Group Head of AI

What matters most

• Set the enterprise AI roadmap

  Align AI strategy with business objectives, operational priorities, risk considerations and customer needs; prioritize high-ROI opportunities across underwriting, pricing, claims and operations.

• Build the AI organization and delivery capacity

  Attract, develop and retain AI talent while working with technology and data teams through dotted-line relationships to define requirements, infrastructure needs and delivery plans.

• Turn AI into measurable decisions

  Work with underwriting, claims and actuarial teams to embed insights in decisions, measure business impact, improve solutions and support adoption through change management.

• Keep AI aligned with risk and compliance

  Work with Legal, Risk and Compliance so initiatives fit enterprise frameworks, meet regulatory requirements and maintain model transparency.

• Evaluate relevant technology and vendors

  Assess emerging AI technologies, vendor solutions and market trends for their relevance to the (re)insurance business.

Likely challenges to think through

• Choosing value that can be delivered

  A likely trade-off is high potential ROI versus data readiness, infrastructure effort and risk across competing use cases. Prepare a prioritization approach that makes those factors explicit and defines how business impact would be measured.

• Coordinating without direct control

  Dotted-line technology and data relationships may make delivery coordination difficult while business teams must change how they use insights. Prepare an example of aligning owners, delivery plans and adoption measures across functions.

• Balancing innovation with oversight

  Emerging AI and vendor options may promise faster capability but complicate transparency and regulatory alignment. Prepare a framework for evaluating their business relevance alongside model explainability and risk review.

Topics to prepare

• Enterprise AI strategy and value

  Review how business objectives, operational priorities, risk and customer needs shape an AI roadmap. Suggested exercise: compare two hypothetical use cases on expected return and competitive advantage, stating your assumptions. The posting explicitly calls for an enterprise strategy and high-ROI opportunities.

• (Re)insurance decision workflows

  Review where underwriting, pricing, claims, operations and actuarial decisions could use AI insights. Suggested exercise: map one decision in each area and identify who would act on an insight. These functions are named in the posting; the maps are preparation examples, not stated projects.

• AI approaches and operating models

  Refresh the distinctions among machine learning, generative AI, automation and data-driven operating models. Suggested exercise: explain which approach might fit three hypothetical insurance tasks and when none would be appropriate. The posting requires a strong understanding of these areas.

• Data and infrastructure dependencies

  Review how data requirements and infrastructure choices affect the delivery of AI capabilities. Suggested exercise: sketch the dependencies and unresolved questions for a hypothetical claims use case. The role works with technology and data teams to define needs and delivery plans; no specific platform is prescribed.

• Risk, compliance and model transparency

  Review how an AI initiative can be assessed against enterprise risk and compliance frameworks while making its outputs understandable. Suggested exercise: list questions to resolve with Legal, Risk and Compliance before a hypothetical deployment. The posting explicitly assigns this alignment and requires model transparency.

• Adoption and AI organization design

  Review how leadership, talent development and change management support sustained use of AI insights. Suggested exercise: outline the roles and decision rights needed to move one initiative from delivery into business use. Building the AI organization and driving adoption are explicit responsibilities; this structure is a suggested exercise.

Skills to revise

• Prioritize a use-case portfolio

  Practice producing a one-page ranking of hypothetical underwriting, pricing, claims and operations opportunities, showing expected value, risk, dependencies and trade-offs. This rehearses the posting’s requirement to prioritize high-return opportunities with business leaders; the scoring method is a suggestion.

• Turn strategy into a delivery brief

  Practice drafting a short brief for one hypothetical AI capability: business decision, success measure, data and infrastructure questions, stakeholders and delivery milestones. This reflects the role’s work with technology and data teams, including through dotted-line relationships.

• Plan impact measurement and improvement

  Practice defining a baseline, an outcome measure and a review cadence for a hypothetical AI-assisted decision. State how feedback from underwriting, claims or actuarial teams would inform changes. The posting explicitly calls for measurable impact and continuous improvement.

• Lead a cross-functional adoption discussion

  Practice facilitating a mock discussion among business leadership, technology, operations and risk stakeholders about how an AI insight enters an existing decision. Summarize owners, objections and next steps. This reflects the posting’s emphasis on collaboration, alignment and change management.

• Evaluate an AI vendor proposal

  Practice writing a short assessment of a hypothetical vendor solution covering business fit, data and infrastructure needs, transparency, risk and expected value. Evaluating emerging technologies and vendors for relevance to (re)insurance is an explicit responsibility; this assessment format is a suggestion.

• Map evidence to the role

  Prepare a one-page map of actual examples against the posted leadership, technical-fluency and cross-functional requirements. Note the bachelor’s degree requirement and distinguish preferred regulated-industry experience and advanced education from required qualifications.

• Draft an illustrative AI roadmap

  Rank possible underwriting, pricing, claims and operations use cases by business value, feasibility, risk and customer impact. Label it as a proposed approach, not Everest’s existing plan.

• Rehearse an enterprise initiative walkthrough

  Prepare one real example covering the business objective, your leadership decisions, work with business and technology teams, adoption barriers and measured outcomes. Identify what you would do differently.

• Practice a technical design exercise

  For a hypothetical insurance use case, sketch the data, infrastructure and delivery needs; explain when ML, generative AI or automation fits, and how insights would enter a human decision workflow.

• Outline an AI organization plan

  Prepare a concise view of the capabilities you would build, how you would develop and retain talent, and how you would coordinate delivery with technology and data teams through dotted-line relationships.

• Show how you would drive adoption

  Describe a proposed rollout with underwriting, claims or actuarial partners, including change-management actions, business-impact measures and a feedback loop for improving the solution.

• Prepare a risk and vendor review

  Draft a sample review covering model transparency, regulatory and enterprise-risk alignment with Legal, Risk and Compliance, plus criteria for evaluating emerging AI technologies and vendors in (re)insurance.

• Bring executive-level questions

  Ask how Everest prioritizes AI opportunities, what data and infrastructure constraints matter most, how success is measured, and where the new leader would have decision authority across Global Operations & IT and business teams.