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Learn AI · the takeaway

Three questionssettle any AI pitch.

The same three that close the executive brief. Below them, the longer list from the deep dive, for when you want to go further.

Learn AIThe whole story

Three questions that settle any AI pitch

The three questions that settle any AI pitch
  1. 1
    What goes in, and what comes out?
    No clear answer, no project. Yet.
  2. 2
    How did it score on data it had never seen, and on how many rows?
    The demo score is the student marking their own exam.
  3. 3
    What does a wrong answer cost, and who signs the ones it gets to make alone?
    The threshold and the autonomy are your decisions, not the vendor’s.
Going further · by part of the deep dive
Part 1
Revisit

Before you believe a model

  1. 1
    What goes in, and what comes out?

    If nobody can name the inputs and the output, there is no model yet, only an ambition.

  2. 2
    How did it score on data it had never seen, and how many rows was that?

    The score on its own data is always flattering. Under a few hundred held-back rows, the honest score is a guess.

  3. 3
    Where on the dial does it sit, and why there?

    Linear, tree or deep. The right answer is usually further left than the slide, and every step right costs explainability.

Part 2
Revisit

Before you trust an assistant

  1. 1
    Which bucket is this request in?

    It knows this already; it needs our documents; it needs a tool; it should not do this alone. The bucket decides the design.

  2. 2
    What does it fetch, and who keeps those documents current?

    Answers over your data are only as good as the search step and the documents behind it.

  3. 3
    Which tools can it touch, and which steps are irreversible?

    Reads are cheap and reversible. Sends, payments and deletions are not. Those stay behind a person until the logs earn trust.

  4. 4
    Who signs the irreversible steps, and where is that recorded?

    An agent without a named approver is an incident waiting for a date.

Part 3
Revisit

Before you fund a rollout

  1. 1
    How many usable rows exist, and where did they come from?

    Most of the project is the cleaning. A demo built on something else proves nothing about your data.

  2. 2
    What does a false alarm cost, and what does a miss cost?

    The threshold is a business decision. If the team never asked you for these numbers, they chose it for you.

  3. 3
    What share still goes to a person at launch, and what is the plan to lower it?

    The business case is the reduction in review share over time, not the model price.

  4. 4
    Who owns the monitoring chart, and what is the alarm line?

    A model nobody is watching decays quietly. Ask how long a retrain takes.

  5. 5
    Which rung of autonomy is this asking for, and which controls for that rung are already in place?

    The honest answer is usually one rung lower than the slide. Controls come before autonomy, not after the first incident.

Every dataset in this section is synthetic and every model is fitted live in your browser. These are illustrative walk-throughs, not client results. The right model for a decision depends on your data, your systems and what the decision costs when it is wrong.

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