Associate, Enterprise AI Solutions

TKO; United States; Hybrid; USD 100,000 - 180,000; Full time

Job summary

TKO in New York City is seeking an Associate, Enterprise AI Solutions to build and ship verified AI workflows, agents, and skills that scale across WWE, UFC, IMG, PBR, On Location and corporate functions. This hands-on role focuses on delivering reusable, trusted artifacts that teams can adopt and extend.

You will own the end-to-end lifecycle of capabilities—from workflow extraction to validation—while coordinating with identity, connectors, and security teams to ensure robust, scalable

Qualifications

• Portfolio of AI agents, assistants or automations built and shipped into production.
• Ability to analyze real business workflows and translate into a technical solution.
• Hands-on fluency with enterprise AI platforms and integration patterns.

Responsibilities

• Build and ship verified cross-functional artifacts (agents, skills, products).
• Own artifacts as products with owners, adoption metrics, and versioning paths.
• Define quality bars, evaluation plans, and post-launch reviews for each capability.
• Diagnose and fix platform/agent issues across retrieval, permissions, and instructions.
• Maintain a live view of verified core and upcoming work; log weekly updates.

Skills

Agent-building

Workflow discovery

Automation design

Training & enablement

Pattern library

Tools

Glean

ChatGPT

Copilot

Claude

Agentforce

Job description

Title: Associate, Enterprise AI Solutions

Reports To: Senior Director, AI Products & Platforms

Location: NYC - Hybrid

The Role and What You'll Do

TKO is seeking an Associate, Enterprise AI Solutions to operate and improve the enterprise AI platform's agent-building experience and build the verified core of agents, skills, and other products that enable TKO to deliver AI-driven outcomes to our business units across WWE, UFC, IMG, PBR, On Location and TKO corporate. The role owns the quality, discoverability, and day-to-day improvement of the capabilities it builds, partnering with the appropriate technology owners on identity, connectors, security, and infrastructure changes.

This is a hands-on builder seat, not a coordinator role: the person will go into a team's actual workflow, understand how the work really gets done, identify what is common enough across TKO to be worth building as a verified capability, and then build and ship it — instruction sets, sources, actions, and evaluation — as the canonical version other teams adopt and extend.

This role does not exist to service one-off agent requests; it exists to find the workflows underneath those requests, decide which generalize, and produce a small number of verified artifacts the rest of the organization can build on top of.

The Associate, Enterprise AI Solutions will:

• Personally build and ship the verified cross-functional artifacts — agents, skills and other products — that encode a workflow shared across business units or corporate functions, including politically visible or technically awkward ones.
• Own those artifacts as products, not deliverables: each has a named business owner who takes responsibility for adoption, a standing feedback cadence, a stated scope, and a versioning and update path.
• For each verified capability, agree with the business owner on the workflow outcome, what the work costs today, the target behavior after launch, launch responsibilities, and how results will be reviewed. Use that agreement to drive the build and post-launch decisions, not as a substitute for building.
• Define and hold the quality bar before an agent goes live — top user intents covered, out-of-scope handling, human handoff path, success measures, and daily monitoring through the first two weeks — and design and run the evaluation (real-intent test set, success criteria, fallback/error rates) as the launch gate. Every build carries a documented baseline and both a business-impact metric and an adoption-quality metric, not just a launch checklist.
• Run post-launch review on every agent; use actual usage, user feedback and workflow outcomes to diagnose low adoption, document a hypothesis, and decide whether to iterate, retire, or re-promote it.
• Diagnose and fix failures at the platform and agent level, distinguishing issues with retrieval, permissions, instructions, source coverage, actions, and intent scope. Identify access, connector, source-quality, security, and support-path dependencies early; coordinate resolution with the responsible platform or IT owner and elevate genuine product gaps with a reproducible case. Keep the business team informed through resolution or a clear handoff.
• Go into the actual workflows across WWE, UFC, IMG, PBR, On Location and corporate functions — sitting with teams to understand steps, systems, handoffs and workarounds, not the summarized version in a request — and separate the underlying workflow from the stated ask.
• For each candidate workflow, separate the non-AI baseline (process redesign, integration, data quality, access) from the specific increment AI adds, and be willing to say a request needs foundations fixed first, or isn't an AI problem at all.
• Look at requests across business units and name the underlying capabilities worth building centrally versus what should be a template, pattern, or working session, or built by the team itself — sorting each into advance, validate first, fix foundations first, or decline.
• Maintain a live picture of workflow coverage across TKO and bring a recommendation on where to concentrate build effort.
• Build and maintain a pattern library (templates, worked examples, reference instruction sets) and run hands-on agent-building working sessions so teams leave with a shipped or near-shipped agent of their own.
• Own how agents are surfaced, named, described and ranked so the catalogue stays usable and the distinction between verified and experimental is clear.
• Design and deliver hands-on agent-building training for business unit users and champions, built from real workflows, then hand the framework to the change and enablement team for repeat delivery; produce day-one and role-based starting content for Wave 2 cohorts.
• Maintain one current view of the verified core and what's coming, and keep decisions traceable (what was built, what was declined and why, what was pushed back with a pattern).
• Issue a short weekly written update (what shipped, what's blocked, what decision is needed) and maintain a live action log for build commitments.

You Have These

• A demonstrable portfolio of AI agents, assistants or automations you personally built and shipped into production use by other people — the primary screen, weighted above everything else. Leading a deployment or coordinating builders, without personally building production capabilities, does not meet this requirement.
• Demonstrated ability to analyze a real business workflow and translate it into a technical solution by sitting with the team and identifying what should and should not be automated — including the judgment to call out when the real fix is a process or data change, not an agent.
• Deep hands-on fluency with at least one enterprise AI platform (Glean, ChatGPT, Copilot, Claude, Agentforce, or comparable), including prompt/instruction design, retrieval and permissions behavior, and tool/action wiring.
• Demonstrated ability to design and run your own evaluations — building test sets from real user intents, defining success criteria, and measuring fallback and error rates to decide launch readiness.
• Ability to diagnose agent failure at the platform level and distinguish retrieval, permissions, instruction, source-coverage and intent-scope causes.
• Experience teaching other people to build — training, enablement sessions, office hours, or a documented pattern library others adopted.
• Demonstrated judgment working across autonomous business units: finding the common capability underneath local requests, building once and reusing, and holding a defensible no.
• Evidence of building reusable, supported capability rather than volume — shared components, templates or platform assets other teams built on top of.

We'd Love If You Also Have These

• Track record owning adoption and usage outcomes for an enterprise tool, with measurable behavior change rather than deployment or attendance metrics.
• Experience serving as a hands-on technical partner to a business team from workflow discovery through launch and measurable adoption, including aligning on outcomes, managing expectations, and resolving difficult escalations.
• Business analysis, process mapping or solutions consulting background applied to technical delivery.
• Experience running a demand intake and prioritization mechanism as a published process across multiple business units.
• Experience standing up governance for AI systems — lifecycle gates, testing, guardrails, approval and retirement.
• Program management discipline: cadence design, action and risk logs, readiness gates.
• Experience troubleshooting enterprise platform dependencies such as SSO, connectors, access, and source coverage in partnership with infrastructure owners.
• Experience working alongside a strategic vendor relationship, including scope allocation and roadmap influence.
• Enablement or change management background, including champion networks and self-serve build programs.
• Multi-brand or federated operating environment, where each business unit has genuine autonomy and central functions must earn their mandate.
• Media, sports, entertainment or live events exposure (useful, not required).