About the job 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.