Senior Director, Head of Services AI

Summary

The Head of Services AI will lead the AI-driven transformation of our 900-person Professional Services (PS) organization.

Job Description

Head of Services AI

This senior leader is responsible for defining and executing a strategy to embed Artificial Intelligence across service delivery, with the goal of dramatically improving efficiency, quality, and speed. The mandate is to achieve a 50–80% reduction in implementation time and effort within 24 months by leveraging AI tools, automation, and innovative practices. As the Head of AI, this role will oversee a small technical team dedicated to AI tooling and model tuning, while collaborating closely with Services delivery, engineering, and consulting teams to ensure AI adoption translates into real business results. This is a highly visible role that requires a blend of strategic vision, hands-on technical know-how, team leadership, and a strong focus on business outcomes.

Core Responsibilities

• AI Strategy & Roadmap: Develop and drive the overall strategy for integrating AI into Professional Services workflows from two standpoints: external services delivery and internal services operations. Identify high-impact areas in the implementation lifecycle (e.g. solution configuration, coding, testing, data migration, knowledge transfer) where AI and machine learning can streamline workflows, reduce costs, and improve quality. Likewise identify high-impact areas in the PS end-to-end business lifecycle (e.g. pipeline generation, solutioning, selling, contracting, staffing, billing, enabling staff, etc.) where AI and machine learning can streamline workflows, reduce costs, and improve quality. 

• AI Tooling & Model Integration: Select and implement the right AI tools and platforms (such as generative AI, machine learning models, NLP/NLG systems, and RPA) to automate or accelerate key services tasks. Oversee the fine-tuning of models to the P&C insurance domain and our product specifics, ensuring AI outputs are accurate and relevant. Evaluate build vs. buy for AI solutions and manage vendor relationships where applicable.

• Implementation of AI Solutions: Lead pilots and full-scale rollouts of AI-enabled processes. Work with delivery teams to embed AI tools into their day-to-day activities, such as using AI for project planning, risk identification, and knowledge management. Ensure that new AI solutions are integrated with existing systems and workflows for seamless adoption. 

• Team Leadership and Development: Build and manage a small, high-impact team of AI engineers, data scientists, and automation specialists. Provide technical guidance and set priorities for this team to develop and maintain AI tools and services. Foster a culture of innovation and experimentation, enabling the team to rapidly prototype solutions while maintaining a clear focus on business value. Mentor team members and promote continuous learning in AI/ML technologies.

• Cross-Functional Collaboration: Serve as the bridge between the PS organization and other departments (Engineering, Product Development, Data Analytics). Collaborate with product engineering to align on data access, APIs, and any product changes needed to support AI-driven services. Partner with delivery and consulting leaders to redesign processes around AI capabilities and ensure buy-in at all levels. Coordinate with the enterprise’s data and security teams to govern AI usage, addressing data privacy, model compliance, and ethical considerations in line with company policies and industry regulations.

• Change Management & Enablement: Champion AI adoption across the PS team. Develop training programs and materials to upskill consultants and project teams on new AI tools — making AI a “co-pilot” for every consultant. Establish an internal AI community of practice to share successes, use cases, and lessons learned. Drive change management efforts to shift the culture towards an “AI-first” mindset in service delivery, helping PS colleagues adapt and make this mindset shift.

• Performance Monitoring & Optimization: Define clear metrics to track the impact of AI initiatives on implementation projects. Monitor progress against the 50–80% efficiency improvement target, including metrics like reduction in average project delivery time, decrease in person-hours per project, automation rates for key tasks, and improvements in profit margin or capacity. Use these metrics to refine the AI strategy continuously. Troubleshoot issues with