Associate Vice President, Head of Enterprise AI Delivery

The Associate Vice President, Head of Enterprise AI Delivery leads the business-facing capability that closes that gap. Reporting to the Chief AI Officer, this leader owns the non-engineering disciplines required to convert enterprise AI strategy into delivered, adopted and measured outcomes, organized into four functions: AI portfolio and value; process reengineering and operating model; enterprise AI products and delivery; and the AI Center of Excellence and adoption. This role is the connective tissue between our technology organization and the divisions it serves. The successful candidate will be equally credible in a technical design review and in a divisional leadership meeting, and will be measured not on what is built but on what changes as a result. AI Portfolio & Value: Facilitate the enterprise AI demand intake, triage and prioritization process, ensuring investment concentrates on the highest value opportunities. Establish and run the delivery operating model for enterprise AI initiatives, including stage-gating, funding checkpoints, dependency management and delivery assurance. Maintain a single enterprise view of the AI portfolio — scope, status, investment, risk and expected value — and provide the reporting that supports executive and Board-level governance. Partner with Division leadership and the enterprise strategy to align AI investment with the enterprise strategic plan and annual planning cycle. Own the business case standard for enterprise AI investment, including baselining, benefit definition and the evidence required at each stage gate. Establish and run post-implementation value measurement in partnership with Finance, including agreed treatment of productivity, cycle-time, quality and cost benefits. Report realized value to executive governance with rigor and candour, including initiatives that have not delivered. Collaborate with various business-side counterparts in Legal, Privacy, Quality and Compliance in the operation of the responsible AI framework, including use-case risk tiering and intake triage. Process Reengineering & Operating Model: Establish process and task mining as standard practice to document the actual as-is state, and build the internal capability to run it. Own quantitative baselining of every process entering the portfolio — cycle time, cost per transaction, touch points, handoffs, rework and defect rates — agreed with Finance at the outset, and maintain a prioritized portfolio sequenced on value, feasibility and risk. Lead task-level decomposition of priority processes, classifying each task as automatable, augmentable, irreducibly human on judgment or accountability grounds. Drive clean-sheet, AI-first redesign that starts from the required outcome, challenging steps that exist because of current capacity limits. Own control redesign as a first-class deliverable, reengineering the associated control. Conduct regulatory and validation impact assessment for every redesign touching a regulated process, covering Good Practice (GxP) requirements, computer system validation, audit trail and data integrity obligations, change control, and ongoing validation after release. Define the target operating model for each redesigned process — roles, decision rights, spans and layers, and where work sits — with the accountable business owner. Design the transition alongside the end state, covering sequencing, parallel running and its cost, cutover criteria and fallback arrangements. Own sustainment, including periodic re-baselining as technology and data availability advances. Enterprise AI Demand & Delivery: Serve as aggregation point for business needs across the enterprise AI estate — GPTeal, the Gemini Enterprise Agentic platform and the capabilities built on them — converting fragmented divisional requests into one prioritized, de-duplicated view of what the enterprise genuinely needs built. Own the business-side roadmap for central AI capabilities —