Chief AI Officer / Head of AI — Insurance & Investments

Hiring company: TheHiveCareers Source: LinkedIn 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.