Business Director, AI Strategy & Transformation – Enterprise Risk Management

Overview

The Director of AI Strategy & Transformation for Enterprise Risk Management is a strategic leadership role focused on modernizing enterprise risk management through technology and data-driven insights. This leader will set the long‑term vision and strategy for integrating Artificial Intelligence, Machine Learning, advanced language models, and transformational data analytics into ERM processes. The role oversees two complementary teams: an AI Strategy team and a dedicated Data & Analytics Transformation team, distinct from the existing ERM Data, Analytics, and Reporting function.

Acting as a bridge between risk professionals, product, and technology teams, the Director will synergize these efforts—using D&A to uncover opportunities and build the case for change, and the AI team to automate modernized processes to increase the efficiency and effectiveness of the risk function.

Data & Analytics for Risk Transformation

Lead and develop a specialized Data & Analytics team focused exclusively on transformational initiatives, operating distinctly from the existing Business As Usual (BAU) data team. Direct hypothesis‑driven data exploration to identify where and how risk management programs, processes, and methodologies should evolve to drive value, time savings, and enhanced risk oversight. Foster a complementary ecosystem between D&A and AI: leverage the D&A team to identify bottlenecks and build the business case for process changes, while deploying the AI team to automate and optimize those modernized processes via machine learning.

Use data to continuously measure the impact of transformational changes, define the “destination state” of our risk capabilities, and guide the strategic roadmap.

Strategy Development & Execution

Formulate a multi‑year AI vision and strategy for ERM that sets a clear long‑term direction for modernizing enterprise risk management while remaining aligned with the overall enterprise strategy. Design a balanced implementation roadmap that delivers immediate quick wins, such as automating highly manual tasks and processes to prove value early while simultaneously building toward long‑term, foundational AI capabilities. Establish a clear prioritization framework to evaluate potential AI use cases based on their complexity, speed to implementation, and long‑term impact on effectiveness and efficiency.

Secure executive alignment through clear business cases that balance improving operational efficiency with the strategic value of long‑term risk reduction. Define the data, technology, and change‑management plans required to sustainably support AI tools over time, ensuring the team transitions smoothly from legacy processes to AI‑assisted workflows.

AI Implementation & Internal Tool Development

Identify and prioritize high‑impact opportunities to use AI, Machine Learning, and Large Language Models to improve the speed, accuracy, and efficiency of enterprise risk management processes. Partner directly with Product, Tech, and Data teams to build, test, and deploy custom internal AI tools specifically tailored for risk managers and compliance officers. Review successful AI projects and tools used in other parts of the company (such as first line risk offices) and adapt those proven technologies for use within the ERM framework.

Enhance efficiency with advanced models like Gemini and Claude in day‑to‑day risk management workflows to automate highly manual tasks in execution of enterprise risk processes. Build automated workflows that use AI to parse and analyze large volumes of unstructured data. Layer AI capabilities on top of existing risk management systems to extract better insights, flag operational anomalies, and reduce manual data entry for the team.

Team Adoption & Value Tracking

Lead the practical training and upskilling of the ERM team, ensuring risk professionals know how to effectively use, prompt, and validate the outputs of new AI tools. Define and track clear metrics to measure the success of AI adoption, focusing on time saved, reduced manual errors, and earlier detection of risks.

• Experience:
  
  4+ years of quantitative analysis, 4+…