AI Enablement Lead

Patreon•San Francisco, CA

1d•$175,000 - $262,500•Hybrid

About The Position

Patreon is hiring an AI Enablement Manager to help the company make AI a durable, governed part of how work gets done. The role connects employees who understand their workflows with practices and systems for building reliable AI-enabled tools. It is a company-wide individual-contributor position with autonomy to set the roadmap, manage intake, and own a portfolio of deliverables and their impact metrics. The role is based in New York or San Francisco and follows a hybrid model requiring three days in the office per week for employees based there. The AI Enablement Manager will work with business teams to redesign workflows, build new AI-enabled tools, and improve existing systems. They will partner with Engineering and Security on internal tools and production-data standards, and will co-build automations with workflow owners using no-code and low-code platforms. The role also develops AI literacy and power users, measures adoption and outcomes, manages tool costs and benefits, assesses AI maturity, plans enablement, and supports change management and AI-fluency hiring practices. Deliverables are expected to follow disciplined practices such as version control, review, testing, staged rollout, monitoring, and clear ownership, while protecting data integrity and trust.

Requirements

At least 6 years of experience in business systems, internal tools, technical program management, solutions engineering, operations, enablement, or a similar role, with a track record of building things others adopted.
Hands-on fluency with current AI tools and no-code or low-code automation platforms; experience building working automations and agents for real business processes and ability to demonstrate them.
Working familiarity with software deployment practices, including version control, code review, testing, environments, staged releases, monitoring, and rollback.
Ability to read and lightly modify code such as Python, SQL, or JavaScript, work with APIs, and participate effectively in design reviews; production software development is not required.
Experience translating between business stakeholders and engineers, including writing requirements, defining MVPs, and negotiating scope.
Analytical skills to define instrumentation, retrieve usage data using SQL or spreadsheets, and communicate conclusions and implications to leadership.
Experience designing learning or enablement programs that changed behavior, with evidence beyond attendance.
Familiarity with data governance and responsible AI practices in a business context, including data classification, permissions, vendor evaluation, and risk review.
Ability to set priorities in an ambiguous environment, manage multiple workstreams, and keep stakeholders across teams informed.

Nice To Haves

Experience working closely with cross-functional partners in typically non-technical roles and understanding their workflows and sources of friction.
Experience with Databricks, Workday, Ashby, Lattice, or similar enterprise systems.
Experience building champions networks or power-user programs.
Prior work on hiring rubrics or interview design.

Responsibilities

Partner with Engineering to translate business needs into problem statements, MVP definitions, data contracts, and success criteria for internal tools.
Carry prototypes into internal production with Engineering, participate in design and code reviews, and own the business side of launch and post-launch iteration.
Co-own with Engineering and Security standards for AI-enabled workflows that touch production data, including data classification, permissions, connector and agent approval, audit logging, retention, and governed-environment requirements.
Help Engineering prioritize internal tooling using quantified business impact, clear scope, and committed business owners.
Map high-friction workflows with the people who run them and identify where AI can remove steps, augment judgment, or should not be used.
Co-build automations with workflow owners using no-code and low-code platforms, and enable owners to run, monitor, and extend the resulting tools.
Make existing systems AI-enabled when that is preferable to building new tools, including by adding agents, summarization, classification, or routing.
Run an intake and prioritization process for requests, scoring them on impact, effort, data readiness, and risk.
Manage the scope, milestones, stakeholder updates, launch, and ongoing ownership for individual builds.
Maintain a discoverable shared library of reusable automations, prompts, agents, and templates, with clear ownership.
Design and deliver role-based AI enablement for low-adoption users, based on their actual work.
Identify and develop at least one power user per function, with a path from first use to independent building.
Publish company-wide skills, prompts, and workflow templates, and keep them current as tools change.
Run office hours, demo days, and show-and-tell sessions to surface team work and needs.
Own AI-stack adoption metrics, including active use by function, depth of use, and outcome impact.
Extend the existing AI adoption dashboard and publish monthly leadership readouts with recommendations on investment, simplification, or discontinuation.
Quantify the impact of individual builds to rank the portfolio using evidence.
Manage the view of AI tool costs and benefits, including usage against spend, consolidation recommendations, and renewal inputs.
Periodically assess AI maturity by function and maintain a rolling 6-to-12-month roadmap for enablement and internal tools.
Design and apply change management for new tools and workflow changes, including communications, training sequences, and feedback loops.
Partner with Talent on AI-fluency interviewing by contributing to rubrics, calibrating interviewers, and helping assess candidates where AI fluency is a hiring criterion.