Job Description Summary

The Director of AI Strategy & Transformation for Wind Engineering will establish and lead the application of Artificial Intelligence (AI) as a scalable and value-generating capability across the Wind Engineering organization. This role is accountable for defining the AI vision and roadmap, building the organizational and technical systems required to scale, and ensuring AI initiatives deliver measurable business outcomes.

As a newly created leadership role, this position requires a leader who can operate effectively in uncertainty, shape strategy while driving execution, and build durable structures—including governance, operating models, and partnerships—that enable sustained impact.

Job Description

Key Responsibilities:

AI Strategy, Vision & Value Delivery

• Define and own the AI strategy and multi-year roadmap for Wind Engineering, aligned with business priorities and enterprise AI direction.
• Identify, prioritize, and sequence high-value AI use cases across design, analysis, validation, manufacturing support, operations, reliability, and lifecycle optimization.
• Establish clear value hypotheses, success metrics, and ROI tracking for AI initiatives.
• Ensure balance between rapid experimentation and development of scalable, repeatable capabilities.

Scalable Systems, Platforms & Governance

• Design and implement AI systems that scale, including standards for data, models, tooling, deployment, and lifecycle management.
• Establish and lead AI governance for Wind Engineering, including:
  • Model risk management and validation
  • Data quality, lineage, and access standards
  • Responsible AI, safety, and regulatory compliance
  • Decision rights and investment prioritization
• Partner with Digital, IT, and Data leaders to align on platform strategy, architecture, and MLOps practices.
• Ensure AI solutions are maintainable, auditable, and reusable across products and teams.

Organizational Design & Talent Leadership

• Define the AI operating model for Wind Engineering (centralized, federated, or hybrid), including roles, interfaces, and engagement models.
• Build, lead, and develop a high-performing team of AI engineers, data scientists, and technical leaders within this operating model.
• Define capability requirements, skill profiles, and career paths for AI-enabled engineering roles.
• Lead hiring, onboarding, and succession planning for critical AI leadership and technical roles that would include direct and indirect reporting lines.

Adoption, Change & Engineering Integration

• Drive broad adoption of AI within engineering, embedding AI tools, workflows, and decision-support into standard engineering processes.
• Lead identification of core AI skillsets required across the entire Wind Engineering team. Incorporate these into competency models and work to develop and institute training as required.
• Partner with engineering leaders to integrate AI into design reviews, validation workflows, and operational decision-making.
• Lead change management efforts, including training, communications, and communities of practice, to increase AI fluency and trust.
• Establish feedback loops to continuously improve usability, effectiveness, and adoption.

External Partnerships & Ecosystem Development

• Develop and manage strategic external partnerships with AI technology providers, software vendors, startups, universities, and research institutions.
• Evaluate when to build, buy, or partner to accelerate capability development and value realization.
• Structure and govern partnerships to ensure IP protection, scalability, security, and long-term value.
• Represent Wind Engineering in external forums and collaborations related to AI and advanced engineering methods.

Cross-Business Collaboration

• Serve as a partner to Product Management, Supply Chain, Services, Commercial, and Enterprise Digital teams to ensure AI initiatives deliver end-to-end business impact.
• Align Wind Engineering AI efforts with enterprise AI standards, platforms, and investments, influencing direction where needed.
• Enable reuse and scaling of AI solutions across functions, regions, and product lines.

Leadership in Ambiguity

• Translate ill-defined problems and emerging opportunities into clear strategies, executable plans, and scalable solutions.
• Make informed tradeoffs across speed, risk, technical depth, and business value.
• Set direction and maintain momentum in a