Post by Tauseef Irfan M.Eng. MBA PMP CPIM CSCP

𝗠𝗼𝘀𝘁 𝘀𝘂𝗽𝗽𝗹𝘆 𝗰𝗵𝗮𝗶𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗳𝗮𝗶𝗹 𝗳𝗼𝗿 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝗿𝗲𝗮𝘀𝗼𝗻. They are built like technology projects. Not product solutions. After revisiting the work of Marty Cagan, I realized many supply chain AI initiatives could benefit from a product management mindset before writing a single prompt. Here is how I apply Marty Cagan’s framework when building supply chain AI agents. 𝗦𝘁𝗲𝗽 𝟭: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘁𝗵𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺, 𝗡𝗼𝘁 𝘁𝗵𝗲 𝗔𝗜 Wrong: “We need a procurement agent.” Better: “Our buyers spend 3 hours every morning reviewing late PO confirmations.” The problem comes first. The agent comes later. 𝗦𝘁𝗲𝗽 𝟮: 𝗜𝗱𝗲𝗻𝘁𝗶𝗳𝘆 𝘁𝗵𝗲 𝗨𝘀𝗲𝗿 Who is the customer? • Demand Planner • Buyer • Inventory Analyst • Production Scheduler • Supply Chain Director Each role has different pain points. One agent for everyone usually helps no one. 𝗦𝘁𝗲𝗽 𝟯: 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 Not: “Deploy an AI agent.” Instead: • Reduce MD06 review time from 4 hours to 30 minutes • Improve supplier response visibility by 80% • Reduce stockout recovery analysis time by 90% Measure business outcomes, not AI activity. 𝗦𝘁𝗲𝗽 𝟰: 𝗧𝗲𝘀𝘁 𝘁𝗵𝗲 𝗥𝗶𝘀𝗸𝘀 According to Marty Cagan, successful products remove four risks: 1. Value Risk Will planners actually use it? 2. Usability Risk Can they understand the recommendation? 3. Feasibility Risk Can we access the ERP data? 4. Business Viability Risk Will it generate measurable ROI? Many AI projects skip these questions entirely. 𝗦𝘁𝗲𝗽 𝟱: 𝗕𝘂𝗶𝗹𝗱 𝗮 𝗧𝗶𝗻𝘆 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗲 Before building a production-grade agent: • Test with 100 exceptions • Validate recommendations • Measure time savings • Collect planner feedback A 1-week prototype can save months of wasted development. 𝗦𝘁𝗲𝗽 𝟲: 𝗧𝗵𝗶𝗻𝗸 𝗣𝗿𝗼𝗱𝘂𝗰𝘁, 𝗡𝗼𝘁 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 Projects end. Products evolve. The best supply chain AI agents continuously improve through: • User feedback • New business rules • Better prompts • Better data quality • Additional workflows That is product management. That is how enterprise AI scales. My experience building supply chain AI agents has taught me that the technology is usually the easy part. The hard part is understanding the planner, buyer, scheduler, or manager well enough to solve a problem they genuinely care about. Build fewer agents. Solve bigger problems. #SupplyChain #ArtificialIntelligence #AIAgents #AgenticAI #ProductManagement #MartyCagan #SupplyChainAI #SAP #InventoryManagement #Procurement #DemandPlanning #DigitalTransformation #SupplyChainLeadership #ClaudeAI #MicrosoftCopilot #SnowflakeAI