Use an Assumption Register in a Business Case Interview

TL;DR

• In this fictional practice scenario, a business case interview often provides incomplete information.
• Use fields for assumption, reason, decision sensitivity, and validation step.
• Keep the criteria appropriate to the case and avoid inventing universal success thresholds.

Make the uncertainty part of the recommendation

A business case interview often provides incomplete information. You must move forward without pretending the missing facts are known. An assumption register gives you a compact way to record what you are assuming, why it matters, and what evidence would change the recommendation.

This is a practice framework using a fictional business scenario, not financial advice or a forecast. The goal is to make reasoning reviewable. A neat calculation is not useful if the inputs quietly shift from rough assumptions into apparent facts.

Related reading: Best AI Interview Assistant Software for Financial Analysts.

Start with the decision the case requires

Clarify whether you are recommending a launch, diagnosing a problem, choosing a segment, or prioritizing an experiment. The same market information can support different decisions, so identify the requested outcome before building a model.

For a fictional subscription service, the question might be whether to pilot a new onboarding package for a small customer segment. That is narrower than deciding whether the whole company should change its business model. Keep the recommendation within the scope of the case.

Ask what constraints matter: budget, time, operational capacity, or the need to preserve the existing customer experience. If the interviewer asks you to assume, choose a clear working value or qualitative condition and label it. Do not stall indefinitely waiting for perfect information.

Create a four-field register

Use fields for assumption, reason, decision sensitivity, and validation step. For example, you might assume that the target segment has a recurring onboarding problem. The reason could be limited case evidence. The recommendation is sensitive because a package has little value if the problem is rare. The validation step could be a focused customer study or a bounded pilot.

Keep given facts in a separate area. If the case explicitly states current usage, do not mark it as your estimate. If you infer willingness to pay from usage, label that inference rather than merging the two ideas.

Limit the register to assumptions that can change the decision. Recording every minor detail creates paperwork without improving the recommendation. A useful register highlights the uncertainties that deserve attention first.

Distinguish arithmetic from evidence

When you calculate an illustrative result, show the units and the assumptions behind it. Multiplying a hypothetical number of customers by an assumed price produces a scenario, not a verified revenue forecast. State that distinction clearly.

Use a small number of scenarios if the result is sensitive to an uncertain input. You might compare low and high adoption assumptions to see whether the proposed pilot still makes sense. Do not create a complicated spreadsheet simply to give uncertain inputs an appearance of precision.

Explain what the calculation contributes. It may reveal that an operational cost dominates the decision or that the pilot can be bounded cheaply enough to gather evidence. If the arithmetic does not influence the recommendation, it may not deserve much interview time.

Challenge the most important assumption

Ask the practice interviewer to invalidate one high-sensitivity assumption. Suppose the target customers already solve the onboarding problem with an internal process. Would you abandon the package, narrow the segment, or change the experiment? Trace the impact rather than defending the original idea automatically.

Consider alternative explanations for the available evidence. Low feature usage may reflect lack of awareness, lack of need, or a broken workflow. Each suggests a different next action. Do not choose the explanation that best supports your preferred proposal without a reason.

A good register makes this update easy: identify the changed row, revise the affected part of the recommendation, and preserve the parts that still hold. This demonstrates adaptable reasoning rather than indecision.

Propose an experiment that resolves the uncertainty

Choose a validation step connected to the decision. If the key uncertainty is whether the problem matters, a feature build may be premature. If the problem is established but the workflow is unclear, a small prototype or controlled pilot may provide more relevant evidence.

State what result would support continuation, revision, or stopping. Keep the criteria appropriate to the case and avoid inventing universal success thresholds. The important point is that you know how the evidence will influence the next decision.

Also identify practical limits. A small pilot may not reveal long-term retention or full operational costs. Name what it can and cannot establish. This prevents a modest experiment from being presented as proof of an entire business strategy.

Rehearse a concise recommendation

End with the recommendation, the two assumptions that matter most, the proposed validation, and the condition that would change your view. Do not read the entire register aloud. Use it to keep the summary honest and focused.

Phantom Code AI’s mock-interview workflow can provide a setting for practicing role-related reasoning. The assumption register is a custom worksheet, and any generated business claim should be checked against the case facts. A confident answer does not supply missing market evidence.

If you are comparing practice tools, the AI interview software guide provides a broader shortlist. In the trial, look for follow-ups that challenge the assumptions behind your recommendation. The strongest case answer shows how you reached a decision with incomplete information and what you would learn next to improve it.