Manager Intelligent Automation Jobs in Rosemount, MN

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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.

You'll enjoy the flexibility to work remotely * from anywhere within the U.S. as you take on some tough challenges.

For all hires in the Minneapolis or Washington, D.C. area, you will be required to work in the office a minimum of four days per week.

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

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

You'll be rewarded and recognized for your performance in an environment that will challenge you and give you clear direction on what it takes to succeed in your role as well as provide development for other roles you may be interested in.

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 
• 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
   

The strongest candidates will have experience across several of the following areas:

• 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

Preferred Qualifications:

• 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 a...