Prioritize the roadmap for support automation with limited engineering capacity

Problem Statement Description

You are leading execution for a support automation roadmap serving creators who rely on a large-scale digital platform for publishing, monetization, audience growth, account health, and policy guidance. Creator support demand is rising, resolution speed is a visible pain point, and engineering capacity is constrained, forcing trade-offs across automation initiatives such as self-serve help, AI-assisted triage, workflow routing, agent tooling, proactive issue detection, and escalation improvements.

The interview focuses on how you would prioritize and sequence work when multiple stakeholders—creator success, support operations, trust and safety, engineering, data science, legal, and regional teams—are competing for limited resources. The expected scope is not to invent a full product vision, but to create an execution plan that improves time-to-resolution while protecting trust, quality, and operational stability.

Your response should clarify what “support automation” means in this context, identify the highest-value creator support workflows, and explain how you would make roadmap decisions under real constraints. You should also address how to launch safely, measure impact, manage dependencies, and adjust if automation creates poor outcomes or unexpected support burden.

The experience should consider:

- The highest-friction creator support journeys, such as account access, monetization issues, content moderation appeals, payout questions, and product troubleshooting

- Prioritization criteria for choosing among automation investments with limited engineering, data, and operations capacity

- Owners, dependencies, sequencing, and cross-functional coordination across product, engineering, support operations, policy, legal, and analytics

- Go/no-go criteria for launches, including quality thresholds, escalation paths, coverage, and readiness of support teams

- Rollout strategy across creator segments, geographies, issue types, and risk levels

- Guardrails for automation quality, creator trust, accessibility, responsible AI use, and human-in-the-loop escalation

- Communication plans for internal teams and creators when workflows, response times, or support channels change

- Rollback, incident response, and prevention mechanisms if automation misroutes cases, delays urgent issues, or gives incorrect guidance

The goal is to demonstrate how you would turn an ambiguous support automation mandate into a realistic, sequenced execution plan that improves creator resolution speed while balancing impact, effort, risk, and long-term operational leverage.