Over the past few days, I went through dozens of discussions from supply chain professionals across different industries to understand where AI is actually creating value. The more I read, the clearer one pattern became.
AI is definitely being adopted in supply chains, but not in the way LinkedIn headlines or software vendors often suggest.
The biggest success stories weren't about autonomous planning or AI running an entire supply chain. They were about solving everyday problems that quietly consume hours of a planner's time.
- Drafting emails, SOPs, and reports
- Searching ERP data using natural language
- Summarizing supplier communications
- Processing orders and documentation
- Supporting warehouse operations
- Using computer vision to improve workplace safety
None of these replace planners. They simply remove friction from their day.
What surprised me even more was what wasn't working. Across industries, practitioners kept coming back to the same challenge: data quality.
If inventory records are inaccurate, supplier lead times are outdated, or master data has been neglected for years, AI doesn't solve the problem. It simply produces faster answers from flawed information.
Another observation caught my attention.
Despite all the excitement around AI, I found very few practitioners claiming that it had dramatically improved demand forecasting or inventory optimization at scale. Most successful implementations focused on something much less glamorous like reducing repetitive work, making information easier to find, and helping people make decisions faster.
That completely changed how I think about supply chain AI.
Instead of asking: “How can AI replace planners?”
I'm starting to think the better question is: “What's the most frustrating part of a planner's day that AI can eliminate?”
To me, that's where the biggest near-term opportunity lies: not replacing expertise, but amplifying it.
Sometimes, the biggest transformation doesn't come from replacing people. It comes from giving them back an hour of their day.