VP, AI (Agentic Platforms & Transformation)

Job Description

Position Summary

We are building an applied AI function focused on transforming how work gets done across the enterprise through agentic systems, workflow redesign, and intelligent data integration.

We are seeking a VP, AI to lead this transformation end-to-end.

This role owns how AI is identified, evaluated, built, and scaled within the organization’s workflows, moving from fragmented experimentation to a structured, repeatable system that delivers measurable business impact. You will operate across platforms, define how agents interact with enterprise systems and data, and establish the operating model required to scale adoption.

This is a systems leadership role requiring a balance of strategy, product thinking, and execution. You will translate ambiguous business problems into deployable solutions, drive platform and architecture decisions, and build the frameworks that embed AI into day-to-day operations.

Key Responsibilities

AI Strategy & Business Impact

• Lead structured evaluation of AI platforms (e.g., Gemini, Claude, Perplexity), identifying strengths, limitations, and integration pathways
• Translate platform capabilities and constraints into clear prioritization for enterprise-wide adoption
• Identify and scale high impact use cases tied to measurable business outcomes

Agentic Platform & Workflow Ownership

• Design and scale multi-step, agent-driven workflows that automate and augment core business processes
• Translate complex, ambiguous workflows into structured, automatable systems
• Ensure AI is embedded into core operations, not deployed as isolated tools
• Establish reusable patterns to scale beyond one-off solutions
• Drive consistency across teams while maintaining speed and flexibility

Enterprise Data & Integration Strategy

• Define how third-party systems (SaaS platforms, databases, APIs) integrate into AI workflows
• Establish scalable ingestion and integration patterns working with Infrastructure and Architecture Leadership throughout the organization (APIs, connectors, BigQuery, MCP, etc.)
• Ensure data is structured, accessible, and governed for AI consumption

Adoption & Workflow Enablement

• Own adoption of AI-driven workflows across the organization
• Ensure all AI initiatives are tied to clear, quantifiable outcomes
• Drive initiatives from POC → production → sustained usage
• Define and track success metrics, including:

• Workflow adoption
• Time saved / efficiency gains
• Throughput and decision velocity
• Business impact (cost, revenue, productivity)

Measurement & Performance Ownership

• Define and track success metrics across all AI initiatives, including:
  • Decision velocity improvements
  • Productivity and output lift
• Establish baseline metrics prior to deployment and continuously measure post launch impact
• Ensure all AI solutions are tied to clear, quantifiable business outcomes

Workflow Transformation & Operating Model

• Redesign business processes to embed AI into daily operations
• Establish and refine frameworks for intake, prioritization, and scaling of AI initiatives
• Track engineering velocity, output, and impact across workstreams

Technical & Engineering Leadership

• Build and lead a high-performing team of AI engineers and integration specialists
• Establish standards for agent design, orchestration, and integration
• Ensure high-quality execution across POCs and production systems
• Partner with TPMs and engineering leadership to drive structured delivery

Cross-Functional Leadership

• Act as the bridge between business, engineering, and platform teams
• Present clear, opinionated recommendations to senior leadership
• Drive alignment and best practices across the enterprise
• Engage with external partners (e.g., Google) to accelerate innovation and influence roadmap direction

Qualifications & Experience

Required:

• 12+ years of experience across AI, engineering, data, or technical product leadership
• Proven track record of deploying AI-driven systems in enterprise environments
• Strong understanding of LLM/agent architectures, orchestration patterns (RAG, tool use, multi-agent systems), and API-driven integrations
• Experience making architecture decisions across AI platforms, including tradeoffs between RAG, direct API access, and hybrid approaches
• Experience translating ambiguous bus