When polished output is easy to generate, the interview needs to reveal how the candidate reached the recommendation. Use a short, realistic work sample, make the AI policy explicit and ask the candidate to defend their assumptions.
Decide what the exercise is measuring
A take-home task should resemble the actual work. If the role involves commercial analysis, ask for a decision memo using a fictional dataset. If it involves coordination, ask for a launch plan with dependencies. Do not use visual polish as the main score for a role that needs operational judgment.
Set a time budget, provide the inputs and explain the evaluation criteria in advance. As a suggested default, keep an unpaid exercise to roughly an hour. Pay for longer assignments and avoid asking applicants to complete commercially usable client work for free.
State the AI policy before the task
If AI is allowed, ask candidates to explain where they used it, which outputs they checked and what they changed. Do not demand access to personal chat histories or accounts. If the assessment needs a tool-free component, say so and create a controlled, accessible format.
Do not treat an AI detector as conclusive evidence of misconduct. Evaluate the candidate’s explanation and ability to work with the material. Provide equivalent access where a particular paid tool is essential.
A sample case you can adapt
Give candidates a synthetic funnel with visits, qualified enquiries, booked calls and closed deals. Include an ambiguous definition and one inconsistent total. Ask them to recommend the first investigation, not to manufacture a complete growth strategy.
- Output: one page with the issue, evidence and next action.
- Constraint: no additional budget for the first experiment.
- Follow-up: explain what could make the recommendation wrong.
- Change: introduce one new fact during the discussion.
This reveals whether the candidate notices data problems and can update a view rather than defend an attractive slide at all costs.
Make the debrief the centre of the assessment
Ask which number they trust least, which assumption has the biggest effect and who would implement the recommendation. Then ask for the next three actions and an escalation condition. A good answer connects analysis to execution and distinguishes uncertainty from missing effort.
Score data interpretation, prioritisation, communication and operational feasibility separately. Record evidence immediately and compare candidates on the same rubric. A different recommendation can still be strong if the reasoning is sound.
Close the loop respectfully
Tell candidates when to expect an outcome and who can answer questions. Retain assessment material only as long as necessary under your hiring process. A fair, bounded interview is also an early demonstration of how the founder makes decisions and treats people.
Preparing as a candidate? Practise with our interview questions and fictional worked business case.
FounderSide’s practical editorial guidance for startup hiring and operations. Examples and assessment frameworks are illustrative, not reported client outcomes. Adapt them to your company’s stage and needs.
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