AI Strategy, Productization & Engineering Transformation

Purpose of the Role

The VP, AI & Intelligent Products owns the definition, development, and scaling of artificial intelligence across Automated Financial Systems (AFS). This role is responsible for establishing AI as a core driver of product differentiation, engineering velocity, and enterprise value creation.

As AFS’s first dedicated AI leader, this role is accountable for building the AI capability from first principles while simultaneously delivering near-term product and productivity impact. The role combines strategic leadership, hands-on technical execution, and cross-functional influence to embed AI deeply into the company’s product and engineering operating model.

Position on the Executive Leadership Team

The VP, AI & Intelligent Products is a member of the Management Committee 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

The VP, AI & Intelligent Products owns the end-to-end AI function across AFS, including strategy, product integration, architecture, governance, and engineering enablement.

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.

Core Responsibilities

Define and own AFS’s AI strategy and translate it into a prioritized roadmap aligned to enterprise value creation.

Deliver AI-enabled product capabilities that create measurable customer value and differentiation.

Establish the company’s AI architecture, including LLM integration, RAG systems, agent frameworks, and MCP patterns.

Design and implement AI governance, risk management, and compliance frameworks aligned to FFIEC, SOC 2, and internal standards.

Embed AI into the engineering lifecycle to improve development velocity, quality, and efficiency.

Lead hands-on design and implementation of initial AI systems and features.

Upskill engineering teams and drive adoption of AI tooling, workflows, and best practices.

Define and track metrics tied to engineering productivity, product impact, and AI-enabled value creation.

Serve as the primary advisor on AI to the CEO, Executive Leadership Team, and Board.

Define and build the future AI team, including hiring strategy and organizational design.

Key Strategic Deliverables

Establish AI as a core capability embedded across AFS product and engineering workflows.

Deliver initial AI-enabled product features into production with measurable customer impact.

Drive meaningful improvement in engineering productivity and time-to-delivered-value.

Define and operationalize a scalable AI architecture and governance model.

Build a foundation for a dedicated AI organization that can scale with company growth.

First 12 Months Outcomes: What Success Looks Like

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.

·         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 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.

·         Establish a transparent KPI dashboard reviewed with the CEO/ELT at least monthly (delivery speed, quality, cost, adoption, and risk/compliance indicators).

Ways of Working

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.

Profile

The right leader for this role is equally a strategist, builder, and operator. They are comfortable defining direction in ambiguous environments while also personally executing critical work to establish credibility and momentum.

They demonstrate strong technical judgment, a deep understanding of AI systems and their practical applications, and the ability to translate those capabilities into business value.

They build trust quickly, influence without authority, and are effective partners to product and engineering teams working through transformation. They are comfortable engaging at the executive and board level while remaining grounded in implementation details.

Qualifications

We are looking for a practical builder-operator who can help a PE-backed business move quickly from AI experimentation to durable value creation. Prior AI experience is important, but we are not over-calibrating on years-in-seat; evidence of shipping and scaling real AI capabilities in production matters most.

·         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.

·         Bias toward action: proven track record of delivering meaningful outcomes quickly, iterating in the open, and driving decisions through ambiguity.

·         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.