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AI Product Management

Deciding which AI project deserves to exist

Before any screen or model, someone has to decide which task deserves to be automated and which does not. This framing mission sets the AI product strategy upstream of development, so the project that starts is the one worth starting.

  • Prioritising by frequency and risk
  • Build versus buy trade off
  • A measurable roadmap

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Key challenges

Most artificial intelligence projects do not fail on the technology, they fail on the choice of task. A well built model on the wrong scope produces nothing useful. AI product strategy means making that choice early, against an explicit criterion, rather than letting it get decided by default as development goes along.

  • Value before the project

    A project is justified by what it must deliver, time given back, an error avoided, a shorter lead time, never by the wish to use AI for its own sake. If nobody can say why the project deserves to exist, it does not start.

  • Frequency and the cost of error

    A task deserves automation when it recurs often and its error stays recoverable. The other cases call for a different answer: stronger human oversight, a simple rule, or no project at all.

  • The scope that grows on its own

    Without an explicit trade off, a first version expands screen after screen until it becomes hard to defend internally or externally. Cutting the scope is an active choice, not a budget constraint.

  • Knowing when to walk away

    Saying that a project does not deserve to exist, or not yet, saves more time than any optimisation. It is the hardest call to own in public, and the most profitable one.

What your PO / PM does

  • 1

    Frame the expected value

    Work with the teams concerned to establish what the project must actually change in their work, and on what basis that change can be observed once the tool is in place.
  • 2

    Prioritise the use cases

    Cross the frequency of each candidate task with the cost of its error to rank the cases, keep the ones that hold up, and set aside the rest without burying them silently.
  • 3

    Weigh build versus buy

    Compare what an off the shelf tool already covers, what would need to be built to measure, and what each option commits you to in dependency, maintenance and lead time.
  • 4

    Set the success criteria

    Define before launch what will tell whether the project worked, and the point beyond which stopping is better than continuing.

What stays in your hands

  • Prioritised roadmap

    The ranked list of retained use cases, with the reason for each one's position and what sets it apart from the cases left out.

  • Framing memo

    A short document that fixes the scope of the first version, what it covers and what it deliberately leaves aside.

  • Prioritisation matrix

    The frequency and error cost crossing applied to every use case studied, so the trade off stays traceable afterwards.

  • Success and exit criteria

    What must be observed to judge the project, and the written condition that triggers a stop if the project does not hold its promise.

Expected results

  • Decisions people can stand behind

    The choice to launch or to walk away rests on an explicit criterion, not on intuition or the pressure of the moment, which makes it defensible in front of leadership.

  • Fewer projects, better chosen

    The number of initiatives launched goes down, but each one answers a task that was actually identified rather than an idea that looked good on paper.

  • Teams aligned on a clear scope

    Developers, users and leadership share the same definition of what the first version must do, and what it does not do yet.

How we move forward

  1. 1

    Value diagnostic

    Identify candidate tasks with the teams concerned, naming each action the way it is actually named on the ground, before talking about any tool.
  2. 2

    Mapping and prioritisation

    Cross frequency and error cost for every candidate task, and rank the use cases instead of handling them all at once.
  3. 3

    Build versus buy trade off

    Study what the market already offers against what a custom build would allow, with what each option implies once in place.
  4. 4

    Roadmap and exit criteria

    Write down the retained scope, the order of delivery, and the condition that will say whether the project kept its promise or must stop.

Who it is for

This mission is for leadership teams and project owners who have several possible AI use cases and no settled target yet, or who want to check that a project already under way really answers a real task before investing further in it.

In the same family

Frequently asked questions

How do you build an AI roadmap?By starting from tasks teams actually perform, not from a list of ideas. Each candidate task is measured on its frequency and the cost of its error, ranked, then ordered into a roadmap that starts with the strongest case.
How do you prioritise AI use cases?By crossing two criteria, how often the task happens and what it costs if it fails. A frequent task with a recoverable error deserves priority. Rare cases or ones with a costly error call for a different approach.
Should you build or buy an AI solution?It depends on what the market already covers against the real need, and what each option commits you to in dependency and maintenance. The trade off is made case by case, not on principle for either option.
How do you measure the ROI of an AI project without inventing figures?By defining, before launch, what will be observed once the tool is in place, and comparing that observation to the starting situation. A figure announced before any real measurement is an estimate, not a result.
When should you not launch an AI project?When nobody can name the precise task it should change, when the target task is too rare to justify the effort, or when the possible error would cost more than the expected gain. In those cases, walking away is the right call.

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