01Key Responsibilities
- Translate product rules and releases into SOPs, knowledge, training, controls and operational acceptance criteria.
- Own service, backlog, quality, complaints, incident and exception metrics for assigned products.
- Design AI-assisted workflows with transparent permissions, human checkpoints and fallback paths.
- Coordinate Product, Engineering, Operations, Risk, Compliance, Legal and Customer Service through changes.
- Validate that AI knowledge sources are current, versioned and traceable to approved documentation.
- Run post-launch reviews and identify root causes of contact, failure or manual effort.
- Maintain risk/control assessments and evidence for material operational changes.
- Prioritize automation based on value, risk and customer impact rather than novelty.
- AI-enabled ways of working:
- Use approved GenAI copilots, enterprise search/RAG, analytics and workflow agents to reduce repetitive work and improve decision preparation.
- Keep prompts, outputs and automated actions within approved data-access, confidentiality and retention rules.
- Correct inaccurate summaries, classifications or recommendations before they become customer, operational or system records.
- Feed recurring AI errors, knowledge gaps and process friction into product, documentation and control improvement.
- Human accountability: AI is a copilot, not the accountable decision-maker.
- The role holder remains responsible for judgement, quality, privacy, security and appropriate escalation.
- Credit, fraud blocking, legal/regulatory, financial advice, safety-critical, clinical, employment and other high-impact decisions must follow delegated authority and required human review.
- Success Measures:
- Product changes launch with fewer operational defects
- Service cost and manual effort decline
- Customer and control outcomes improve
- Knowledge/SOPs remain aligned to product releases .
