Money missing. A theory that felt obvious. Nobody had actually checked it.
A company had coin-counting machines losing money across its whole network. Month after month, the losses kept coming. The company had a theory: theft. Someone on the transport crews was skimming cash before it ever got counted.
So they investigated people. Travel, hotel stays, hours of interviews, spread out over a year.
Here's what nobody checked first. "The losses are caused by theft" had never actually been verified. It had just been repeated enough times, by enough people, that it stopped sounding like a guess and started sounding like a fact. Everything that followed, a full year of investigation, was built on a sentence nobody had tested.
A new risk manager took the case. His first move wasn't a new suspect. It was a refusal. He wouldn't accept "catch the thief" as the actual problem, because that framing had already decided the answer before anyone looked. He restated it without naming a cause: money is leaving these machines, and success is the leak stopping.
That single change opened a door the old framing had kept shut. "Catch the thief" can only be solved by finding a thief. "Stop the leak" can be solved a dozen different ways, including ways that have nothing to do with a person at all.
The test that actually broke the case
Before you spend real time or money on a theory, ask what would have to be true for it to hold up, then check whether it does.
For the theft theory to explain a steady, network-wide shortfall, there would have to be a thief, or a ring of them, moving enough cash to account for the full pattern. So the risk manager tested it directly. No individual and no plausible group could account for a loss that consistent and that widespread. The assumption failed.
That's not a setback. That's the test doing its job before more money got spent chasing the wrong explanation. Once the theft theory was ruled out, the reframe from earlier paid off immediately: if it's not people, look at structure instead. The machines themselves turned out to have a mechanical fault. A year of investigating people ended the moment someone tested the one assumption everyone had already decided was true.
Why this keeps happening
Most decisions don't fail because someone reasoned badly. They fail because someone skipped the step where you check whether the starting premise is actually real, and instead built a year of good, careful work on top of an unverified guess.
The fix isn't complicated. Before you accept a problem as stated, run two questions:
- Is this actually verified, or has it just been repeated enough to sound true?
- If I state the problem without naming a cause, does a different answer become visible?
Where this gets harder, not easier
Add an AI tool into the mix and the same failure shows up in a new shape. It's tempting to treat a confident answer from a model as the rationale for a decision. "The model recommended it" sounds authoritative, in a way "it felt right" never did, which makes it more dangerous, not less. A well-written paragraph from a tool can paper over an assumption nobody actually checked, the same way "everyone knows it's theft" did for a year.
The tool can help you reason through a decision. It cannot verify your assumptions for you, and it cannot own the outcome. That part is still yours; a confident output is not the same as a tested one.
LinkedIn's 2026 Skills on the Rise report, published in February, backs this up from a different angle. They split AI skills into two distinct, separately-tracked categories this year: the technical side, things like prompt engineering and model fine-tuning, and a category they call AI Business Strategy, built around Responsible AI and judging where AI actually belongs in a business decision. LinkedIn's own stated reason that second category is growing fast: companies need leaders who can decide where AI adds real value and where it's a confident-sounding distraction, not just people who know how to run the tool. That's a judgment skill, not a technical one, and it's exactly what verifying an assumption, AI-assisted or not, actually requires.
What to check before your next big call
Name the assumption your decision is standing on. Ask what would have to be true for it to hold up. Then check, don't assume. A theory that's been repeated for a year without ever being tested is not a fact. It's just a guess that got comfortable.
That's the whole method taught inside the Decision Intelligence Lab, the four-phase approach behind "Decisions You Can Defend": accept the input carefully, apply context, test the assumptions before you stand on them, then act and document why. This case is one worked example of it. The course walks through several more, including two full ones built around a vendor decision and a hiring decision, side by side.
The TEAM Solutions Decision Intelligence Lab™ teaches this method in full. Available levels of engagement:
- Tool Only access to the Decision Assistant.
- The Full System adds the complete course and the assistant.
- All-Inclusive for unlimited use.
