{"685211":{"#nid":"685211","#data":{"type":"news","title":"If I Were Starting My Supply Chain Career Today, Here\u2019s How I\u2019d Learn GenAI","body":[{"value":"\u003Cp\u003E\u003Cem\u003EBy Chris Gaffney, Managing Director, Georgia Tech Supply Chain and Logistics Institute | Supply Chain Advisor | Former Executive at Frito-Lay, AJC International, and Coca-Cola\u003C\/em\u003E\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EIntroduction\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThis year has felt like a lifetime in the Generative AI (GenAI) world. Tools, capabilities, and best practices are shifting monthly, sometimes weekly. For supply chain professionals, the message is clear: ongoing development is not optional. Like lean, analytics, or S\u0026amp;OP in prior decades, GenAI proficiency is quickly becoming a differentiator. The question is not if you\u2019ll integrate GenAI into your workflow, but how quickly and effectively.\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EThe Evolution of GenAI in 2025\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EWhen we look back to January, it\u2019s striking how much progress has been made in less than a year. Early in 2025, the conversation centered on \u003Cstrong\u003Eagentic AI\u003C\/strong\u003E and \u003Cstrong\u003Elarger models\u003C\/strong\u003E. GPT-5 and Claude 4 improved reasoning and context windows, while OpenAI introduced ChatGPT Agent in preview, able to carry out bounded multi-step tasks like retrieving files, browsing the web, and drafting structured outputs. In supply chain, this translated into early experiments with automating shipment steps or running contract reviews in a single query \u2014 tasks that were pilot-level at best in January.\u003C\/p\u003E\u003Cp\u003EBy mid-year, \u003Cstrong\u003Emultimodal capabilities\u003C\/strong\u003E and \u003Cstrong\u003Eenterprise copilots\u003C\/strong\u003E began shifting from concept to daily use. Users could combine text, image, and voice inputs to detect defects or summarize complex documents, and copilots became embedded inside SAP, Oracle, Microsoft, and Google platforms. For the first time, GenAI wasn\u2019t just a tool \u0022off to the side\u0022 but something integrated directly into the systems supply chain professionals rely on.\u003C\/p\u003E\u003Cp\u003EIn the second half of the year, new capabilities started layering on: memory, specialized small models, and synthetic data with digital twins. Memory allowed copilots to recall context from prior chats or S\u0026amp;OP cycles, reducing rework. Domain-tuned models made GenAI lighter, cheaper, and faster for logistics, procurement, and planning tasks. And digital twin integration allowed organizations to stress-test networks under disruption scenarios, from weather to labor shortages.\u003C\/p\u003E\u003Cp\u003EEnterprises also moved closer to operations with \u003Cstrong\u003EAI at the edge\u003C\/strong\u003E, using IoT data for predictive maintenance or real-time routing. At the same time, \u003Cstrong\u003Eguardrails and compliance\u003C\/strong\u003E became a central topic, with more organizations creating clear \u0022green\/yellow\/red\u0022 tiers for safe use. And in Q4,\u003Cstrong\u003E collaboration AI\u003C\/strong\u003E and \u003Cstrong\u003Ehybrid architectures\u003C\/strong\u003E came to the forefront \u2014 copilots that can negotiate contracts in multiple languages, and architectures that blend closed and open-source models to balance sovereignty, cost, and security.\u003C\/p\u003E\u003Cp\u003EFor \u003Cstrong\u003Emainstream individual users\u003C\/strong\u003E, the picture is simpler but still powerful. Anyone with ChatGPT Plus or Copilot today can take advantage of:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003E\u003Cstrong\u003EMemory and custom instructions\u003C\/strong\u003E to save preferences and formats across sessions.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EProject-only memory\u003C\/strong\u003E (rolling out) to organize work by context.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EAgent previews\u003C\/strong\u003E like Operator to see how automation might work on bounded tasks.\u003C\/li\u003E\u003Cli\u003E\u003Cstrong\u003EConnectors and file uploads\u003C\/strong\u003E to bring internal data into conversations.\u0026nbsp;\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EFor \u003Cstrong\u003Eleaders\u003C\/strong\u003E, the focus is on policy, safe pilots, and scaling. They are:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003ESponsoring agent experiments in low-risk domains (like supplier alerts).\u003C\/li\u003E\u003Cli\u003EEmbedding copilots in enterprise systems for daily planning and reporting.\u003C\/li\u003E\u003Cli\u003EFormalizing AI use policies so employees know what\u2019s encouraged, conditional, and off-limits.\u003C\/li\u003E\u003C\/ul\u003E\u003Cp\u003EThe net result: what started in January as experimentation has, by October, become a layered landscape. Individual users now have practical tools to reclaim time, while leaders are piloting more ambitious integrations and building the governance to make adoption sustainable.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003E1. Action Planning is Critical\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThe pace of change makes a one-and-done training activity insufficient. Think of GenAI skills like fitness: it requires steady reps over time. Professionals who set quarterly development goals \u2014 experimenting with new tools, building prompt libraries, testing workflows \u2014 will not only stay current but pull ahead.\u003C\/p\u003E\u003Cdiv\u003E\u003Cp\u003E\u003Cimg src=\u0022https:\/\/www.scl.gatech.edu\/sites\/default\/files\/news\/2025-09\/figure1-QtrlyGenAI_dvlpt_cycle.jpg\u0022 alt=\u0022Quarterly GenAI Development Cycle table\u0022\u003E\u003C\/p\u003E\u003C\/div\u003E\u003Cp\u003E\ud83d\udca1 Try This Quarter:\u003C\/p\u003E\u003Cul\u003E\u003Cli\u003EBuild a custom prompt library for routine tasks (e.g., supplier follow-ups, KPI summaries).\u003C\/li\u003E\u003Cli\u003ETest one open-source tool such as LangChain or Haystack.\u003C\/li\u003E\u003Cli\u003EUse AI to summarize two recent meetings and validate output with your notes.\u0026nbsp;\u003C\/li\u003E\u003C\/ul\u003E\u003Ch3\u003E\u003Cstrong\u003E2. Prompt Maturity is the New Literacy\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EI\u2019ve personally learned the most about prompting by asking ChatGPT to critique my style against a 12-step framework. The feedback gave me a process improvement plan I still use today. Prompt maturity isn\u2019t abstract \u2014 it\u2019s a measurable, improvable skill.\u003C\/p\u003E\u003Cp\u003E\u003Cimg src=\u0022https:\/\/www.scl.gatech.edu\/sites\/default\/files\/news\/2025-09\/figure2-steps1-12.jpg\u0022 alt=\u0022Steps 7-12: Advanced Implementation\u0022\u003E\u003C\/p\u003E\u003Cp\u003E\ud83d\udca1 Applied step: Rewrite one work prompt per week by climbing the ladder.\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003E3. Unlocking Personal Productivity\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EOne of the fastest returns from GenAI comes from personal productivity. In our short courses this year, I\u2019ve seen learners gain comfort and lower stress as they practice more with the tools. Many reclaimed time by using GenAI for emails, presentations, meeting notes, and data prep.\u003C\/p\u003E\u003Cp\u003EWhile the list of GenAI time-saving strategies is broad, some uses are already mainstream and validated by thousands of professionals. The table below organizes these strategies into categories, provides guidance on how to accomplish them, and highlights common watch-outs to ensure they deliver value without risk.\u003C\/p\u003E\u003Cp\u003E\u003Cimg src=\u0022https:\/\/www.scl.gatech.edu\/sites\/default\/files\/news\/2025-09\/figure3-TimeSavingStrategies.jpg\u0022 alt=\u0022Time Saving Strategies\u0022\u003E\u003C\/p\u003E\u003Cp\u003E\ud83d\udca1 Try this week: Track one workflow where AI saved time and estimate the hours reclaimed.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003E4. Critical Thinking: Ironically More Important than Ever\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EWe wrote about critical thinking and added it to our curriculum after studies raised concerns about overreliance on AI. The smarter the tools become, the more important it is to validate their outputs.\u003C\/p\u003E\u003Cp\u003E\u003Cimg src=\u0022https:\/\/www.scl.gatech.edu\/sites\/default\/files\/news\/2025-09\/figure4-CriticalThinkingFrameworksForSCPros.jpg\u0022 alt=\u0022Critical Thinking Frameworks for Supply Chain Students and Professionals\u0022\u003E\u003C\/p\u003E\u003Cp\u003E\ud83d\udca1 Applied step: Take one AI output this week and run it through the checklist \u2014 you\u2019ll see both strengths and blind spots.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003E5. Advocating for Strategy and Guardrails\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EWe\u2019ve seen firsthand how AI policies can evolve. One major retailer shifted in less than a year from a rigid \u201conly data scientists experiment\u201d model to encouraging all employees to try safe versions of multiple LLMs. This shift shows why professionals should advocate for strategy and guardrails that evolve with the technology.\u003C\/p\u003E\u003Cp\u003E\u003Cimg src=\u0022https:\/\/www.scl.gatech.edu\/sites\/default\/files\/news\/2025-09\/figure5-FrameworkUseTiersDataSensitivity.jpg\u0022 alt=\u0022Framework: Use Tiers \u0026amp; Data Sensitivity\u0022\u003E\u003C\/p\u003E\u003Cp\u003E\ud83d\udca1 Ask your manager: Which of our daily tasks fall into green, yellow, and red today?\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003E6. Agents: Early but Essential\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EMany industry partners are actively testing agents. Our software partners are hitting singles and doubles now, with bigger \u201chome run\u201d opportunities still developing. Agents aren\u2019t fully reliable yet, but they are advancing quickly and will increasingly appear in ERP, TMS, and WMS platforms.\u0026nbsp;\u003C\/p\u003E\u003Cp\u003EIn practice, most organizations today sit between \u003Cstrong\u003ELevel 1 (Exploratory)\u003C\/strong\u003E and \u003Cstrong\u003ELevel 2 (Task-Specific Agents)\u003C\/strong\u003E, with early pilots pushing into \u003Cstrong\u003ELevel 3 (Augmented Workflows)\u003C\/strong\u003E. Tech-forward enterprises \u2014 particularly in retail, e-commerce, and global manufacturing \u2014 are building domain-specific agents for forecasting, procurement support, and transportation planning, often embedded inside ERP or planning platforms. These companies are experimenting with multi-agent coordination but keep humans firmly in the loop. By contrast, mainstream companies are still largely in the exploratory stage: individuals using general copilots for drafting documents or ad hoc analysis, without enterprise integration, security controls, or governance. The gap is widening \u2014 forward-leaning firms are developing playbooks for orchestrated workflows, while many organizations are just beginning to set policies and figure out where AI fits safely into their operations.\u003C\/p\u003E\u003Cp\u003E\u003Cimg src=\u0022https:\/\/www.scl.gatech.edu\/sites\/default\/files\/news\/2025-09\/figure6-AgentMaturityPathSupplyChain.jpg\u0022 alt=\u0022Agent Maturity Path in Supply Chain\u0022\u003E\u003C\/p\u003E\u003Cp\u003ELooking ahead, \u003Cstrong\u003ELevel 4 (Collaborative Automation)\u003C\/strong\u003E is where the near-term breakthroughs will happen. In the next 3\u20135 years, we can expect multi-agent orchestration to become a practical tool for managing recurring disruptions \u2014 think transportation rerouting during weather events or automated supplier alerts when delivery milestones are missed. Early adoption will occur in large, tech-forward enterprises with strong governance and secure infrastructure. Level 5 (Autonomous Resilience) remains aspirational: while the vision of end-to-end supply chain automation is compelling, regulatory hurdles, trust, and explainability challenges mean human oversight will remain essential. The more realistic trajectory is that enterprises will selectively automate narrow disruption scenarios while maintaining tight human control, with broader autonomy coming only as governance, standards, and trust mechanisms mature.\u003C\/p\u003E\u003Cp\u003E\ud83d\udca1 Applied step: Identify one repetitive process in your work that could be a candidate for an agent.\u0026nbsp;\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003E7. Human in the Loop: Non-Negotiable\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003ECompetition has improved model quality this year \u2014 but hallucinations and memory issues remain. That\u2019s why \u201chuman in the loop\u201d is not just a principle; it\u2019s operational reality. AI is still an assistant, not a replacement.\u003C\/p\u003E\u003Cp\u003E\ud83d\udca1 Applied step: Write down one checkpoint you always apply before sharing AI outputs.\u003C\/p\u003E\u003Ch3\u003E\u003Cstrong\u003EConclusion\u003C\/strong\u003E\u003C\/h3\u003E\u003Cp\u003EThese observations \u2014 from teaching courses, updating curriculum, and watching partners experiment \u2014 motivated this article. GenAI is evolving at extraordinary speed, and our profession must evolve with it. Build your plan, refine your prompts, reclaim time, apply critical thinking, advocate for strategy, explore agents, and always keep the human in the loop. Those who do will thrive in 2026 and beyond.\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThis year has felt like a lifetime in the Generative AI (GenAI) world. Tools, capabilities, and best practices are shifting monthly, sometimes weekly. For supply chain professionals, the message is clear: ongoing development is not optional. Like lean, analytics, or S\u0026amp;OP in prior decades, GenAI proficiency is quickly becoming a differentiator. The question is not if you\u2019ll integrate GenAI into your workflow, but how quickly and effectively.\u0026nbsp;\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Generative AI is rapidly evolving, and for supply chain professionals, adopting it quickly and effectively is becoming essential to stay competitive."}],"uid":"27233","created_gmt":"2025-09-24 13:17:49","changed_gmt":"2026-02-27 15:20:05","author":"Andy Haleblian","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2025-09-25T00:00:00-04:00","iso_date":"2025-09-25T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"679472":{"id":"679472","type":"image","title":"If I Were Starting My Supply Chain Career Today, Here\u2019s How I\u2019d Learn GenAI","body":null,"created":"1772205493","gmt_created":"2026-02-27 15:18:13","changed":"1772205579","gmt_changed":"2026-02-27 15:19:39","alt":"Futuristic illustration showing lightbulb with elements of modern supply chain inside.","file":{"fid":"263639","name":"StartingSupply-ChainCareer-Today.jpg","image_path":"\/sites\/default\/files\/2026\/02\/27\/StartingSupply-ChainCareer-Today.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2026\/02\/27\/StartingSupply-ChainCareer-Today.jpg","mime":"image\/jpeg","size":105606,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2026\/02\/27\/StartingSupply-ChainCareer-Today.jpg?itok=e5D2ReOJ"}},"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"}}},"media_ids":["679472","674087"],"related_links":[{"url":"https:\/\/www.scl.gatech.edu\/education\/professional-education\/course\/gaiascp","title":"Generative AI Application for Supply Chain Professionals course"},{"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":"186857","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":""}}}