Chief AI Officer / Head of AI — Insurance & Investments

Job Summary

The Chief AI Officer / Head of AI is responsible for developing and executing the organization's enterprise AI strategy, establishing AI governance, identifying high-value AI use cases, and scaling artificial intelligence across business and operational functions.

Within Insurance & Investments, the role applies Generative AI, Machine Learning, predictive analytics, intelligent automation, NLP, computer vision, and AI agents to areas such as underwriting, claims, fraud detection, customer service, investment research, portfolio management, risk, compliance, operations, and financial analysis.

The role bridges business strategy, AI technology, data, innovation, risk, and commercialization, ensuring AI initiatives generate measurable business value while meeting regulatory, security, privacy, and responsible-AI requirements.

Key Responsibilities

1. Enterprise AI Strategy

Develop and execute the organization's enterprise AI strategy and roadmap.

Establish AI priorities aligned with corporate and business-unit objectives.

Identify and prioritize high-value AI opportunities.

Build an AI use-case portfolio across insurance, investments, operations, and customer functions.

Develop AI investment cases, budgets, and ROI frameworks.

Define AI adoption, productivity, revenue, and business-value KPIs.

Advise executive leadership and the Board on AI opportunities and risks.

2. Generative AI & AI Agents

Develop enterprise strategy for Generative AI and AI agents.

Identify use cases for LLMs, copilots, intelligent assistants, and agentic workflows.

Establish enterprise standards for LLM usage.

Evaluate foundation models, AI platforms, and vendor solutions.

Lead development of secure internal AI assistants and knowledge systems.

Establish controls around hallucination, model quality, data leakage, prompt security, and AI reliability.

3. Insurance AI

Lead AI initiatives across:

Automated underwriting

Risk assessment and pricing

Claims automation

Claims triage

Fraud detection

Customer-service automation

Document and policy analysis

Predictive customer analytics

Churn and retention prediction

Personalized insurance products

Agent/broker support

Computer vision for claims assessment

NLP for policy and claims documents

4. Investment & Wealth AI

Apply AI across:

Investment research

Market and financial analysis

Portfolio analytics

Risk modeling

Investment decision support

Algorithmic and quantitative strategies

Investor personalization

Wealth-management assistants

Client communications

Financial document analysis

Alternative-data analysis

Portfolio monitoring

Regulatory and investment reporting

5. AI / Machine Learning Platform

Establish scalable enterprise AI/ML architecture.

Oversee AI platforms, model infrastructure, APIs, and deployment environments.

Partner with Technology and Engineering on MLOps/LLMOps.

Establish model development, testing, deployment, monitoring, and retirement processes.

Support cloud-based AI infrastructure and GPU/compute requirements.

Promote reusable AI capabilities and enterprise AI platforms.

Ensure integration with existing data and technology ecosystems.

6. Data & AI Collaboration

Partner with the Chief Data Officer / Head of Data on data strategy.

Ensure AI initiatives have access to high-quality, governed data.

Establish data pipelines and feature/data platforms required for AI.

Promote responsible use of internal and external data.

Collaborate with Data Science, Analytics, Engineering, and BI teams.

7. AI Governance & Responsible AI

Establish an enterprise AI governance framework.

Define AI policies, standards, controls, and approval processes.

Address model risk, bias, explainability, transparency, privacy, and security.

Establish AI risk assessments and model validation processes.

Work closely with Risk, Compliance, Legal, Information Security, and Internal Audit.

Ensure AI solutions comply with applicable financial-services regulations.

Establish responsible AI principles and monitoring mechanisms.

8. AI Innovation & Use-Case Development

Establish AI labs, innovation programs, or AI Centers of Excellence.

Run AI proof-of-concepts, pilots, and MVPs.

Evaluate emerging AI technologies and vendors.

Develop AI prototypes and scale successful solutions.

Establish an ideation PoC pilot production scale framework.

Track AI initiatives and eliminate low-value projects.

9. AI Commercialization

Identify opportunities for AI-enabled products and services.

Develop new AI-powered customer and investor propositions.

Explore AI-based revenue streams.

Support AI-enabled personalization and digital experiences.

Measure AI's impact on revenue, customer experience, productivity, and cost reduction.

10. AI Vendor & Ecosystem Management

Evaluate AI vendors, LLM providers, cloud providers, and specialist AI companies.

Develop strategic relationships with technology providers and AI startups.

Manage AI technology contracts and commercial relationships.

Monitor vendor model performance, security, privacy, and operational risks.

11. Leadership & Capability Building

Build and lead AI, ML, data science, and AI engineering teams.

Establish enterprise AI Centers of Excellence where appropriate.

Develop AI skills across the broader organization.

Lead AI training and adoption programs.

Establish AI communities of practice.

Recruit and retain specialized AI talent.

Insurance & Investments AI Priorities

Area AI Applications

Underwriting Risk scoring, document analysis, predictive models

Claims Claims triage, automation, fraud detection, computer vision

Customer Service AI assistants, chatbots, agentic workflows

Pricing Predictive pricing and risk models

Fraud ML-based anomaly and fraud detection

Investment Research NLP, document analysis, research copilots

Portfolio Management Predictive analytics, risk and portfolio insights

Wealth Management Personalized recommendations and AI assistants

Operations Intelligent automation and AI agents

Compliance Regulatory monitoring, document review, surveillance

Risk Predictive risk analytics and scenario modeling

Finance Forecasting, reconciliation, financial analysis

Digital Personalization, recommendation engines, AI-powered UX

Ideal Candidate Profile

12–18+ years in AI, Machine Learning, Data Science, Analytics, Technology, or Digital Transformation.

5+ years in AI/ML leadership with enterprise-scale responsibility.

Proven experience developing and executing an enterprise AI strategy.

Strong hands-on understanding of:

Generative AI

LLMs

AI Agents / Agentic AI

Machine Learning

Deep Learning

NLP

Predictive Analytics

Computer Vision

MLOps / LLMOps

AI Governance

Responsible AI

Experience taking AI initiatives from PoC pilot production enterprise scale.

Financial-services experience strongly preferred.

Experience in insurance, banking, investment management, asset management, wealth management, or FinTech advantageous.

Strong understanding of data architecture, cloud platforms, APIs, cybersecurity, privacy, and model risk.

Strong executive communication and commercial/business-acumen skills.

Bachelor's/master's degree in Computer Science, AI, Machine Learning, Data Science, Engineering, Mathematics, Statistics, or related field; PhD advantageous for research-heavy environments.