AI Process Transformation Lead

GE Vernova•Greenville, NC

3d•Onsite

About The Position

The AI Process Transformation Lead owns the Wind Engineering AI use case portfolio and ensures disciplined execution from ideation through production scale. Reporting to the Director – AI Strategy & Transformation, this role is responsible for translating high-potential AI opportunities into a structured, prioritized, value-driven transformation portfolio that improves engineering productivity, quality, speed, and decision-making. This leader serves as the portfolio integrator for AI-enabled process transformation across Wind Engineering. The role manages intake, prioritization, sequencing, maturity gates, value hypotheses, success metrics, and ROI tracking for AI initiatives. It also orchestrates execution pathway decisions across build, buy, partner, or ARC Foundry models, ensuring each initiative has the right ownership, resourcing, governance path, and value case. The ideal candidate is a transformation-oriented leader who can combine process discipline, Lean thinking, business case rigor, stakeholder alignment, and practical understanding of AI-enabled engineering workflows. This role is not primarily a hands-on AI architect or tool developer; rather, it ensures that AI initiatives are selected, shaped, governed, advanced, and scaled in a way that delivers measurable business outcomes for Wind Engineering.

Requirements

Bachelor’s degree in Engineering, Computer Science, Business, Operations, Data/Analytics, or a related technical or transformation field.
Significant experience leading process transformation, digital transformation, engineering operations, Lean initiatives, technology programs, or AI-enabled improvement portfolios in a complex technical organization.
Demonstrated ability to manage a budget and team of resources with clear prioritization, governance, metrics, and executive visibility.
Experience translating ambiguous business or engineering problems into structured initiatives with clear scope, value hypotheses, owners, milestones, and measurable outcomes.
Strong understanding of how engineering workflows operate, including design, analysis, validation, testing, product support, quality, or lifecycle processes.
Ability to partner effectively with technical teams, business leaders, finance, Digital/IT, governance, and external partners.
Excellent executive communication skills, including the ability to summarize complex initiatives into clear decisions, tradeoffs, risks, and value cases.

Nice To Haves

Strong transformation mindset with the ability to move from strategy to execution without losing sight of measurable business value.
Practical familiarity with AI, generative AI, machine learning, automation, analytics, or AI-assisted engineering workflows.
Experience with Lean, Kaizen, value-stream mapping, standard work, or continuous improvement methods.
Ability to identify where AI can eliminate waste, reduce rework, improve engineering throughput, strengthen quality, or accelerate decision-making.
Strong business acumen and comfort with ROI, NPV, benefit tracking, productivity metrics, cost avoidance, and value realization.
Comfortable operating in ambiguity and creating structure where processes, ownership, or decision rights are not yet fully defined.
Strong systems thinking; able to connect process, people, data, tools, architecture, governance, and adoption into a scalable operating model.
Able to influence without direct authority across matrixed engineering, digital, and enterprise teams.

Responsibilities

Own the Wind Engineering AI Use Case Portfolio: Manage the end-to-end AI use case portfolio across Wind Engineering, from idea intake through scaled production impact. Establish and operate a clear intake process for AI ideas originating from subsystems, engineering teams, Lean/Kaizen events, Digital, ARC Foundry, enterprise initiatives, and external partners. Maintain visibility of the active AI pipeline, including ideas under evaluation, POCs in progress, MVPs in development, production-scale pilots, and scaled solutions. Identify overlap, duplication, or fragmentation across AI efforts and drive consolidation where common patterns or reusable solutions exist.
Manage Prioritization and Sequencing of AI Use Cases: Develop and maintain a structured prioritization framework for AI use cases across Wind Engineering. Evaluate use cases based on expected business value, engineering impact, feasibility, data readiness, process maturity, risk, scalability, and alignment to strategic priorities. Sequence initiatives to balance quick wins, foundational capability building, and larger transformation-scale opportunities. Support leadership reviews by preparing clear summaries of portfolio health, value potential, execution status, risks, and key decisions needed.
Drive POC → MVP → PPS Maturity Gates: Define and operate a disciplined stage-gate process for progressing AI initiatives from early concept to proof of concept, minimum viable product, production pilot, and production-scale solution. Establish clear entry and exit criteria for each maturity gate, including business value, technical feasibility, data readiness, user validation, governance readiness, adoption plan, and scaling path. Ensure POCs are designed to test specific value hypotheses rather than becoming open-ended experiments. Partner with the AI Governance Lead and Senior AI Architects to ensure that scaling decisions consider model risk, data standards, Responsible AI expectations, architecture, maintainability, security, and compliance.
Define Value Hypotheses, Success Metrics, and ROI Tracking: Require each AI initiative to have a clearly articulated value hypothesis before significant resources are committed. Define success metrics appropriate to the use case, such as engineering hours avoided, rework reduction, defect prevention, cycle-time improvement, improved first-pass yield, faster decision-making, improved test coverage, reduced manual effort, or improved quality outcomes. Create a consistent language for evaluating AI value across teams, avoiding inconsistent or inflated business cases. Ensure initiatives that do not demonstrate sufficient value, feasibility, or adoption readiness are paused, redirected, or stopped. Build portfolio-level reporting that allows Wind Engineering leadership to understand value delivered, value in flight, and value at risk.
Orchestrate Build vs. Buy vs. ARC Foundry Decisions: Lead structured decision-making on the right execution path for each AI use case: internal build, vendor buy, ARC Foundry engagement, Digital/IT delivery, external partner support, or hybrid approach. Assess whether a use case is best served through existing enterprise platforms, reusable internal capabilities, ARC Foundry support, subsystem-level development, or third-party tools. Partner with Senior AI Architects and Digital/IT leaders to evaluate technical fit, integration complexity, scalability, cybersecurity, maintainability, and platform alignment.
Integrate Lean + AI into Engineering Process Transformation: Identify AI opportunities through process transformation, Lean events, Kaizens, value-stream mapping, quality improvement efforts, and engineering workflow reviews. Help teams distinguish between AI for design, AI for engineering productivity, and AI-enabled process transformation. Ensure AI is used to address meaningful process waste, bottlenecks, rework, quality gaps, knowledge-friction, and decision-cycle delays. Partner with Lean and subsystem leaders to embed AI opportunity identification into standard process improvement routines. Support the development and use of AI readiness checklists for transformation events, ensuring that AI opportunities are identified early and evaluated consistently.
Coordinate Cross-Functional Execution: Act as the connective tissue across engineering teams, Digital, IT, ARC Foundry, AI Architects, Adoption & Enablement, Governance, Finance, and business stakeholders. Ensure each AI initiative has a clear owner, sponsor, delivery model, adoption path, governance path, and measurable success criteria. Facilitate alignment across stakeholders when use cases span multiple subsystems, tools, workflows, or business functions. Ensure lessons learned from pilots and deployments are captured and reused across the broader AI portfolio.
Enable Scale and Sustainability: Ensure AI initiatives are designed with scale in mind from the beginning, including ownership model, support model, process integration, user adoption, governance, and lifecycle management. Work with the AI Adoption & Enablement Lead to ensure successful handoff from initiative delivery into sustained usage and behavioral change. Work with Senior and Embedded AI Architects to ensure technical solutions are reusable, maintainable, secure, and aligned with approved patterns. Help define when a solution should remain local, when it should become a reusable Wind Engineering capability, and when it should be elevated to enterprise or ARC Foundry scale. Create mechanisms to monitor whether scaled solutions continue to deliver value after initial deployment. Drive continuous improvement of the AI portfolio process itself, including intake quality, gate discipline, business case rigor, stakeholder engagement, and adoption feedback loops.

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