|
||||
|
||||
Logic — the layer everyone thinks the work is aboutAsk a team what decision architecture means and most will describe Logic. The criteria. The weights. The if-this-then-that rules. The reasoning that takes the framed inputs and produces the call. They are not wrong that Logic matters. They are wrong that this is the layer the work starts at, which is why so many AI projects begin with someone whiteboarding rules and end with a system nobody trusts. The work starts at the Audit. By the time you reach Logic, you have already picked a decision worth architecting, mapped its trigger, and made its frame explicit. Logic is where you encode how the framed inputs combine to produce a specific call. This is the layer that finally looks like what people imagine when they think about AI. It is also the layer most teams have less of written down than they assume. Here is a quick test. Pick one of your priority decisions. Write down the logic that produces the right answer, as if you were teaching a new hire who would never get a chance to ask follow-up questions. Most operators get about two paragraphs in before they hit a sentence that starts with "it depends" — and that is the honest moment. The "it depends" is where the logic lives, and where the encoding stops being automatic and starts being a real piece of work. Logic has three parts. Criteria, weights, and resolution. Most teams have the criteria. Half have the weights. Almost nobody has the resolution. |
||||
|
||||
|
||||
|
||||
|
Criteria, weights, resolution. Write them down for one decision and the work that was looking like an AI project becomes something simpler. It becomes an inspectable system that can be audited, improved, and corrected without a black box anywhere in the middle. The encoding is not done — there are two more layers to come — but the hardest intellectual work is. The system can now make the call. The remaining question is what it does with the call. One more honest moment. When you write the logic down for the first time, you will frequently discover that your team's stated criteria do not match the actual outcomes. People say they use one set of weights and the case history shows another. This is not deception. It is the gap between explicit and implicit logic, and it is exactly what decision architecture is for. The version you write down is not always the version you have been running. The point is to make the version you write down become the version you run, deliberately, and inspectably. Most AI projects skip this work because it is uncomfortable. The discomfort is the point. There is no version of this where you get the benefit without doing the writing. |
||||
|
||||
|
Next Week · Part 5 of 6 Layer 05 — MoveWith the logic encoded, the system can make the call. But making the call and acting on the call are not the same thing. Move is the layer that turns a decision into a committed action — and the layer where most teams' AI deployments quietly stop being useful. |
||||
|
Writing the logic down for one of your own decisions and finding that the version you write does not match the version you have been running? That is the most important conversation a Discovery call can have. Bring the gap. We will work through how to close it deliberately.
Or read this issue on LinkedIn. |
||||
|
