AI Enablement Manager

• $10 billion+ generated by creators since Patreon's inception

• 25 million+ paid memberships on Patreon today.

We're continuing to invest heavily in building the best creator platform with the best team in the creator economy and are looking for an AI Enablement Manager to support our mission.

This role is based in New York or San Francisco and open to those who are able to be in-office 3 days per week on a hybrid work model.

About the Team

The IT team makes sure every Patreon employee has the tools they need to build and support our product. We sit alongside Workplace, People Operations, and Systems which gives us a view across every business function and the access to act on what we see. You’ll join a team that already administratively owns the AI stack, the identity and access layer, and the systems that connect them.

About the Role

Patreon runs on a modern AI stack (Claude, Zapier, Replit, Gemini, Databricks and more), and most of the company uses it daily. What we have not yet built is the connective tissue between the people who know their workflows best and the practices that turn those workflows into durable, governed tools.

This role owns that connection. You will work alongside teams across the company to redesign how work gets done, in two directions at once: building new workflows and tools with AI, and making existing workflows AI-enabled so that the systems people already rely on become faster and smarter. Some of that you will build yourself. Some of it will help enable non-eng teams to build. All of it you will ship with the same discipline necessary to maintain systems over time: version control, review, testing, staged rollout, monitoring and a clear ownership.

The scope is company-wide and the role is an individual contributor with real autonomy. You will set the roadmap, run intake, and be accountable for a portfolio of deliverables and the metrics behind them.

These deliverables support Patreon's push for operating leverage: a company where every function gets measurably more done as AI becomes part of how work happens, without trading away data integrity or trust. Rough time allocation is a guide, not a rule.

Partner with Engineering to enable non-Eng teams to build internal tools (about 25%)

• Operate as the standing bridge between business functions and Engineering. Write problem statements, MVP definitions, data contracts and success criteria that engineers can build to support non-eng tooling.

• Carry prototypes into internal production in partnership with Engineering: participate in design and code review for internal tools, and own the business side of launch and post-launch iteration

• Co-own, with Engineering and Security, the standards for how AI-enabled workflows touch production data: data classification, read and write permissions, connector and agent approval, audit logging, retention, and what must never leave a governed environment

• Help Engineering prioritize internal tooling work by bringing quantified business impact, clear scope and committed business owners to the table

Dissect workflows and co-build automation with their owners (about 30%)

• Map high-friction workflows end to end with the people who run them, and identify where AI removes steps, where it augments judgment, and where it should stay out

• Co-build with the owner using no-code and low-code platforms (Zapier, Claude, Replit, Google Workspace, Slack). The owner leaves able to run, monitor and extend what you built together

• Make existing systems AI-enabled where that beats building something new: layering agents, summarization, classification or routing onto tools teams already use

• Run a lightweight intake and prioritization process for requests, scored on impact, effort, data readiness and risk

• Carry light product and project management for each build: scope, milestones, stakeholder updates, launch, and a named owner afterward

• Maintain a shared library of reusable automations, prompts, agents and templates with clear ownership and discoverability

Raise AI literacy and develop power users (about 20%)

• Design and deliver role-based enablement for low-adoption users, built around their actual work rather than generic tool training

• Identify and develop at least one power user per function, with a defined path from first use to independent builder

• Publish company-wide skills, prompts and workflow templates, with a cadence for keeping them current as tools change

• Run office hours, demo days and show-and-tell sessions that surface what teams have built and what they need next

Measure adoption and report impact (about 10%)

• Own adoption metrics across the AI stack: active use by function, depth of use (templates reused, automations running, agents in production), and outcome impact (cycle time, ticket deflection)

• Extend the existing AI adoption dashboard and publish a monthly readout to leadership with recommendations on where to invest, simplify or stop

• Quantify the impact of individual builds so the portfolio can be ranked on evidence

Govern, assess and plan (about 15%)

• Own the tool cost and benefit view: usage against spend by platform, consolidation recommendations, renewal inputs

• Run a periodic assessment of 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 comms, training sequences and feedback loops

• Partner with Talent on AI fluency interviewing: contribute to rubrics, calibrate interviewers and help assess candidates for roles where AI fluency is a hiring criterion

What you’ll need

• 6+ years in roles such as business systems, internal tools, technical program management, solutions engineering, operations or enablement, with a track record of building things other people adopted

• Hands-on fluency with current AI tools and no-code and low-code automation platforms (for example Zapier, Make, n8n, Claude, ChatGPT Enterprise, Replit or similar). You have built working automations and agents for real business processes, and you can show them

• Working familiarity with how software is deployed: version control, code review, testing, environments, staged releases, monitoring and rollback. You do not need to write production software, but you can read and lightly modify code (Python, SQL, JavaScript), work with APIs, and hold your own in a design review

• Experience translating between business stakeholders and engineers: writing requirements, defining MVPs, negotiating scope

• Analytical skill to define instrumentation, pull usage data (SQL or spreadsheets), and present a clear "so what" 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: data classification, permissions, vendor evaluation, risk review

• Ability to set your own priorities in an ambiguous space, run multiple workstreams, and keep stakeholders informed across teams

Bonus if you have:

• Experience working closely with cross-functional partners in roles that are typically non-technical, so you understand how their work actually flows and where the friction lives

• 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

• Put Creators First | They’re the reason we’re here. When creators win, we win.

Patreon operates under a hybrid work model, where employees based in office locations are expected to come into the office three days per week, excluding sick time and paid leave. The goal of this policy is to be intentional about the in-person time we spend together to strengthen the feeling of community at Patreon. Candidates hired into remote-eligible roles are not expected to meet the same requirements.