Digital Transformation & AI Adoption, AVP

What you'll do

• Own end-to-end change management for major AI capability deployments, from stakeholder mapping and impact assessment through to adoption tracking, workflow telemetry, and continuous improvement.
• Design and execute adoption campaigns that move employees through awareness, activation, and embedded habit formation, with clear milestones, success metrics, and accountability.
• Build and manage a network of AI champions embedded within business units to serve as peer advocates and first-line enablement support.
• Partner with senior leadership across Technology, Investment, and Operations to secure visible sponsorship and align change programmes with business unit priorities.
• Lead cross-functional working groups to identify and remove systemic blockers to adoption, including tool friction, policy gaps, workflow integration issues, cultural resistance, and insufficient enablement support.
• Apply structured change management methodologies adapted to fast-moving enterprise AI deployment where tooling, models, governance requirements, and use cases evolve rapidly.
• Design role-based AI learning journeys tailored to distinct employee personas such as investment professionals, operations staff, and functional specialists.
• Develop a cohesive internal communications strategy for AI fluency, including executive briefings, team-level messaging, intranet content, and showcase events.
• Build and operate a structured feedback collection mechanism that generates actionable insight on adoption barriers and programme effectiveness.
• Design change programmes that address new behaviours in the agentic AI era, including delegating to AI agents appropriately, reviewing and verifying agentic outputs, applying guardrails, and maintaining meaningful human oversight.
• Produce regular adoption dashboards and narrative reports for the Director of AI Fluency and senior technology leadership, analysing usage data, workflow signals, cohort patterns, and qualitative feedback.
• Run periodic AI fluency assessments across the organisation, combining survey data, usage telemetry, and role-based capability benchmarks.
• Build rapid, high-quality AI prototypes that allow users to test concepts against real workflows and provide early feedback.
• Develop the business case for continued AI fluency investment by connecting adoption data, productivity indicators, quality measures, and risk-management outcomes to tangible valu