Frameworks

Make attention reliable.

Most bad decisions are not failures of intelligence. They are failures of attention. The question no one thought to ask. The default no one chose. The decision that got made by absence. A framework is just a way to make attention reliable, to put the important question in front of you before the moment has passed rather than after the cost has landed.

What follows is not theory. It is the thinking I use, stripped to what you can apply on your own. The deeper, tailored versions of this work live in what I do with organizations, but you do not need those to get most of the value. Take these, adapt them, make them yours.

Organizational

The Decision Principles

Everything else rests on five reframes. They are not controls or procedures; they are the way of seeing that makes controls obvious later. Get them wrong and every tool choice, policy, and review inherits the error. Get them right and most of what looks like an AI problem turns out to be a decision problem you already know how to handle.

1. Govern the decision, not the tool.

Most AI governance goes looking in the wrong place. It asks which tools are approved, which models are allowed, which prompts are permitted. But a tool no one has acted on has produced neither value nor risk. Both appear at a single moment: when an output stops being a draft and becomes the basis for a decision someone carries out. That decision is the thing to govern. You can allow every tool in the building. What matters is what happens when the output leaves the screen and shapes a choice.

2. Calibrate to consequence, not likelihood.

The instinct is to manage AI by how often it is wrong. That is the wrong axis. A model can be right ninety-nine times, and the hundredth, a client-facing number, a legal position, a pricing call, can cost more than the ninety-nine saved. Frequency is comforting and mostly irrelevant. Consequence is the axis that matters. Put your attention where a single error is expensive, not where errors are common. Most of them are cheap. A few are not, and those few are the whole game.

3. Absence is not neutrality.

If you have not decided how AI influences your decisions, it is tempting to believe you have stayed neutral. You have not. You are running a system already: the tool's default settings, the habits of whoever happens to be using it, the judgment of the person under deadline. That is a system, just an unexamined one, and it was designed by no one. The only real choice is whether the system that governs your decisions is one you chose, or one that got installed while you were not looking.

4. An error does not stay where it starts.

AI errors do not sit politely where they are made. A misclassification becomes a line in a report. The report becomes a recommendation. The recommendation becomes a decision, and by then the original mistake is three steps away and wearing a suit. This is why checking the tool is not enough. By the time an error is visible in the outcome, it has already moved through everything in between. You catch it at the decision, where it can still be stopped, or you catch it afterward, in the cost.

5. Every consequential decision needs a name.

A policy cannot be held accountable. A document does not answer the phone when something goes wrong. Only a person does. For any decision that carries real consequence, a name has to be attached before it executes: not a committee, not a function, a person who owns the outcome and has the authority to change it. Ownership assigned after the fact is not ownership, it is blame. The decision that belongs to everyone belongs to no one.

None of this requires new software. It requires seeing AI where it actually operates, at the decision, and refusing to let the consequential ones happen by default.

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Organizational

The Absence Audit

Put the third principle to work. The Absence Audit finds the decisions your organization is already making by not making them. It takes about an hour and a willingness to hear uncomfortable answers. Run it on any process where AI now touches the outcome, and ask five questions in order.

  1. Where did a default become a decision?Walk the workflow and find every place a tool's out-of-the-box setting now governs something that matters: a threshold, a category, a suggested action no one revisits. Defaults are invisible precisely because nobody chose them, which is exactly what makes them worth hunting for.
  2. Who chose it?For each one, ask who decided it should work this way. If the honest answer is "no one," or "it came configured that way," you have found a decision installed by absence. Mark it. These are the ones that surprise people later.
  3. Who pays if it is wrong?Follow the consequence downstream. If this default is wrong, who absorbs the cost: a client, a team, a regulator, the brand? Name them. A default with a victim and no owner is the risk you came here to find.
  4. Who has the authority to change it?Ask who could fix it today if they wanted to. Often no single person can, because ownership was never assigned. A decision no one can change is a decision no one controls.
  5. What question should have been asked before it went live?For each default you found, write down the question that would have surfaced it in advance. That list is the real output. Next time, those are the questions you ask before the tool goes live, not after it has already decided for you.

You finish with a short list of decisions your organization was making silently, each now carrying a name, a cost, and an owner. That list is worth more than most AI policies, because it is specific, and it points at exactly what to fix first.

Download the fillable worksheet (PDF)
Templates

Two you can lift

They map straight onto the Audit. The Record gives every consequential decision an owner. The checklist stops the next default from being installed in the first place. Copy them as they are.

Decision Ownership Record

One entry per AI-influenced decision that carries consequence.

Decision: ______________________________ What the AI did: ______________________________ What a human decided: ______________________________ Owner (a name): ______________________________ Date reviewed: __________ Next review: __________
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Before You Adopt

Run this before rolling out any AI tool.

[ ] What decision will this tool touch? [ ] What does it do by default that we never chose? [ ] What does our account tier actually expose about our data? [ ] Who owns the output? [ ] Who do we call when it is wrong?
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Personal

The Unasked Question

This is the personal habit under all of it, the one behind "someone has to ask." Good decisions are rarely lost on the hard questions everyone is already debating; those get answered. They are lost on the one question no one thought to raise. This is a way to find it on purpose, before the decision instead of after.

  1. State the decision plainly, in one sentence.If you cannot say it in a sentence, you are not ready to make it. Strip out the justification and the backstory. What, exactly, is being decided, and by when?
  2. List the questions everyone is already asking.Write down the questions live in the room, the ones being actively debated. These are the safe ones. They will get answered whether or not you do anything. Set them aside deliberately.
  3. Ask what would have to be true for this to go badly in a way no one is discussing.Not the obvious risks. The quiet ones. What assumption is everyone treating as settled? What would have to be true for the most confident person in the room to be wrong?
  4. Ask who is not in the room, and what they would ask.Every room has a missing perspective: the customer, the junior who will implement it, the regulator, the person who inherits the consequence. Seat them in your head and let each one ask their question.
  5. The question that surfaces from three and four is usually the one worth asking.It will feel slightly unwelcome. That is the signal, not a reason to hold it. Ask it out loud, before the decision, while it can still change the outcome. Make a habit of being the one who does.
Download the fillable worksheet (PDF)