Digital Transformation & AI Adoption, AVP

Introduction

Enterprise Agentic AI adoption does not happen through tool deployment alone - it is a sustained organisational behaviour change that requires deliberate design, measurement, and iteration. The Digital Transformation & AI Adoption, AVP is the driving force behind Temasek's effort to move employees from AI awareness to embedded, productive AI habits at scale.

This role sits within the AI Automation & Fluency function and is responsible for designing and executing structured change programmes that embed new AI ways of working across Temasek's investment, operations, and support teams. In the agentic AI era, this includes building employee capability not just to build AI agents themselves, but to confidently delegate tasks to AI agents, design multi-step workflows, apply appropriate guardrails, and exercise oversight of autonomous AI processes.

This role will sit very close to actual business users, coaching them to adopt these behaviours in real workflows while helping measure usage, confidence, quality, and realised value over time. 

Responsibilities

Enterprise AI adoption leadership 

• Own end-to-end change management for major AI capability deployments - from initial stakeholder mapping and impact assessment through to sustained adoption tracking, workflow telemetry, and continuous improvement. 
• Design and execute adoption campaigns that move employee populations through awareness, activation, and embedded habit formation, with clear milestones, success metrics, and accountability at each stage. 
• Build and manage a network of AI champions embedded within business units - who serve as peer advocates and first-line enablement support. 
• Partner with senior leadership across Technology, Investment, and Operations to secure visible sponsorship for AI adoption initiatives and align change programmes with business unit priorities. 
• Lead cross-functional working groups to identify and remove systemic blockers to adoption - whether tool friction, policy gaps, workflow integration issues, cultural resistance, or insufficient enablement support. 

Change management depth 

• Apply structured change management methodologies adapted to the fast-moving context of enterprise AI deployment, where tooling, models, governance requirements, and use cases evolve rapidly. 
• Design role-based AI learning journeys tailored to distinct employee personas - investment professionals, operations staff, and functional specialists - reflecting the different AI use cases, risk profiles, workflow contexts, and oversight responsibilities each group faces. 
• Develop a cohesive internal communications strategy for AI fluency, including executive briefings, team-level messaging, intranet content, and showcase events that maintain momentum and surface success stories

• Build and operate a structured feedback collection mechanism that generates actionable insight on adoption barriers and programme effectiveness, feeding directly into programme iteration.
• In the agentic AI era specifically, design change programmes that address the new behaviours required: how to delegate to AI agents appropriately, how to review and verify agentic outputs, how to apply guardrails, and how to maintain meaningful human oversight as AI takes on more complex, multi-step tasks. 

Outcome-driven adoption measurement 

• 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 to generate actionable programme recommendations. 
• Run periodic AI fluency assessments across the organization, combining survey data, usage telemetry, and role-based capability benchmarks to identify cohorts requiring targeted support and demonstrate measurable capability uplift over time. 
• 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 value - supporting the Technology function's broader ROI narrative to the CTO and Board

Requirements

Experience and background 

• Degree in Engineering, Computer Science, or equivalent practical experience in digital transformation, enterprise technology adoption, or AI-enabled change programmes. 
• 7+ years of experience in enterprise technology adoption or change management. Prior experience as software engineer, solution architecture, data or technical product delivery will be of advantage.
• Demonstrated success driving large-scale AI or digital adoption programmes beyond pilots into sustained, measurable usage.
• Sufficient technical literacy to understand and credibly explain AI capabilities - including generative AI, agentic workflows, adoption analytics, and human-in-the-loop oversight - to non-technical audiences  

Skills and capabilities 

• Exceptional communication and storytelling ability: able to craft compelling narratives for audiences ranging from senior management to staff, making the value, mechanics, and safe use of AI adoption tangible and motivating. 
• Strong programme management discipline: able to run multiple concurrent change workstreams across different business units with clear milestones, stakeholder accountability, adoption metrics, and documented outcomes. 
• Data-driven and rigorous: comfortable designing measurement frameworks, interpreting adoption analytics and usage telemetry, and using data to make programme decisions rather than relying on anecdote. 
• Influential without authority: skilled at building trust and momentum across teams where you have no direct reporting line - the AI champion network, business unit heads, platform engineering peers, and control partners. 
• Resilient and iterative: enterprise AI adoption encounters resistance, setbacks, and shifting priorities; you approach these with curiosity rather than frustration, and adapt programmes based on feedback, adoption data, and evolving AI capabilities.