AI Can Spot Problems But Not Fix Them | Tech News

0:00It's September 10th. Tech news today starts now. AI powered and innovation ready. I'm Cory with the story. The supply chain world has gotten really good at spotting problems. Like when a shipment is delayed or a factory has an issue. We're talking about AI tools that can flag these disruptions, hours, even days before they used to. But here's the kicker. Knowing about a problem and actually fixing it are two different things. The real cost estimated at a massive $184 billion comes from the time it takes to go from awareness to action. This lag isn't for lack of trying. Companies have invested heavily in AI for things like predicting demand, tracking shipments, and assessing supplier risks. These systems work. They tell you when something's up. But the big money is lost in the decisions that follow. Should we pay extra for faster shipping, split in order, or find a new carrier? Companies are complex choices that still get bottlenecked by human review. It turns out a lot of supply chain teams spend nearly 30% of their time just investigating

1:02disruptions, not solving them. While executive CAI is a top priority, actually giving software the authority to make financial decisions remains rare. Most AI deployments are set up to generate alerts, which then become task waiting in a plan as already full inbox. The real game changer isn't just better detection. It's about giving AI agents the power to act within predefined limits. Think of it like this. If a contracted carrier is late beyond a certain point, and there's a cheaper, approved alternative, let the AI reroute it automatically. Or if consolidating two shipments saves money, let the AI make that call, as long as it stays within budget and policy. This requires a shift in how we think about these systems. Decisions need to be codified as policies, not just tribal knowledge. Autonomous chain systems must be able to accept machine initiated actions, and accountability needs to follow the automated decision, not just the person who eventually reviews it. This isn't about fully autonomous chains overnight, but about letting AI handle the routine,

2:08bound to decisions so humans can focus on the truly complex stuff.