VP, AI Transformation

UnitedHealth Group•Eden Prairie, MN

6d•$200,400 - $343,500•Remote

About The Position

The VP, AI Transformation will lead the design and delivery of enterprise AI transformation programs across Finance, LCRA, Marketing, People, Government Affairs, and other corporate functions. The initial priority will be Finance, partnering closely with the CFO organization to modernize core processes, data, platforms, and ways of working through AI, automation, and digital technology. This is a leadership role in our technology organization for someone who has successfully partnered with Finance (or other corporate function) executives and teams to deliver large-scale transformation. The ideal candidate has led technology, data, AI, or digital product organizations and understands how Finance operates across areas such as FP&A, controllership, accounting, treasury, tax, procurement, and financial reporting. You will own the technology strategy, transformation portfolio, and delivery model for corporate functions. You will work at the intersection of business leadership, enterprise technology, data, engineering, cybersecurity, risk, and external partners. You will be accountable not only for deploying AI solutions, but also for establishing the architecture, data foundations, governance, reusable platforms, and internal capabilities required to scale them safely and economically. Success will be measured by business outcomes: improvements in productivity, decision quality, forecast accuracy, control effectiveness, employee experience, speed, and cost—not by the number of pilots or technologies deployed.

Requirements

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
Demonstrated ability to navigate enterprise data access, data quality, governance, privacy, risk, and control requirements
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

Nice To Haves

Enterprise Finance platforms, including ERP, EPM, planning, consolidation, reporting, procurement, treasury, tax, and financial-close technologies
Modern cloud and data architectures, including data lakes or lakehouses, data warehouses, APIs, integration platforms, master data, metadata, and data governance
Generative AI platforms and patterns, including LLM gateways, RAG, enterprise search, agents, orchestration, evaluation, monitoring, and human-in-the-loop controls
Machine learning, analytics, business intelligence, process mining, workflow, robotic process automation, and intelligent document processing
Secure software engineering, DevSecOps, MLOps, LLMOps, testing, observability, reliability, and production-support practices
AI governance, model risk, privacy, cybersecurity, responsible AI, financial controls, and regulatory compliance
Product operating models, portfolio management, agile delivery, OKRs, value realization, and technology-finance management
Experience leading Finance technology, corporate systems, enterprise applications, data and analytics, or AI within a large global enterprise
Experience working in a regulated industry such as healthcare, financial services, insurance, or life sciences
Experience with large-scale ERP or Finance-platform modernization
Experience establishing or scaling an AI engineering, data-product, forward-deployed engineering, solutions-engineering, or internal-platform organization
Experience creating reusable enterprise AI services and reducing the cost and delivery time of subsequent use cases
Experience managing a transition from consultancy-led programs to internally owned technology products and capabilities
Familiarity with change management, operating-model redesign, and adoption programs for Finance and other corporate functions
Advanced degree in computer science, engineering, information systems, business, finance, or a related field

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
Build and Scale the Enterprise Transformation Portfolio
Own the portfolio of AI and technology transformation engagements across Finance, LCRA, Marketing, People, Government Affairs, and other corporate functions
Establish Finance as the initial transformation domain, then apply successful delivery patterns, platform capabilities, and governance models to additional functions
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
Ensure every initiative has a clear business owner, technology owner, value case, adoption plan, and measurable outcome
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
Partner with ERP, EPM, data-platform, and corporate-systems leaders to embed AI capabilities into existing workflows and platforms
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
Work with cybersecurity, privacy, legal, compliance, and enterprise-risk teams to operationalize responsible AI standards throughout the delivery lifecycle
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
Set the engineering and product-management expectations for quality, security, reuse, documentation, and production readiness
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
Ensure solutions transition into sustainable ownership, support, and lifecycle-management models
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
Build 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
Establish clear criteria for moving solutions from experimentation to production and from function-specific implementations to enterprise services
Develop the Organization and Partner Ecosystem
Build and lead a senior organization spanning technology strategy, product management, architecture, engineering, data, AI delivery, and transformation leadership
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
Establish 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