AI Use-Case Feasibility Assessment

A model demo answers one question. Whether the case survives value, data, model behavior, integration, risk, and daily operation answers another, and only the second one decides funding.

A model demo answers one question. Whether the use case works as a business decision answers another. We test one specific case across value, data, model behavior, integration, risk, and daily operation, and the evidence lands on one of four calls. Proceed, run an experiment, prepare the missing conditions, or stop.

The use-case owner signs one of four calls, proceed, experiment, prepare, or stop, with the feasibility evidence and the assumptions behind it attached to that call.

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Illustration of AI Use-Case Feasibility Assessment: a team assessing systems and data against a readiness checklist

Some of the 500+ brands we've worked with

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  • Atasun Optik
  • Memorial
  • Tosla
  • Isuzu
  • Duru
  • GS Store

Steps, gates, and who decides

How we work

The use case gets tested as a connected business and operating decision. Isolated model performance is an input, never the answer.

  1. Agree on the decision charter

    The use-case boundary, intended value, users, decision owner, constraints, and the evidence needed to choose among four outcomes all get settled first.

    AI assist
    From approved intake notes, the model drafts the use-case boundary and evidence checklist.
    Human gate
    Dependency mapping starts after the use-case owner accepts a narrow decision charter. Your use-case owner confirms the boundary before the feasibility case is assembled.

  2. Map the dependencies

    Data, model options, integration paths, operating responsibilities, and risks come under examination, together with the assumptions connecting them.

    AI assist
    From technical documents, the model groups data, model, and integration dependencies into a first map.
    Human gate
    Representative testing begins once the owner accepts the dependencies that could invalidate the case. Which dependency can invalidate the case remains a decision for the use-case owner.

  3. Test representative cases

    Representative and adverse cases run against the agreed acceptance conditions. Exceptions and unresolved evidence get recorded as they appear.

    AI assist
    Against the acceptance conditions, the model runs representative and adverse cases, recording each exception.
    Human gate
    Which critical case determines proceed, experiment, prepare, or stop? The use-case owner decides which critical result controls the outcome.

  4. Issue the outcome

    The chosen outcome goes on record with its conditions, residual risks, owners, and the next experiment, preparation task, or stop point.

    AI assist
    Using the tested cases, conditions, and residual risks, the model drafts the outcome dossier.
    Human gate
    Handoff requires the owner’s accepted outcome, with ownership of the next action recorded. Acceptance of the outcome and ownership of the next action stay with your use-case owner.

Named artifacts you keep

What you get

Each artifact shows the decision owner why the use case earned its outcome and what remains unresolved.

  • Decision record

    The proceed, experiment, prepare, or stop dossier

    The recommended outcome, supporting rationale, conditions, residual uncertainty, and next action for this specific use case.

  • Matrix

    Feasibility evidence and the assumptions behind it

    The sources, open assumptions, option dependencies, and questions that could change the feasibility result.

  • Test evidence

    Tested-case readout and the exceptions it raised

    The tested cases, observed behavior, acceptance results, and exceptions across the feasibility dimensions.

  • Roadmap

    Sign-off note for the feasibility outcome

    The accepted outcome, assigned owners, conditions, remaining risks, and next review or stop point.

Scope and honest limits

When to bring us in

Reach for this when a promising use case still carries feasibility questions nobody has answered. Enthusiasm and unanswered questions tend to grow together.

A good fit when

  • A demo already works, but nobody has checked whether the use case survives your real data, the systems it must integrate with, and the people who would run it daily.
  • Enthusiasm for the use case is growing faster than the answers, while the open feasibility questions have not been written down anywhere.
  • The person who would sponsor the build is genuinely open to stopping it, so a negative finding is worth as much to them as a positive one.
  • Value, data, model behavior, integration, risk, and daily operation each look plausible alone, but nobody has weighed them against each other.
  • You have partial evidence, a shortlist of options, and a rough sense of the dependencies, yet nothing that survives a challenge from finance or engineering.
  • There is no agreed test for what would count as acceptable behavior, so every discussion about the exceptions restarts from scratch.
  • A decision is expected soon, but nobody has written what the next step should be under each possible outcome.

Better handled as other work when

  • You need the build itself. This work decides whether to start, and the engineering and ongoing operation are separate commissions.
  • A regulator or certification body has to accept the outcome. The dossier records what we tested, and turning that into approval is your qualified authority's call.
  • You want a guaranteed return, a promised accuracy figure, or an assurance that deployment carries no risk. Feasibility work produces none of the three.

If one of these is closer to your situation, start here instead: View the parent service

Advice from people who build

We've worked with more than 500 brands since Zeo started in 2011. The people helping you decide where AI fits, and where it doesn't yet, are senior engineers and strategists who build and operate production AI systems. The advice stays grounded in work that actually shipped.

Content we've produced on this topic

Videoby Burak PehlivanEffective Use of Artificial Intelligence for E-Commerce Companies | Burak Pehlivan, icerik.comArticleby Didem HimmetliUse of AI in the Retail IndustryArticleby Elif Naz Akan KarakoçUse of AI in Banking and FinanceArticleby Elif Naz Akan KarakoçArtificial Intelligence in E-Commerce

Tools we use

Tools behind this work

  • Airtable

    keeps every assumption, exception, owner, and decision call attached

  • OpenRouter

    checks whether feasibility survives an alternative model or provider

  • Braintrust

    tests representative cases and failure slices against agreed rubrics

  • Jupyter

    reruns feasibility assumptions across value, data, cost, and operations

Next step

Choose the next move with evidence

One use case. Four possible outcomes. The first discussion identifies which evidence could still change the call.
Talk to Zeo

Before you decide

Questions we hear a lot

No. Model behavior is one dimension alongside value, data, integration, risk, and day-to-day operation. A good isolated result can still depend on a broken integration or an operating responsibility nobody owns. Representative and adverse cases test the connected use case. The decision follows the failure that materially affects that case.