Senior Director, Enterprise Customer Care - AI Strategy

Role overview A senior leadership role focused on embedding artificial intelligence and actionable analytics into a large enterprise customer care organization. The position owns the AI and intelligence roadmap end-to-end, turning contact-center signal (resolution paths, escalation triggers, knowledge gaps, agent workload patterns) into tools and products that help agents, leaders, and customers in production.

Responsibilities - Define and own the AI and intelligence strategy for Enterprise Care, aligned to a Journey Ownership model and near-term organizational roadmaps. - Pilot, evaluate, and recommend AI tools for agent-facing use cases such as co-pilot and in-call assist, smart case routing, knowledge base suggestions, escalation prediction, and automated SOP drafting. - Partner with program management, product, and engineering to move AI tooling from pilot to production, including integration with CRM, knowledge base, and ticketing platforms. - Design the analytics framework Enterprise Care runs on, delivering dashboards, signals, and periodic reports usable by leaders and agents without central data team routing. - Build or commission a complexity intelligence layer that classifies contacts by tier, flags high-effort accounts, and surfaces burnout signals from workload data. - Evaluate AI-readiness across the organization, identifying data quality gaps and what it would take to close them, and translate AI capabilities into operational impact for executives.

Requirements - 10+ years across customer support, customer success operations, or enterprise service delivery, with at least 2 years working with AI or ML tools in a production support or CX environment. - Hands-on experience with AI-assisted support tools such as Salesforce Einstein, Glance, or equivalents, including a clear point of view on what works at the agent level. - Operational data fluency: define a metric, query a dataset with analyst support, interpret distributions, and communicate findings without overstating precision. - Strong judgment about where AI genuinely helps versus where it creates noise, hallucination risk, or agent distrust, plus the credibility to defend that call with skeptical stakeholders. - Experience partnering with engineering or data teams to ship intelligence products: writing clear specs, reviewing implementations, and giving useful feedback without being an engineer. - Leadership presence with both frontline agents and executives, and a track record of building things that actually got used.

Benefits and work setup Hybrid working model with in-person collaboration days and flexibility by region. Total rewards include base pay set by skills, experience, and geographic zone, plus overtime or bonus where eligible, equity, and a benefits package. Compensation varies by zone and is set against local labor markets.