Associate Vice President, Head of Enterprise AI Delivery

Job Description

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 — what is built, in what sequence, and against what definition of done — holding the pen on requirements, acceptance criteria and release priorities. Technical roadmap, architecture and engineering delivery remain with platform leadership.
• •Translate reengineered processes into platform requirements. Where a redesigned process depends on a capability that does not yet exist, ensure it enters the central roadmap with a business case and a named beneficiary attached.
• •Own the user experience (UX) and user interface (UI) standard for enterprise AI capabilities.
• •Lead strategic delivery and execution for the enterprise's most critical AI initiatives — the small number of programs where the outcome matters enough to warrant central accountability — from approved business case through to deployed, adopted and measured capability.
• •Ensure the right capabilities are built at the right quality, the right price and the right time: own the definition of done with the business, and the delivery schedule.
• •Deploy AI talent where the work actually is — embedding forward deployed engineers and process specialists directly into the divisional and functional teams delivering an initiative.
• •Direct the forward deployed engineering team to build working proofs of concept for reimagined, AI-first processes, with particular emphasis on agentic capabilities where process design and technical design cannot meaningfully be separated.
• •Maintain the boundary deliberately: production platform ownership, model operations and infrastructure remain with the technology organization.
• •Partner with Procurement and Technology leadership on build, buy and partner decisions, commercial strategy with software, model and cloud providers.

AI Delivery Execution:

• •Lead the AI delivery and process engineering function.
• •Organize and manage a federated delivery model between various AI engagement customers and the central supporting team.
• •Own the enterprise change and adoption strategy for AI, including stakeholder and readiness assessment, communications, champion networks and role-based enablement.
• •Define and report the adoption measurement framework — depth and quality of usage, not licenses deployed — and act on it.
• •Partner with L&D to build enterprise AI literacy, define new and changed roles, and integrate AI capability into skills taxonomy and job architecture.
• •Design and deliver enablement for senior leaders, recognizing that executive modeling is the single strongest determinant of adoption at scale.
• •Lead, develop and grow a global team of executives.

Education Requirement:

• •Bachelor's degree in business, technology, engineering, life sciences or a related discipline is required.
• •Advanced degree (Master of Business Administration or equivalent Master's) is preferred.

Required Experience and Skills:

15+ years of progressive experience in technology, transformation, operations or strategy within a large, complex enterprise, including at least 8 years of leadership experience; or an advanced degree with a minimum of 12 years of such experience including at least 6 years of leadership experience.

Demonstrated experience leading enterprise-scale business transformation with measurable, independently verifiable business outcomes.

Demonstrated track record translating emerging technology into operating model change and realized value — not pilots, proofs of concept or strategy documents alone.

Deep working knowledge of at least four of the following disciplines, and credible command of the rest: portfolio and delivery management, business process reengineering, enterprise product management, organizational change management, Center of Excellence design, and benefits or value realization.

Experience establishing and scaling new operating models across a global, matrixed organization.

Sufficient technical fluency in modern AI — including large language models, retrieval, evaluation methodol