team members at every level - from executives to frontline planners - have access to approved enterprise AI tools and agent-building capabilities, along with practical training tied to real workflows.\u003C\/p\u003E\u003Cp\u003EEffective enablement includes:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EEnterprise licensing and governance that remove friction while protecting data\u003C\/li\u003E\u003Cli\u003EHands-on guidance tied directly to day-to-day supply chain work - reporting, master data cleanup, production monitoring, inventory analysis, schedule validation\u003C\/li\u003E\u003Cli\u003EClear operating guardrails that define appropriate data use and boundaries\u003C\/li\u003E\u003Cli\u003ELeadership support for responsible experimentation\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003ERestricting access may feel prudent. In practice, it slows learning and reinforces dependency on centralized teams. Broad enablement builds capability across the organization.\u003C\/p\u003E\u003Ch3\u003E3. Create Local Ideation and Scaling Mechanisms\u003C\/h3\u003E\u003Cp\u003EDurable progress does not originate only from centralized programs. It often begins at the front line.\u003Cbr\u003ELeaders should create simple, visible mechanisms for individuals and teams to experiment within defined guardrails and to share what they are building.\u003C\/p\u003E\u003Cp\u003EThis includes:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003ERecurring forums or showcases where teams present working solutions\u003C\/li\u003E\u003Cli\u003ECurated libraries of effective prompts, workflows, and agents\u003C\/li\u003E\u003Cli\u003EClear channels for submitting ideas and documenting results\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EMost importantly, organizations must be able to move from local experimentation to scaled adoption. That requires:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EIdentifying the strongest minimum viable solutions emerging from the field\u003C\/li\u003E\u003Cli\u003ERefining and hardening them into repeatable workflows\u003C\/li\u003E\u003Cli\u003EProductizing and scaling what demonstrably improves performance\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EThe objective is not activity. It is building capability that compounds over time.\u003C\/p\u003E\u003Cp\u003EThese steps are straightforward. They require intention and follow-through. That is what separates durable capability from scattered experimentation.\u003C\/p\u003E\u003Cp\u003EIt is not too late to lead. The last several years have provided lessons - technical, organizational, and cultural. Leaders who absorb those lessons and design deliberately for scale will build AI capabilities that strengthen over time.\u003C\/p\u003E\u003Cp\u003EThat kind of progress is not flashy. It does not depend on moonshots or fully autonomous systems operating in isolation. It depends on clarity, access, discipline, and accountability.\u003C\/p\u003E\u003Cp\u003EIn 2026, novelty will attract attention. Durability will create an advantage.\u003C\/p\u003E\u003Cp\u003EThe organizations that win will not be the ones with the most pilots. They will be the ones who consistently translate AI into measurable operational improvement.\u003C\/p\u003E\u003Cp\u003EThis is the year to move from experimentation to engineered results.\u003C\/p\u003E\u003Ch2\u003E\u003Cstrong\u003EPut points on the board.\u003C\/strong\u003E\u003C\/h2\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EIn 2026, supply chain leaders must move beyond experimentation with AI to achieve consistent, measurable results by focusing on practical, scalable applications that augment human decision-making and improve productivity.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Practical guidance to drive real progress in 2026."}],"uid":"27233","created_gmt":"2026-02-18 17:20:05","changed_gmt":"2026-02-24 00:01:16","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2026-02-24T00:00:00-05:00","iso_date":"2026-02-24T00:00:00-05:00","tz":"America\/New_York"},"extras":[],"hg_media":{"679399":{"id":"679399","type":"image","title":"AI-Driven Decision Intelligence  Across the Supply Chain","body":null,"created":"1771877803","gmt_created":"2026-02-23 20:16:43","changed":"1772457797","gmt_changed":"2026-03-02 13:23:17","alt":"Illustration of AI-driven supply chain decision intelligence, featuring analytics dashboards and AI\u2011powered insights supporting materials management, production scheduling, inventory management, transportation, and demand planning.","file":{"fid":"263562","name":"bnr-CM-AI-DrivenDecisionIntelligence_1024x1024.jpg","image_path":"\/sites\/default\/files\/2026\/02\/23\/bnr-CM-AI-DrivenDecisionIntelligence_1024x1024.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/23\/bnr-CM-AI-DrivenDecisionIntelligence_1024x1024.jpg","mime":"image\/jpeg","size":163591,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/23\/bnr-CM-AI-DrivenDecisionIntelligence_1024x1024.jpg?itok=Cmb-KmGb"}},"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"}},"679403":{"id":"679403","type":"image","title":"Michael Barnett","body":null,"created":"1771883408","gmt_created":"2026-02-23 21:50:08","changed":"1771883408","gmt_changed":"2026-02-23 21:50:08","alt":"Michael Barnett","file":{"fid":"263563","name":"Barnett-Michael-2022.jpg","image_path":"\/sites\/default\/files\/2026\/02\/23\/Barnett-Michael-2022.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/23\/Barnett-Michael-2022.jpg","mime":"image\/jpeg","size":21649,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/23\/Barnett-Michael-2022.jpg?itok=RzHA96wO"}}},"media_ids":["679399","674087","679403"],"related_links":[{"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":"1250","name":"Center for Health and Humanitarian Systems (CHHS)"},{"id":"1242","name":"School of Industrial and Systems Engineering (ISYE)"},{"id":"1243","name":"The Supply Chain and Logistics Institute (SCL)"}],"categories":[{"id":"194606","name":"Artificial Intelligence"},{"id":"42911","name":"Education"},{"id":"145","name":"Engineering"}],"keywords":[{"id":"2556","name":"artificial intelligence"},{"id":"194489","name":"scl-spot"},{"id":"167074","name":"Supply Chain"},{"id":"187190","name":"-go-gtmi"}],"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":""}}}