What technical risks would you review before launching AI assistance in food delivery group ordering?

Problem Statement Description

Swiggy is exploring AI assistance for food delivery group ordering, focused on office lunch coordinators who regularly collect preferences, manage budgets, handle dietary constraints, coordinate payments, and place repeat orders for teams. The AI assistant could help simplify decisions such as restaurant selection, order consolidation, reminders, substitutions, and repeat-order suggestions, but the launch sits inside a complex marketplace with real-time menus, restaurant prep variability, delivery batching, payments, and customer trust expectations.

In this Technical PM interview, you are being asked to identify and structure the key technical risks that should be reviewed before launch. The focus is not on designing the full feature, but on showing how you would evaluate readiness across data, AI behavior, integrations, reliability, privacy, security, accessibility, operations, and rollout risk.

Your response should be grounded in the Swiggy group-ordering workflow: multiple participants, one or more coordinators, time-sensitive lunch windows, changing restaurant availability, dietary and budget preferences, and the need to drive repeat group orders without increasing cancellations, support contacts, or operational load.

The experience should consider:

- Requirements and edge cases for group ordering, including participant changes, split payments, budget limits, unavailable items, substitutions, and order edits before checkout.

- Data quality risks around menu freshness, restaurant availability, delivery ETA, user preferences, dietary labels, historical orders, and group-level recommendations.

- AI-specific risks such as hallucinated recommendations, unsafe or misleading dietary suggestions, biased restaurant ranking, poor personalization, and lack of explainability.

- API and system dependencies across menus, carts, payments, restaurant confirmation, delivery assignment, notifications, loyalty, and customer support workflows.

- Reliability and latency expectations during peak office lunch hours, including graceful degradation if AI services, recommendation systems, or third-party components fail.

- Privacy, consent, and security concerns when using individual food preferences, office group behavior, payment details, addresses, and repeat-order history.

- Rollout, observability, and monitoring needs, including experiment design, anomaly detection, human support escalation, rollback triggers, and post-launch incident handling.

- Product trade-offs between automation and user control, AI convenience and trust, personalization and privacy, operational efficiency and marketplace fairness.

The goal is to demonstrate how you would think like a Technical PM preparing a responsible launch: identify the highest-risk failure modes, connect them to user and business impact, define what must be validated before release, and explain how Swiggy should monitor and manage the feature once it reaches real office lunch ordering traffic.