Director, AI Enablement and Innovation

Define and own the knowledge architecture and taxonomy strategy for CX learning and enablement content (including canonical content models, metadata standards, and governance). Design and implement generative AI/RAG solutions, intelligent search, conversational assistants, and contextual learning surfaces that integrate with product workflows and frontline tools. Own strategy and execution for content lifecycle automation and integrations (CMS ↔ LMS ↔ KM ↔ product docs ↔ translation pipelines), improving authoring, review, and publishing velocity. Act as technical product owner for integrations and tooling (content APIs, search, knowledge base platforms, LLM infra), coordinate with Product Engineering and P&E for secure, scalable deployments. Drive experimentation and pilot programs to validate AI/automation use cases (e.g., auto-summary, auto-tagging, content recommendations, test generation). Evaluate vendors and open-source approaches; manage POC lifecycle. Implement measurement frameworks for knowledge efficacy: search success, answer relevance, RAG precision/recall, content reuse, time to publish, and impact on case deflection and support outcomes. Build data governance practices for content/AI (bias, privacy, provenance, feedback loops). Collaborate with Curriculum Development, Learning Business Partners, Adoption & Value, and Support to ensure innovation projects map to clear business outcomes. 8+ years' experience in knowledge management, product/technical program management, or AI/data product roles with at least 3+ years leading teams or programs. Practical experience deploying enterprise AI/search/KM solutions (LLM/RAG, vector search, knowledge graphs, conversational AI) and implementing integrations between LMS/CMS/KM systems. Strong technical literacy: APIs, data modeling, metadata/taxonomy design, and vendor evaluation for AI/KM tooling. Experience working with engineering/product teams to bring technical solutions to production. Bachelor's degree in technical field, information science, or equivalent experience. Hands-on experience with LLMs, vector databases, RAG approaches, and tools Experience in SaaS/HR tech or with support and digital adoption use cases. Familiarity with content localization pipelines and enterprise taxonomy management. Advanced degree in information science, AI, or related field. Technical product leadership, innovation & experimentation, systems thinking, data-driven decision making, stakeholder influence across technical and non-technical partners, risk and governance orientation.