Senior AI Transformation Lead

Senior AI Transformation Lead

Global Service Organization

Overview

Seeking a highly experienced AI Transformation Lead to establish and drive the organization's AI strategy across the global Service business.

This is a hands-on leadership role responsible for identifying, developing, implementing, and scaling AI-enabled solutions that improve productivity, knowledge reuse, service model design, decision-making, and operational effectiveness across a globally distributed service organization.

Unlike traditional AI development roles, this position combines strategic leadership, technical expertise, organizational change management, and project execution. The successful candidate will not only build solutions but will also help define the long-term AI framework, coach and educate service teams, establish governance models, and demonstrate measurable business outcomes through initial implementation projects.

The primary objective is to enable service specialists to spend more time serving customers, developing innovative solutions, and growing the business by reducing reliance on manual spreadsheets, presentations, repetitive calculations, and disconnected workflows.

Key Responsibilities

AI Strategy & Transformation

• Define and implement the long-term AI roadmap for global Service organization.
• Establish a scalable framework for AI adoption across multiple service businesses and technology domains.
• Develop practical, business-focused AI use cases that deliver measurable productivity improvements.
• Identify opportunities to transform existing spreadsheet-, PowerPoint-, and calculation-driven processes into scalable digital solutions.
• Create reusable AI capabilities and assets that can be leveraged across multiple service organizations and regions.

AI Solution Development & Delivery

• Lead the design and implementation of AI-enabled business solutions, copilots, agents, and decision-support tools.
• Develop flexible modeling environments that allow service teams to rapidly adapt assumptions, customer requirements, and service models without rebuilding analyses from scratch.
• Integrate AI solutions with existing digital infrastructure, service applications, simulation tools, and business platforms.
• Deliver an initial high-impact pilot project demonstrating measurable business value and organizational adoption.
• Guide the successful transition of pilot projects into scalable operational capabilities.

Data Strategy & Governance

• Define foundational requirements for AI-ready data architecture.
• Establish governance models supporting secure, compliant, and scalable AI adoption.
• Provide guidance regarding data storage, accessibility, ownership, and usage standards.
• Support development of a coherent data ecosystem that connects service data, engineering knowledge, simulation outputs, and operational information.
• Ensure AI outputs are built on validated data structures and reliable business information rather than stand-alone generative responses.

Organizational Enablement & Coaching

• Serve as the primary AI advisor for service leadership.
• Educate leaders and team members on practical AI applications and realistic use cases.
• Coach service professionals in developing and utilizing AI tools and agents within their daily workflows.
• Build organizational AI capabilities through knowledge transfer and hands-on engagement.
• Foster an AI-enabled culture that encourages participation, experimentation, and continuous learning.

Global Collaboration

• Partner with stakeholders across North America, Europe, the Middle East, and other global service locations.
• Collaborate with service leaders supporting hydrogen, methane pyrolysis, carbon capture, nuclear, geothermal, thermal power generation, and emerging technologies.
• Coordinate cross-functional initiatives involving service operations, digital teams, engineering, product management, and business leadership.
• Ensure solutions are scalable across a globally distributed organization.

Example AI Use Cases

Service Productivity & Knowledge Management

• AI-powered service knowledge assistants
• Service documentation and technical content generation
• Intelligent retrieval of engineering and operational knowledge
• Automated meeting summaries and action tracking
• Service proposal generation and optimization

Service Model Design & Simulation

• AI-supported service model configurators
• Digital service contract design tools
• Operational and maintenance optimization models
• Scenario planning and business modeling environments
• Customer-specific service solution generators

Operational Effectiveness

• Engineering productivity assistants
• Workflow automation and process orchestration
• Predictive operational insights
• Asset performance intelligence
• Resource planning and decision support

Data & Digital Foundation

• Knowledge graph and retrieval platforms
• AI-ready data lake structures
• Agent ecosystems integrated with enterprise data sources
• Self-service AI tools for business users
• Reusable AI frameworks and templates

Key Qualifications

Required

• 7+ years of experience delivering AI, data, analytics, or digital transformation initiatives in industrial, engineering, energy, manufacturing, or service environments.
• Proven experience leading enterprise AI adoption programs from concept through deployment.
• Hands-on expertise with:
  • Large Language Models (LLMs)
  • Agentic AI frameworks
  • Retrieval-Augmented Generation (RAG)
  • Microsoft Copilot ecosystem
  • Azure AI Services
  • Data architecture and integration
  • AI governance and responsible AI practices
• Demonstrated ability to translate complex business challenges into scalable AI-enabled solutions.
• Experience leading multidisciplinary teams and influencing senior stakeholders.
• Strong understanding of business process transformation and productivity improvement methodologies.
• Experience working in global, matrixed organizations.

Preferred

• Experience within energy, utilities, industrial services, engineering services, or digital service businesses.
• Experience integrating AI with simulation tools, engineering applications, and digital service platforms.
• Familiarity with data lake architectures, digital twins, and industrial data ecosystems.