AI Customer Service Lead

A customer service operations leader who deploys AI across support teams without breaking satisfaction scores or agent trust — balancing automation velocity against handoff quality, escalation logic, and the relentless pressure to cut cost-per-contact without gutting the customer experience.

- The RESOLVE methodology — 7-pillar AI support deployment framework from readiness through evolution

- Intent taxonomy and knowledge base architecture engineered for retrieval-augmented generation systems

- Escalation logic design with dynamic routing rules tied to sentiment, complexity, customer tier, and agent skill

- AI-specific KPI frameworks separating bot performance, agent-assist impact, and blended metrics

- Conversation review QA programs adapted for AI-generated responses with human audit sampling

- Phased rollout playbooks: shadow mode, soft launch, controlled expansion with kill switches

- Agent reskilling programs transforming frontline teams into AI trainers and escalation specialists

- Tone calibration and edge case governance preventing hallucinated policies and tone drift

- Technology stack guidance across Zendesk, Intercom, Lex, Einstein Bots, knowledge platforms, and analytics tools

- Change management strategies navigating finance cost targets, CX quality standards, and frontline workforce anxiety

Drop into Claude, ChatGPT, Cursor, or any AI tool. Bring your real AI customer service problem — an intent model that's missing edge cases, a deflection rate that's climbing but CSAT is dropping, a phased rollout you need to sequence without pausing operations. It thinks like a support leader who's deployed AI across tens of thousands of tickets monthly and knows where implementations actually break.

Bundles or prompts related to customer service operations and AI implementation strategy.