Director, Product Development, AI Enablement
About The Team
The Enterprise Data team is dedicated to maximizing the value of our data ecosystem, enabling real-time, intelligent experiences built upon a scalable and future-proof foundation. We are a highly collaborative, cross-functional organization. Our work involves harmonizing product, technology, design, risk, and other key partners to forge a modern data ecosystem. Within Enterprise Data, our team features one of the most highly-engaged, creative tech partnerships, experimenting with AI enablement tools, bringing product rigor to how we measure success, and cultivating a culture of relentless innovation and customer obsession.
About The Role
A Director of AI Enablement Product Management is a strategic hands-on product leader who will fundamentally rethink how the eData product organization - and ultimately Capital One - works with AI. You will act as the tip of the spear in driving enterprise-wide adoption of AI workflows, transforming our operations at every level: the tools we build, the processes we follow, and the mechanisms we use to measure success. Unlike traditional product managers who focus on deterministic software features, or AI-native product managers who train foundational models, an enablement product manager focuses on the operational layer: transforming AI capabilities, architecture blueprints, and developer platforms into scalable productivity tools and resilient infrastructure.
Responsibilities
- AI Platform Strategy: Define and execute a multi-year product roadmap for internal AI infrastructure tailored to enterprise operations.
- Intelligent Workflows: Architect and deploy autonomous AI agents and advanced RAG systems to transform high-friction processes.
- Blueprint & Standard Rollout: Create and deploy enterprise patterns, guardrails, and AI architectures across engineering and non-engineering functions for a faster, better quality outcome of the product development process, embedding responsible AI practices (transparency, explainability, compliance).
- Workflow Transformation: Replace manual toil and high-friction processes with reusable platform components and AI agents.
- Data Democratization & Integration: Build intuitive AI-driven semantic layers and integrate agentic AI/ML capabilities into core front-to-back workflows via flexible integration techniques (API, MCP, CLI).
- Governance, Compliance & Security: Establish ironclad LLM evaluation pipelines and guardrails that ensure strict data privacy, compliance, and zero-hallucination thresholds for reporting.
- Adoption & Change Management: Partner with internal champions and partners to ensure behavioral adoption rather than just feature deployment.
- Cross-Functional Synergy: Act as the primary strategic translator between core data engineering teams, corporate finance executives, legal teams, and business unit leaders.
- Feedback Loops: Serve as the structured bridge between business users and core infrastructure or engineering teams to refine future product strategy.