The opportunity
Enterprise support at scale generates an enormous amount of signal: contact patterns, resolution paths, escalation triggers, knowledge gaps, agent workload. Most of it currently goes unused.
What you'll do
Define and own the AI and intelligence strategy for Enterprise Care, aligned: to the Journey Ownership model and the organization's 6- and 12-month roadmap.
Pilot, evaluate, and recommend AI tools for agent-facing use cases: co-pilot and in-call assist, smart case routing, knowledge base article suggestions, escalation prediction, and automated SOP drafting.
Partner with the Enterprise Support Program Manager and internal Product and: Engineering teams to move AI tooling from pilot to production, including integration with Salesforce, the KB platform, and ticketing systems.
Design the analytics framework that Enterprise Care runs on: a clear set of intelligence products (dashboards, signals, and periodic reports) that leaders and agents can act on without routing every question to a central data team.
Own the complexity intelligence layer: build or commission the tooling to classify enterprise contacts by complexity tier, identify high-effort accounts, and surface burnout signals from agent workload data.
Lead the evaluation of AI-readiness inside the organization: identify where data is clean enough to support AI use cases, where it is not, and what it would take to close that gap.
What they're looking for
- Translate AI capabilities into operational impact for Enterprise leadership:: you can explain what an LLM-based co-pilot is actually doing, why it improves AHT, and what error rates mean for agent trust, without needing a technical co-presenter.
- Stay current on the rapidly evolving landscape of AI tools for customer: support and bring informed, opinionated recommendations rather than waiting for consensus.
- Build the data and AI literacy of the Enterprise Care team over time, so: adoption is durable and not dependent on a single person.
- or more years of experience spanning customer support, customer success: operations, or enterprise service delivery, with at least 2 years specifically working with AI or ML tools in a production support or CX environment.
