Senior Director, Enterprise Customer Care - AI Strategy

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. The Senior Director of AI Strategy exists to change that.

This is a leadership role for someone who understands what AI and intelligent tooling can realistically do inside a support organization, and who knows how to put those capabilities to work for agents, leaders, and customers. You bring a practitioner's eye (you have used these tools, not just read about them) and an operator's discipline (you know the difference between an impressive demo and something that sticks in production).

You will own Toast's AI and analytics roadmap for Enterprise Care: defining the strategy, piloting and selecting tools, and embedding intelligence into how the team operates every day.

• 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.

• 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.

• 10 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.

• Hands-on experience with AI-assisted support tools: you have worked with, not just evaluated, products like Salesforce Einstein, Glance, or equivalent, and you have a point of view on what actually works at the agent level.

• Ability to work with data at an operational level: you can define a metric, query a dataset with analyst support, interpret a distribution, and communicate what it means without overstating precision.

• Strong instincts about where AI genuinely helps in support workflows versus where it creates noise, hallucination risk, or agent distrust, and the credibility to make that call stick with skeptical stakeholders.

• Experience partnering with Engineering or Data teams to get intelligence products built: you can write a clear spec, review an implementation, and give useful feedback without being an engineer.

• Leadership presence: you can