AI Enablement Lead
AI Enablement Lead
Posted 11 days ago
SENIORITY
Manager
About the role
- Strategy & Evangelism
- Define and own the enterprise AI adoption strategy, aligned to business priorities and operating model
- Act as the internal evangelist for AI across the organization — building executive buy-in, business unit champions, and grassroots enthusiasm
- Identify, prioritize, and build the business case for high-value AI use cases across functions (Sales, Finance, Ops, HR, Customer Service, etc.)Translate ambiguous business problems into concrete AI-enabled solutions, partnering with business leaders to scope and sequence initiatives
- Represent AI strategy and progress to executive leadership and the board as needed
- Business Use Case Delivery
- Partner directly with business unit leaders to identify workflows where AI can measurably improve speed, quality, or cost
- Own end-to-end delivery of prioritized use cases: from business case, to pilot, to scaled deployment, to measured value
- Build and manage a prioritized AI use case pipeline / roadmap with clear ROI tracking
- Establish repeatable frameworks for evaluating, piloting, and scaling AI solutions across the businessIT & Engineering Adoption
- Drive AI adoption within IT and engineering to increase development speed, code quality, and delivery efficiency
- Own rollout, training, and adoption strategy for developer-facing AI tools (e.g., GitHub Copilot, coding assistants, internal AI platforms)
- Partner with engineering leadership to embed AI into the SDLC — code generation, testing, code review, documentation, incident response
- Define and track adoption and efficiency metrics for AI-assisted engineering (e.g., cycle time, defect rates, developer satisfaction)
- Microsoft Copilot & Enterprise Tool Adoption
- Own the enterprise-wide adoption strategy for Microsoft Copilot and other licensed AI productivity tools
- Design and run enablement programs: training, champions networks, use-case libraries, adoption campaigns
- Track and report adoption metrics beyond vanity numbers (licenses issued) — actual usage depth, workflow integration, and productivity impact
- Partner with procurement/vendor management on tool rationalization and licensing strategyAI Governance
- Build and own the enterprise AI governance framework: policies for responsible use, data handling, model risk, and human oversight
- Establish approval workflows and accountability models for deploying AI into business-critical processes
- Partner with Legal, Risk, Compliance, and Security to ensure alignment with regulatory requirements (e.g., EU AI Act, sector-specific regulation)
- Own the AI risk register and ensure a documented, auditable decision trail for AI systems in production
- 8+ years in technology strategy, digital transformation, or enterprise IT, with recent (2+ years) hands-on AI/GenAI implementation experience
- Demonstrated experience translating business problems into deployed AI solutions — not just advisory or strategy-deck experience
- Experience driving AI/tooling adoption within an engineering or IT organization, with a grasp of SDLC and developer workflows
- Experience leading Copilot, ChatGPT Enterprise, or comparable enterprise AI tool rollouts at scale
- Working knowledge of AI governance frameworks and emerging regulation (EU AI Act, NIST AI RMF, ISO/IEC 42001, or equivalent)
- Strong stakeholder management skills — credible with both business executives and engineering leadership
- Experience building and running cross-functional programs involving Legal, Compliance, Security, and Data teams
- Comfortable operating at both strategic (roadmap, board reporting) and tactical (pilot design, adoption metrics, hands-on rollout) altitude
- Prior experience in a Head of AI Enablement, AI Transformation, or similar hybrid role
- Consulting background implementing AI programs, or in-house experience scaling AI beyond pilot stage
- Familiarity with Microsoft's AI stack (Copilot, Azure AI, Power Platform) or equivalent enterprise AI ecosystem
- Experience standing up an AI Center of Excellence or governance council from scratch
- Success Metrics (First 12 Months):AI adoption strategy and governance framework defined, approved, and operational[X] business use cases moved from pilot to scaled production with measured ROICopilot / enterprise tool adoption increased from [baseline]% to [target]% active usage
- Measurable improvement in engineering delivery metrics (cycle time, code quality, deployment frequency) attributable to AI tooling adoptionAI governance council established and operating with clear escalation and sign-off processes
- Zero material AI-related compliance or risk incidents
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