Explain the technical trade-offs of adding AI capabilities to Ads

Problem Statement Description

Product context: DoorDash is a local commerce and delivery platform; its products include restaurant delivery, DashPass, grocery and retail delivery, merchant tools, and dasher tools.

DoorDash is exploring how AI capabilities could be added to its Ads product across a local commerce marketplace that includes restaurants, grocery, convenience, and other merchants. The discussion is focused on suburban users, where order density, delivery distance, merchant variety, and customer intent may differ significantly from dense urban markets.

You are asked to explain the technical trade-offs involved in bringing AI into Ads, such as smarter targeting, ranking, creative generation, budget optimization, merchant recommendations, or measurement. The goal is not to pitch a single feature, but to reason through what changes when AI is introduced into an ads system that must serve consumers, merchants, Dashers, and the marketplace fairly and reliably.

Your answer should reflect a Technical PM perspective: how product requirements translate into data, model, API, infrastructure, privacy, experimentation, observability, and operational decisions. Consider how DoorDash would balance improved ad relevance and merchant ROI against latency, explainability, cost, marketplace health, and user trust.

The experience should consider:

- User and merchant workflows impacted by AI-powered ads, including discovery, sponsored placement, campaign setup, recommendations, and reporting.

- Data requirements and limitations, including order history, location, cuisine/category intent, merchant availability, delivery radius, inventory, pricing, and sparse suburban demand patterns.

- Model and system trade-offs across personalization, ranking quality, latency, scalability, cold start handling, freshness, and cost to serve.

- Ads marketplace constraints, including auction dynamics, merchant ROI, consumer experience, delivery reliability, Dasher supply, and avoiding unfair concentration of demand.

- Privacy, safety, and compliance considerations around using customer, merchant, location, and transaction data for ad targeting or optimization.

- Integration needs with existing DoorDash systems such as search, recommendation, merchant tooling, campaign management, measurement, experimentation, and billing.

- Reliability and observability requirements, including monitoring model performance, drift, latency, ad delivery correctness, attribution accuracy, and fallback behavior.

- Rollout trade-offs, including experimentation design, guardrails, phased launches, merchant communication, and rollback criteria.

Your goal is to clearly frame the technical decisions DoorDash would need to make before adding AI to Ads, explain the trade-offs behind those decisions, and show how you would evaluate whether the AI capability improves ads performance without harming consumers, merchants, Dashers, or marketplace economics.