Prompt
You are managing a late-stage opportunity for OpenAI API usage with consumption-based pricing. The buyer wants a credible quarterly forecast before signing, and your leadership team needs a commit number that reflects both upside and risk.
Walk through your forecasting methodology for consumption-based pricing. Focus on how you would build an initial forecast, pressure-test it during the sales cycle, and update it after close.
What to cover
- Inputs you would collect during discovery
- Business use case and rollout plan
- Expected users, requests, workflows, and model mix
- Technical constraints such as latency, context length, and guardrails
- Procurement, budget, and success criteria
- How you translate usage into revenue
- Show how you would estimate token or request volume by use case
- Explain how you would model ramp timing, seasonality, and adoption risk
- Distinguish committed baseline from upside scenarios
- How you validate the forecast
- What signals from pilot usage, solution design, or stakeholder alignment would increase confidence?
- What red flags would cause you to lower the forecast?
- How you communicate the number
- How would you present best case, expected case, and downside case to sales leadership?
- How would you align with the customer so the forecast is realistic without slowing the deal?
Example context
Assume the customer plans to launch two OpenAI-powered workflows: customer support summarization in month 1 and internal knowledge assistant in month 3. They expect a phased rollout across business units, but adoption assumptions are still uncertain.
Your answer should be structured, commercially grounded, and specific to OpenAI’s consumption model, not generic SaaS seat-based forecasting.
