The Unveiling of the Oatmeal

Brent was leaning into his microphone, his voice dripping with the kind of practiced enthusiasm usually reserved for people selling timeshares or miraculous juice cleanses. ‘And here,’ he said, gesturing with a virtual laser pointer at a neon-blue cluster on the screen, ‘our proprietary AI neural network autonomously identifies supply chain disruptions before they manifest in your P&L.’ I squinted at the screen. I’ve spent 16 years as a supply chain analyst, and my eyes have developed a sort of Darwinian defense mechanism against marketing decks. I looked at the data points-there were exactly 46 of them blinking in that specific window-and something felt off. It looked suspiciously like a standard deviation filter I’d seen in a 26-year-old textbook. I reached for my coffee, and that’s when the absolute horror struck. My camera was on.

I hadn’t intended for it to be. I was sitting in my home office, hair looking like a bird’s nest after 6 hours of wrestling with late shipments from the Pacific, wearing a t-shirt with a suspicious coffee stain. I saw my own panicked reflection in the small Zoom window, frozen in the act of blowing on a hot spoonful of oatmeal. I didn’t move. Maybe if I stayed perfectly still, Brent and the 6 other people on the call would think it was just a high-resolution, slightly unflattering still image. But the steam from the oatmeal was moving. The jig was up. I slowly put the spoon down, cleared my throat, and decided that if they had to see my morning face, they were going to have to hear my actual thoughts.

Brent’s smile didn’t drop, but it vibrated. It was the vibration of a man whose script had just run into a wall. ‘It’s a… a complex rules-based engine that leverages… synergies,’ he stammered. He tried to pivot back to the slide about ‘predictive harmony,’ but the damage was done. He had just admitted that his ‘AI’ was a glorified checklist. A series of if-then statements dressed up in a tuxedo and sold for a 96% markup over standard SaaS pricing.

The Linguistic Parasite

This is the Great AI Delusion of the current era. We are living through a period where ‘Artificial Intelligence’ has become a linguistic parasite, attaching itself to any piece of software that can perform basic arithmetic. As a supply chain analyst, I see this daily. Companies are desperate for a magic wand. They want to believe that there is a digital deity that can foresee a port strike in 66 days or predict the exact moment a truck driver in Nebraska is going to decide to quit. So, they buy the ‘AI-powered’ platform. They spend 1006 hours on implementation, only to find out that the ‘Intelligence’ is actually just me, Claire A.-M., or someone like me, manually override-ing the system because it can’t handle a simple contradiction in the shipping manifest.

It’s dishonest, and it’s dangerous. When we hide the logic behind a black box labeled ‘AI,’ we lose the ability to audit the decision-making process. If a rule says ‘If shipment > 10 days late, cancel order,’ I can argue with that rule. I can refine it. But if the ‘AI’ says ‘The neural weights have determined this shipment is a 76% risk,’ what do I do with that? I can’t argue with a weight. I can’t fix a hallucination if I don’t know the parameters that created it.

[The silhouette of a machine is not the machine itself.]

The Freak Ice Storm Incident

I remember a specific instance about 56 days ago. We were testing a new ‘intelligent’ routing tool. It told us to move all our inventory from the West Coast to a warehouse in the Midwest because it predicted a ‘low-risk weather window.’ Two days later, a freak ice storm-the kind that happens once every 46 years-paralyzed the entire region. The ‘AI’ hadn’t seen it coming because it wasn’t actually looking at weather patterns. It was just looking at historical cost averages for that specific week over the last 6 years. It was a fancy if-then statement: ‘If cost < X, move inventory.' It didn't have intelligence; it had a calculator and a very narrow memory. We ended up losing $6,676 in spoilage alone, not to mention the 16 missed deliveries that left our best clients fuming.

There is a profound psychological comfort in the word ‘intelligence.’ It implies a level of care, a level of oversight that human beings are currently too tired or too understaffed to provide. We are outsourcing our responsibility to algorithms because we are overwhelmed by the 5,026 data points we have to track every single hour. But the irony is that these ‘fake AI’ systems actually create more work. I spend 76% of my week auditing the ‘insights’ generated by our platform to make sure it hasn’t suggested something physically impossible, like shipping 26 tons of steel via a drone.

Seeing the Formula

When a vendor says ‘AI,’ we should ask to see the math. We should ask about the training data. I’m tired of the fairy dust. I want software that acknowledges its limitations. I want a platform that says, ‘I found this pattern, and here is exactly why I think it matters.’

I think back to Brent’s face on the screen. After his ‘synergies’ comment, there was this long, awkward silence. I could see the other participants on the call-6 little boxes of people staring at their own desks, probably also embarrassed for him, or maybe they were just checking their own cameras to make sure they weren’t also accidentally broadcasting their breakfast. Brent tried to recover by showing a slide of a futuristic-looking brain made of glowing lines. ‘But the scalability,’ he whispered. I almost felt sorry for him. He was a victim of the hype cycle too. He’d been told he was selling the future, but he was just selling a very expensive version of what we already had in Excel 26 years ago.

The Real Job of Logistics

Actually, that’s not entirely fair to Excel. At least in Excel, I can see the formula. I can press F2 and see exactly which cells are being referenced. These ‘AI’ platforms are often just Excel spreadsheets with the ‘View Formula’ bar hidden and a $56,000 annual subscription fee. It’s a shell game. We’ve reached a point where the term ‘AI’ is used to justify lack of transparency. If the output is wrong, it’s not a bug; it’s just the ‘model’ learning. If the system crashes, it’s ‘re-calibrating.’

I’m a supply chain analyst, not a philosopher, but I know when I’m being sold a lemon. My job is to ensure that 166 containers get from Point A to Point B without disappearing into the ether. I don’t need a neural network to tell me that a blizzard in the Rockies is going to delay a truck. I need a system that gives me the data to make that call myself, or an automation that is honest about the rules it’s following. We have to stop being afraid to ask the ‘dumb’ questions. Because usually, when you ask the dumb question, you realize the ‘smart’ AI is actually the one that’s lacking.

The Final Equation

As the meeting ended, I didn’t even wait for the ‘Leave Meeting’ button to appear. I just shut my laptop lid. I sat there for a while, finishing my cold oatmeal. I thought about the 46 data points, the ‘synergies,’ and the fact that somewhere out there, another analyst was currently being told that a basic linear regression was a ‘deep learning breakthrough.’ We don’t need more ‘AI.’ We need more honesty. We need tools that work, systems that provide clarity, and maybe, just maybe, a reminder to check if the camera is off before we start our morning breakfast.

The reality of the supply chain isn’t a glowing digital brain; it’s a dirty, loud, complex web of 1,006 moving parts that all have to align perfectly. If we keep pretending that magic software is going to fix it, we’re just going to keep paying for if-then statements while the real problems remain unsolved. I’d rather have a transparent ‘If’ than a lying ‘AI’ any day of the week. At least with an ‘If,’ I know where I stand. I know where the logic breaks. And in this business, knowing where things break is 96% of the battle.