How do you give a reliable delivery forecast to executives when there is a lot of uncertainty?
What the interviewer wants to hear
- Use ranges and confidence levels, not single dates
- Base forecasts on data: three-point estimates, velocity or throughput
- Use Monte Carlo or probabilistic forecasting
- Explain the main assumptions and risks driving the range
- Update the forecast regularly as uncertainty reduces
- Separate internal targets from external commitments
Model answer
I avoid giving a single date when uncertainty is high, because it sounds precise but is usually wrong. Instead I give a range with confidence levels, for example a 50% chance by June and 85% by July. I base that on data: three-point estimates for planned work, or historical throughput and velocity for agile teams, and I run a Monte Carlo simulation where possible. I explain the few assumptions and risks that drive the range, such as a vendor API or a regulatory approval, and what we are doing to reduce them. I also separate the team's internal target from an external commitment, which should sit at a higher confidence level. Then I update the forecast regularly, so executives see the range narrow as we learn. This builds trust because surprises become rare and every change has a reason.
Can you answer it under time pressure?
Take a timed hard round and get scored on every answer.
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