AI Strategy & Enablement Lead

AI Strategy Enablement Lead Role Summary The AI Strategy Enablement Lead leads cross-functional coordination of AI initiatives, ensuring that AI efforts across teams are aligned, visible, prioritized, and connected to business and technical outcomes. This role acts as an internal AI strategist, evangelist, and operating-system builder for AI adoption across the company. While technical AI leads own execution within specific domains, this role ensures the broader AI portfolio is coherent, well-governed, communicated, and supported across teams.

Key Responsibilities

Define and maintain the company-wide AI strategy, including priority domains, investment themes, adoption principles, and measurable outcomes. Coordinate AI initiatives across quantum systems, software, hardware, operations, product, data, IT, and business teams to avoid duplication and surface shared opportunities. Partner with technical leaders to align domain-specific roadmaps with the broader company AI strategy.

Identify high-value internal AI use cases across engineering, operations, research, customer workflows, documentation, productivity, and decision support. Establish an AI portfolio management process, including initiative intake, prioritization, ownership, progress tracking, risk review, and executive reporting. Drive internal AI education, enablement, and evangelism through workshops, demos, playbooks, office hours, and success-story sharing.

Help define company-wide AI principles around responsible use, human oversight, data sensitivity, model selection, security, validation, and operational trust. Build alignment between experimental AI pilots and scalable deployment paths, ensuring promising efforts have owners, resourcing, governance, and adoption plans. Maintain visibility into external AI trends, tools, vendor capabilities, and competitive developments relevant to the company’s technical and operational strategy.

Translate AI opportunities into clear narratives for executives, technical teams, and operators, helping the organization understand where AI should be used and where it should not. Required Background Strong experience in AI strategy, technical program leadership, product strategy, innovation, digital transformation, or emerging-technology adoption. Ability to work credibly across technical and non-technical teams, including engineering, research, operations, product, security, and leadership.

Strong understanding of modern AI capabilities, limitations, adoption patterns, and organizational change management.

Requirements

Excellent communication skills, with the ability to turn technical AI concepts into practical priorities, operating models, and executive-ready narratives. Strong systems thinking: understands how AI initiatives interact across teams, tools, data flows, governance, and business goals. Preferred Background Experience introducing AI tools or platforms inside a technical organization.

Familiarity with ML lifecycle concepts, model evaluation, data governance, AI risk management, or responsible AI practices. Experience in deep tech, quantum computing, robotics, scientific instrumentation, advanced manufacturing, or complex engineering environments. Background in technical product management, strategy, consulting, developer relations, or internal platform enablement.

Experience building communities of practice, internal enablement programs, or cross-functional technology councils. Success Measures Company-wide AI strategy established with clear priorities, owners, decision forums, and communication channels. AI initiatives across teams are visible, coordinated, and mapped to measurable outcomes.

Reduced duplication of AI efforts and improved reuse of tools, data patterns, vendors, and lessons learned. Increased adoption of approved AI tools and practices across technical and operational teams.