Metrics question

OpenAI wants to improve order-to-cash for its largest enterprise customers. How would you segment customers, map the highest-friction steps from order creation through invoicing, payment collection, and reconciliation, and prioritize the first three product investments? Include the success metrics you would use and how you would balance requests from Finance, Operations, and Engineering.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Whether you can map a B2B financial process end to end, prioritize with limited engineering capacity, and manage competing functional stakeholders.

Segment customers by contract size and payment complexity, for example top 50 enterprise accounts with custom terms versus the broader enterprise base on standard terms.
Map the order-to-cash chain: order creation, contract terms, invoicing, payment collection, dispute handling, reconciliation, and note where each function (Finance, Ops, Engineering) touches it.
Interview each function to find the highest-friction step, for example manual reconciliation of usage-based invoices against actual API consumption.
Prioritize the first three investments using a reach-times-friction score, for example automated invoice-to-usage matching, self-serve dispute resolution, and payment-method flexibility for large accounts.
Set success metrics: days sales outstanding, percent of invoices requiring manual touch, reconciliation time, and dispute resolution time.
Resolve competing asks by anchoring every request to its effect on DSO or manual-touch rate, not on who asked loudest.

Assumes usage-based billing is the main friction source for an AI API business and prioritizes invoice-to-usage matching first.
Gives a concrete metric target, for example cut manual-touch invoices from an assumed 30% to under 10% in two quarters.
Explicitly trades off Engineering's platform preference (a general billing rules engine) against Finance's need for a fast point fix, and picks a sequence rather than picking a side.
Uses DSO as the single cross-functional metric everyone can rally around.

Listing generic order-to-cash steps without naming which one is actually highest-friction for this business.
Prioritizing by stakeholder volume of requests instead of a reach or cost-of-friction score.
No named success metric, so the investment cannot be judged after the fact.

How would you validate that reconciliation, not collections, is really the biggest friction point?
What would you do if Engineering says the fix requires a full rebuild of the billing system?
How would you measure whether the fix actually reduced manual work, not just moved it?