AI Adoption & Enablement Leader

GE Vernova

1d•$113,200 - $188,800•Onsite

About The Position

The AI Adoption & Enablement Leader drives the adoption of new AI tools and technologies, skills development, and change management across Wind Engineering. This role ensures that new AI capabilities are not only introduced, but effectively adopted, trusted, and sustained in day-to-day engineering work. The position leads training, communications, and community-building efforts that help teams build confidence and practical capability with emerging AI technologies, while also supporting integration into engineering workflows where they create measurable value. This role owns the budget for AI enterprise costs and is accountable for ensuring that AI tool investments deliver sustainable, measurable value across Wind Engineering. Reporting to the Director – AI Strategy & Transformation, the ideal candidate is a people-oriented leader with a strong change management and learning design background who understands the engineering context in which AI is being deployed.

Requirements

Bachelor’s degree in engineering or a related field.
Significant experience in change management, organizational learning, technology adoption, or digital transformation in a complex technical organization.
Experience leading adoption and enablement of new digital tools or technologies across engineering or operational organizations.
Ability to manage a budget with discipline, including cost governance, value tracking, and investment rationalization.
Strong stakeholder engagement skills, able to build relationships across engineering teams, functional leaders, Digital/IT, Finance, and HR.

Nice To Haves

Excellent communication skills — written, verbal, and visual — with the ability to translate complex concepts into accessible content.
Demonstrated ability to operate across a matrixed organization and influence without direct authority.
Practical understanding of AI, generative AI, machine learning, automation, or AI-assisted engineering workflows; specifically, AMP and GE Vernova tools.
Experience with adult learning principles, instructional design, and modern learning delivery approaches (e.g., blended learning, micro-learning, peer learning).
Familiarity with community of practice design, knowledge management, and organizational learning systems.
Understanding of engineering workflows, including design, analysis, validation, testing, product support, or lifecycle processes.
Comfortable with data and analytics, able to create adoption dashboards, identify trends, and use evidence to drive decisions.
Strong growth mindset — energized by helping others develop capability and confident operating in a fast-moving, evolving technology landscape.
Comfortable operating in ambiguity and building structured enablement approaches where none previously existed.

Responsibilities

Drive structured adoption plans for new AI tools and technologies, aligned with engineering priorities, workforce readiness, and user needs.
Partner with engineering teams to introduce, pilot, and scale AI tools and technologies that improve engineering workflows and productivity.
Identify and address adoption barriers, including usability, trust, access, workflow fit, and cultural resistance.
Track adoption rates, utilization patterns, and user satisfaction across deployed AI tools to inform prioritization and investment decisions.
Ensure that tools not achieving sustained adoption are escalated, re-evaluated, or rationalized.
Own and actively manage the AI enterprise cost budget, including tools such as GitHub Copilot, compute infrastructure, AWS Services and new technologies.
Push for sustainable, value-driven implementation of AI solutions, ensuring costs are justified by measurable adoption, utilization, and business value.
Partner with Finance and Digital/IT to maintain budget visibility, forecast spend, and provide leadership with clear cost-versus-value reporting.
Evaluate new tool and technology investments through a structured lens covering capability fit, cost sustainability, adoption likelihood, and alignment to Wind Engineering priorities.
Drive rationalization of tools that are underutilized, duplicative, or not delivering value relative to cost.
Design, build, and maintain AI training and enablement programs that develop practical capability across Wind Engineering, from foundational AI literacy to advanced practitioner skills.
Tailor training to engineering roles and personas, ensuring relevance to how engineers, leads, and managers actually engage with AI in their workflows.
Develop role-based competency matrices that map to AI strategy and career progression expectations.
Continuously update training content to reflect new tools, evolving AI capabilities, governance requirements, and lessons learned from adoption.
Create and sustain AI communities of practice across Wind Engineering to accelerate knowledge sharing, peer learning, and reuse of effective approaches.
Design community programming including working sessions, showcases, lunch-and-learns, collaborative problem-solving events, and recognition mechanisms.
Ensure community activity connects to broader AI strategy priorities, reinforcing focus areas, sharing portfolio wins, and surfacing emerging opportunities from practitioners.
Maintain visibility of community health through engagement metrics, contribution quality, and knowledge reuse indicators.
Develop and execute a Wind Engineering AI communications strategy that builds awareness, understanding, trust, and enthusiasm for AI across the organization.
Design and manage change management plans for significant AI capability introductions, tool rollouts, and workflow transitions.
Ensure communications are tailored to audience, from engineering practitioners to functional leaders to senior executives.
Proactively surface and address resistance, skepticism, or misalignment through structured engagement and targeted messaging.
Partner with the AI Process Transformation Lead to ensure adoption communications are synchronized with portfolio milestones and initiative timelines.
Define and maintain a dashboard of adoption metrics across Wind Engineering's AI tool and capability portfolio, including utilization rates, active user trends, retention, workflow integration depth, and user-reported value.
Establish mechanisms to regularly capture user feedback on AI tool usability, reliability, trustworthiness, and workflow fit.
Use data to identify high-performing adoption patterns that can be replicated and low-performing areas that require intervention.
Report adoption performance to leadership with clear insights, trend analysis, and recommended actions.
Drive continuous improvement of the enablement model itself — refining training programs, community formats, communication approaches, and adoption mechanisms based on evidence.

Benefits

medical, dental, vision, and prescription drug coverage
access to Health Coach from GE Vernova, a 24/7 nurse-based resource
access to the Employee Assistance Program, providing 24/7 confidential assessment, counseling and referral services
GE Vernova Retirement Savings Plan, a tax-advantaged 401(k) savings opportunity with company matching contributions and company retirement contributions, as well as access to Fidelity resources and financial planning consultants
tuition assistance
adoption assistance
paid parental leave
disability benefits
life insurance
12 paid holidays
permissive time off