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AI Enablement Manager

Enterprise ApplicationsPosted on Nov 13
Full-time$175k - $262.5k /yr

About the Job

Requirements

  • At least 6 years of experience in business systems, internal tools, technical program management, solutions engineering, operations, or enablement, with a track record of building solutions adopted by others.
  • Hands-on fluency with current AI tools and no-code or low-code automation platforms, such as Zapier, Make, n8n, Claude, ChatGPT Enterprise, or Replit; must have built working automations and agents for real business processes and be able 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 Python, SQL, or JavaScript, work with APIs, and participate in design reviews.
  • Experience translating between business stakeholders and engineers by writing requirements, defining minimum viable products, and negotiating scope.
  • Analytical ability to define instrumentation, retrieve usage data using SQL or spreadsheets, and present findings and recommendations to leadership.
  • Experience designing learning or enablement programs that changed behavior, supported by 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, run multiple workstreams, and keep stakeholders informed across teams.

Responsibilities

  • Partner with Engineering as the standing bridge between business functions and Engineering; write problem statements, minimum viable product definitions, data contracts, and success criteria for internal tools supporting non-engineering teams.
  • 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 the standards for AI-enabled workflows touching production data, including data classification, read and write permissions, connector and agent approval, audit logging, retention, and restrictions on leaving governed environments.
  • Help Engineering prioritize internal tooling by providing quantified business impact, clear scope, and committed business owners.
  • Map high-friction workflows end to end with their users 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, including Zapier, Claude, Replit, Google Workspace, and Slack; ensure owners can run, monitor, and extend the solutions.
  • Make existing systems AI-enabled when preferable to building new systems by adding agents, summarization, classification, or routing to tools teams already use.
  • Run an intake and prioritization process for requests, scoring impact, effort, data readiness, and risk.
  • Manage scope, milestones, stakeholder updates, launch, and post-launch ownership for each build.
  • Maintain a shared library of reusable automations, prompts, agents, and templates with clear ownership and discoverability.
  • Design and deliver role-based AI enablement for low-adoption users, based on 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, and maintain them as tools change.
  • Run office hours, demo days, and show-and-tell sessions to surface what teams have built and what they need next.
  • Own AI-stack adoption metrics, including active use by function, depth of use such as reused templates and automations or agents in production, and outcomes such as cycle time and ticket deflection.
  • Extend the existing AI adoption dashboard and publish a monthly leadership readout with recommendations on where to invest, simplify, or stop.
  • Quantify the impact of individual builds so the portfolio can be ranked based on evidence.
  • Own the tool cost and benefit view, including usage against spend by platform, consolidation recommendations, and renewal inputs.
  • Run periodic AI-maturity assessments 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 for roles where AI fluency is a hiring criterion.

Desired Qualifications

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

About the Company

Patreon

Membership platform connecting creators with patrons

Patreon is a membership platform that helps creators earn a steady income by connecting them directly with their most devoted fans. Creators set up membership tiers offering exclusive content and perks, and fans pay a monthly fee to access them. The platform handles payments, provides creator tools and integrations to manage memberships, and takes a percentage of earnings as its revenue. This model lets creators rely on recurring support rather than ad-based or per-view income, and it supports a wide range of creators, including writers, musicians, visual artists, and podcasters. Patreon’s goal is to give independent creators financial stability and a closer, ongoing connection with their audience.

Company size
1,001-5,000
Company stage
Series F
Total funding
$352.1M
Headquarters
San Francisco, CA
Founded
2013

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