VP, AI Transformation

Primary Responsibilities

• Lead Finance AI and Technology Transformation
• Serve as the senior technology partner to the CFO and Finance leadership team
• Develop and own a multi-year AI and technology transformation roadmap for Finance, aligned with Finance strategy, enterprise architecture, and business priorities
• Identify and prioritize high-value opportunities across FP&A, controllership, accounting operations, treasury, tax, procurement, financial reporting, and Finance shared services
• Modernize Finance workflows by combining AI, intelligent automation, data products, enterprise platforms, and process redesign
• Lead initiatives such as automated close and reconciliation, intelligent forecasting and scenario planning, management reporting, spend analytics, working-capital optimization, financial controls, and self-service decision support
• Ensure AI solutions integrate effectively with Finance platforms, data environments, and systems of record, including ERP, EPM, planning, reporting, procurement, and workflow platforms
• Partner with Finance, Internal Audit, Risk, Legal, Security, and Compliance to ensure solutions meet financial-control, regulatory, privacy, security, and auditability requirements
• Translate functional strategies and operating challenges into a prioritized portfolio of technology products and transformation programs
• Determine which functions and use cases receive dedicated delivery teams based on value, feasibility, data readiness, risk, and strategic importance
• Maintain an enterprise backlog and make transparent investment, sequencing, scaling, and stop decisions
• Own Technology Strategy and Architecture
• Define the target technology architecture for enterprise AI transformation in partnership with enterprise architecture, data, cloud, integration, security, and infrastructure leaders
• Establish reusable technology patterns for generative AI, machine learning, intelligent automation, workflow orchestration, APIs, enterprise search, retrieval-augmented generation, and AI agents
• Ensure solutions are built on secure, scalable, supportable enterprise platforms rather than disconnected proofs of concept
• Make build, buy, partner, and reuse decisions based on strategic differentiation, total cost of ownership, speed, risk, and long-term maintainability
• Drive interoperability and avoid unnecessary duplication across functions, vendors, models, and data products
• Establish technical standards for solution design, integration, testing, observability, resiliency, model performance, and production support
• Strengthen Data, Governance, and Controls
• Secure the data access, integration, governance, and quality pathways required to deliver transformation at enterprise scale
• Partner with data owners and technology teams to establish trusted, governed Finance data products for AI, analytics, reporting, and automation
• Ensure appropriate controls for data lineage, access, privacy, retention, segregation of duties, financial reporting, and model use
• Establish risk-tiering and governance processes that allow lower-risk use cases to move quickly while applying appropriate oversight to higher-risk applications
• Ensure AI outputs are explainable, traceable, monitored, and auditable where required
• Lead Technology Delivery and Product Management
• Establish a product-oriented operating model that brings together business product owners, product managers, architects, engineers, data scientists, designers, change leaders, and functional subject-matter experts
• Lead multidisciplinary delivery teams responsible for taking opportunities from discovery through architecture, build, deployment, adoption, and ongoing optimization
• Implement disciplined portfolio, product, and agile delivery practices while maintaining appropriate controls for enterprise technology programs
• Hold teams accountable for measurable adoption and realized value, not simply technical deployment
• Build sustainable ownership, support, and lifecycle-management models for solutions
• Build a Reusable Enterprise AI Capability
• Steward the flywheel that turns individual use-case learnings into reusable platform services, data products, architecture patterns, governance controls, and delivery accelerators
• Hold the organization accountable for reducing the marginal cost and time required to deliver each additional use case or functional transformation
• Create common capabilities for model access, prompt and agent management, knowledge retrieval, evaluation, monitoring, human review, security, and workflow integration
• Create mechanisms for sharing technology assets and delivery patterns across Finance and other corporate functions
• Develop clear criteria for moving solutions from experimentation to production and from function-specific implementations to enterprise services
• Build the Organization and Partner Ecosystem
• Set a high bar for hiring and talent-development for both technical leaders and individual contributors
• Develop solid relationships with Finance leaders, enterprise technology teams, and functional executives
• Manage the transition from partner- or consultancy-led delivery to a durable internal technology capability
• Select and manage strategic technology vendors, systems integrators, AI platform providers, and specialist partners
• Ensure external partners transfer knowledge, use enterprise standards, and contribute reusable assets rather than creating long-term dependency
• Develop workforce and sourcing plans that balance speed, specialized expertise, intellectual-property ownership, and operating cost
• Measure and Communicate Value
• Define and maintain the business case for the transformation portfolio, including technology investment, expected value, delivery risk, adoption, and ongoing operating cost
• Report portfolio performance, architecture decisions, risks, dependencies, and value realization to executive leadership
• Establish metrics for productivity, cycle time, cost, quality, forecast accuracy, control effectiveness, adoption, customer experience, and employee experience
• Make evidence-based recommendations about which solutions to scale, redesign, consolidate, or stop
• Ensure benefits are validated with Finance and other functional leaders and can be defended through transparent measurement

Required Qualifications

• 15+ years of experience in technology, engineering, data, product, enterprise applications, or digital transformation leadership
• Several years of experience leading other technology leaders, multidisciplinary teams, or a significant enterprise technology organization
• Demonstrated success serving as a technology leader or strategic technology partner to Finance and CFO organizations
• Experience delivering technology transformation across one or more Finance domains, such as FP&A, controllership, accounting, treasury, tax, procurement, financial reporting, or shared services
• Track record of leading enterprise AI, data, automation, ERP, EPM, or digital-platform programs with direct accountability for measurable business outcomes
• Experience translating Finance and business requirements into technology strategy, architecture, product roadmaps, and delivery plans
• Solid understanding of enterprise architecture, cloud platforms, data platforms, integration patterns, cybersecurity, identity, and software delivery
• Solid working knowledge of modern AI capabilities, including generative AI, large language models, AI agents, machine learning, retrieval-augmented generation, and intelligent automation
• Experience moving AI or digital products from experimentation into secure, governed, production-scale operations
• Experience evaluating build-versus-buy decisions and managing enterprise technology vendors and implementation partners
• Credibility with CFOs and Finance leaders, as well as CIOs, architects, engineers, data scientists, security leaders, and risk professionals
• Ability to communicate complex technology decisions clearly to senior executives and boards or executive committees