Product design question
How would you improve Ramp's AI Token Spend Management for finance teams tracking AI costs?
- Ramp
- Product design
- Medium
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What this question tests
Tests product design for a finance tool addressing a newly emerging cost category that finance teams are not yet used to tracking.
How to approach it
- Define the user need: a finance team trying to understand and control AI API spend across many teams and vendors, a cost category that did not exist in their budgets a few years ago.
- Identify the core pain: AI token spend is fragmented across many providers, unpredictable, and often owned by engineering teams with little finance visibility.
- Prioritize visibility first: consolidated tracking of spend across providers like OpenAI, Anthropic, and others, broken down by team and project.
- Add forecasting: since token usage can spike unpredictably with new feature launches, provide trend based forecasting so finance can anticipate budget impact.
- Add control mechanisms: budget alerts and spend caps per team or project, similar to how Ramp already handles traditional card spend controls.
What a strong answer includes
- Prioritizes consolidated cross provider visibility first, correctly identifying that fragmentation across vendors is the core pain finance teams face with this new cost category.
- Proposes forecasting specifically because AI spend is more volatile and spike prone than traditional software spend, requiring different budgeting tools than Ramp's existing card products.
- Extends Ramp's existing spend control patterns, like budget caps and alerts, to this new cost category rather than building a fully separate paradigm.
Common mistakes
- Designing this as a simple reporting dashboard without addressing the forecasting need created by volatile, spike prone AI usage.
- Ignoring the organizational tension between finance oversight and engineering autonomy over tool choices.
Likely follow-up questions
- How would you forecast AI spend for a team about to launch a major new AI feature?
- What would you do if engineering pushed back against spend caps on their AI tooling?
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More questions from Ramp
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop