Lead AI Enablement at JD Power, driving adoption of AI tools across the enterprise. Chair AI Steering Committee and build internal tools for effective AI integration.
Responsibilities
Personally evaluate, prototype with, and drive decisions on the AI tools and platforms we adopt across the enterprise. This includes hands-on testing of agents, models, and orchestration layers.
Build the internal tools, dashboards, prototypes, and reference implementations that show what good AI adoption looks like in practice, rather than commissioning them.
Set and run the AI adoption strategy across the enterprise, including the metrics, milestones, and reporting cadence the executive team and board use to track progress.
Formalize and operate the AI Enablement Hub as a cross-functional capability. Operating model, intake, prioritization, decision rights, and the cadences that keep it running.
Build and lead a distributed Champions network embedded in business units. The Hub coordinates. Champions deliver inside their functions. You will not pull these roles into a centralized team.
Chair the AI Steering Committee and act as the senior point of coordination across CPTO, CDAO, CHRO, Legal, Risk, Communications, and business unit leaders.
Define and operationalize the JD Power AI governance framework. Policies, guardrails, and responsible-AI guidelines, written by someone who has built with the technology.
Partner with HR on AI skills, role evolution, and the workforce implications of AI productivity gains.
Build the business cases for AI investments and own value delivery across the portfolio.
Communicate AI strategy to internal and external audiences, including the board and our PE owner, in plain business language.
Requirements
IC readiness. The role is an individual contributor seat with executive sponsorship and a Champions network already engaged across the business. You should be energized by personally building and shipping rather than waiting to delegate.
Workforce enablement and cultural change. Demonstrated experience driving adoption of new technology or new ways of working across thousands of people, in environments where the business did not start out asking for it. This is the outcome we measure against.
Governance, responsible AI, and enterprise data. Working knowledge of responsible-AI practices, data classification, and how regulated data moves through AI systems, including the boundaries between what can be shared with hosted models, what stays in private inference, and what triggers Legal review. You should be able to write a usable policy, not just commission one.
Enterprise scale and cross-functional influence. Operating across business and technical functions without direct authority, including credibility with senior engineering leaders. The ability to land tough messages with executives and bring resistant business units along.
A relevant degree is a plus, not a requirement. Equivalent experience is weighted equally. Industry background is open. Financial services, professional services, B2B data, and tech are all relevant. What matters is that you have done this work in an environment with mature engineering and a thoughtful executive team.
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