Principal Product Manager, Enterprise AI Enablement

Procore Technologies•Austin, TX

9d•$198,240 - $272,580•Onsite

About The Position

We're looking for a Principal Product Manager, Enterprise AI Enablement to join Procore's IT Operations organization. In this role, you'll own how Procore's employees adopt and get value from AI across the knowledge worker stack — from platform strategy to governance to measurable productivity impact. The primary goal of this role is to turn AI enablement from a fragmented, business-unit-by-business-unit effort into a centralized, scalable capability with clear standards, adoption metrics, and ROI. As a Principal Product Manager, Enterprise AI Enablement, you'll partner with IT Operations leadership, Business Technology Engineering, Data/Engineering, and business unit stakeholders to define which AI tools and platforms Procore invests in, how they're rolled out, and how adoption is measured. Use your platform evaluation judgment, change management skills, and vendor/software governance experience to consolidate a currently decentralized AI tool landscape into a single accountable model. Procore is at an inflection point — replacing legacy vendor platforms with AI-native alternatives and building internal capability instead of buying it off the shelf — and this role owns that transition.

Requirements

Bachelor's degree in a related field, or equivalent practical experience
8+ years of experience in AI \ Technology Enablement
Demonstrated experience evaluating, selecting, or rolling out AI or automation platforms at enterprise scale
Experience partnering across engineering, data, and business stakeholders to drive adoption of a new technology or platform
Excellent executive communication skills — able to translate technical platform decisions into business-level tradeoffs for senior leadership
Comfortable operating in ambiguity and building structure where none currently exists

Responsibilities

Own the AI enablement roadmap for Procore's knowledge worker stack, including platform selection, rollout sequencing, and retirement of legacy tools
Define and enforce governance standards for AI tool intake, evaluation, and approval, closing the gap left by today's business-unit-by-business-unit ownership model
Partner with Business Technology Engineering on the handoff between enterprise AI enablement and agentic/SDLC workflows so responsibilities don't overlap or drop
Define and own a KPI framework for AI tools company-wide — adoption rate, time-to-value, cost-per-seat, and tool-consolidation savings — and report progress to the AI Steering Committee and executive stakeholders on a recurring cadence
Drive change management and training programs that get employees actually using approved AI tools, not just licensed for them
Evaluate new AI platforms and vendors [e.g., Gemini Enterprise vs. Claude Cowork-style bake-offs] against cost, security, and productivity criteria, and make build-vs-buy recommendations