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Enterprise AI Strategy, Internal Enablement & Adoption

Automated Financial Systems (AFS) is accelerating its digital transformation, and AI is a critical driver of enterprise value creation. We are seeking a VP, AI Business Transformation to serve as the orchestrator of this transformation – translating strategy into execution while shaping the operating model, governance and prioritization that enable AI to scale effectively across the organization.

As AFS's first dedicated AI leader, this role operates as a strategic execution partner and system-level operator. You will work cross-functionally with teams such as Operations, Product, Technology, People, Go-To-Market functions to drive an integrated AI roadmap – ensuring initiatives are aligned, sequenced correctly, and delivering measurable value. You will also partner with executive leadership to shape AFS’s AI operating model, governance, and prioritization approach, translating strategic direction into scalable, cross-functional execution. The role combines strategic leadership, technical expertise, and cross-functional influence to accelerate AI adoption and business value across AFS.

The VP, AI Business Transformation is a member of the Management Committee and reports to the Chief of Staff and partners directly with the CEO and peers across Product, Engineering, Revenue, Operations, and Business Intelligence. The role operates as the enterprise owner of AI strategy, execution, and outcomes, ensuring alignment between AI initiatives and overall company value creation.
Scope of Responsibility and Authority

Own the enterprise AI transformation agenda by defining strategy, governance, operating models, and execution priorities; driving cross-functional delivery and responsible adoption; and ensuring AI initiatives generate measurable business value.
The role is empowered to define priorities, design systems, and recommend or select platforms, vendors, tools, and processes—while operating within AFS’s SOC/ISO/audit control environment. The VP partners closely with Risk and Compliance to ensure AI initiatives and operational decisions maintain fidelity to regulatory expectations, internal control requirements, and established governance programs.
The role is accountable for both building the initial AI capability directly and defining the structure, talent, and operating model required to scale the function over time.

Define and own AFS’s AI strategy and translate it into a clear, pragmatic AI enablement roadmap identifying priority use cases, sequencing, dependencies and value hypotheses.
Define AFS's LLM and AI platform strategy and lead the rollout of standardized AI tools (Claude, ChatGPT, Co-Pilot, etc.), frameworks, and usage practices across the enterprise.
Serve as a central coordinator across Legal, Compliance, Security, and Technology to ensure responsible and compliant AI adoption (FFIEC, SOC 2, and internal standards).
Own end-to-end execution of AI enablement initiatives – from concept through scaled deployment and lifecycle management. Some projects include: Conversion AI-Enablement, Engineering AI-Enablement, BD/Sales AI Transformation, Customer Support AI Transformation, etc.
Upskill engineering teams and drive adoption of AI tooling, workflows, and best practices. Embed AI into the engineering lifecycle to improve development velocity, quality, and efficiency.
Support enterprise change management by building alignment, momentum, and shared understanding across leaders and teams.
Act as a trusted advisor to executive leaders and board on AI-related decisions, trade-offs, investment priorities, and implications.
Manage key AI vendors and strategic partners, ensuring solutions deliver measurable business outcomes, align with enterprise architecture and governance standards, and support AFS's broader AI strategy.
Define and track success metrics across AI initiatives, including adoption, performance, risk indicators, and business impact.
Define and build the future AI team, including hiring strategy and organizational design.

Establish AI as a core capability embedded across AFS product and engineering workflows.
Enable and accelerate delivery of AI-enabled capabilities that generate measurable customer and business impact.
Drive meaningful improvement in engineering productivity and time-to-delivered-value.
Establish enterprise AI governance, standards, and operating mechanisms that enable AI to scale responsibly.
Build the operating model, talent strategy, and governance foundation required to scale AI across the enterprise.

Success in the first year is defined by measurable improvements in delivery speed, cost-to-serve, product differentiation, and operational rigor—while remaining compliant with AFS’s SOC/ISO/audit expectations.

• Establish a transparent KPI dashboard reviewed with the CEO/ELT at least monthly (delivery speed, quality, cost, adoption, and risk/compliance indicators).
• Reduce external services spend by 30% through internal capability build, platform rationalization, and automation.
• Improve time-to-delivered-value by 50% for targeted product/engineering workstreams (measured from intake to production release).
• Deliver 2–4 AI-enabled product capabilities into production (e.G. via the Conversion AI-Enablement program) with defined adoption and customer outcome metrics.
• Stand up an AI architecture and delivery foundation (e.g., RAG/agent patterns, evaluation harnesses, and reusable components) adopted by 3+ product or engineering teams.
• Implement AI governance and risk controls (model inventory, review/approval workflow, logging/monitoring, and periodic control testing) with zero critical audit findings attributable to AI implementations.
• Increase engineering AI-tooling adoption to 70%+ of engineers for approved use cases, supported by training, playbooks, and measured usage.

Outcome-oriented, focused on measurable business impact rather than activity.
Builder-operator mindset, combining strategy with hands-on execution.
Enterprise-oriented, operating across functions to drive alignment and adoption.
Highly accountable, taking ownership for both success and failure of AI initiatives.
Pragmatic and disciplined, balancing speed, quality, and risk in a regulated environment.

The ideal leader is a strategist, builder, and operator who thrives in ambiguity and turns AI opportunities into measurable business value. As AFS's first dedicated AI leader, they are comfortable setting direction while personally advancing critical work, often without a team behind them. They combine strong technical judgment with a practical understanding of AI systems and know how to move from concept to adoption.
They influence without authority, build trust quickly across functions, and partner effectively with product, engineering, and business leaders navigating transformation. Equally comfortable in the boardroom and the details of execution, they translate AI opportunities into clear business cases, drive enterprise readiness, and deliver measurable results.

We are seeking a hands-on builder-operator who can help AFS move quickly from AI experimentation to durable business value. More important than years of AI experience is a proven track record of designing, implementing, and scaling AI capabilities that drive adoption, productivity, and measurable outcomes.

• Demonstrated experience driving an organization to adopt a significant new technology or way of working, ideally including an AI adoption initiative in the last 1–2 years (transforming a workflow/business function).
• Track record partnering across Product, Design, and Engineering specifically.
  • Experience leading product and/or engineering initiatives in a PE-backed (or similarly high-velocity) environment with a clear orientation toward enterprise value creation.
  • Background across SaaS and AI-enabled product delivery at a similar scale/complexity (e.g., multi-team engineering org, enterprise customers, production-grade reliability expectations).
• Demonstrated ability to take AI from concept to production: problem selection, solution design, delivery, rollout, and measurement (e.g., LLM applications, RAG, agents/workflows, evaluation and monitoring).
• Strong technical judgment and architecture fluency; able to partner deeply with engineering while translating trade-offs into business outcomes.
• Experience evaluating and implementing build, buy, and partner strategies for emerging technologies, including vendor selection, platform assessments, and technology due diligence.
• Bias toward action: proven track record of delivering meaningful outcomes quickly, iterating in the open, and translating ambiguity into structure, priorities and action
• Comfort operating in a regulated / audited environment; able to work within SOC/ISO/audit constraints and partner effectively with Risk & Compliance to implement appropriate controls.
• Executive presence and communication skills to advise the CEO/ELT and engage credibly with the Board; able to align stakeholders and drive adoption across functions.

Equal Opportunity Employer
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