Context
Sports ticket operations still run on a lot of manual work. Pricing a slate of events, moving inventory out of holds, and deciding who to call next are weekday jobs. Jump’s public platform is a fan-experience and ticketing system of record for sports teams: primary and resale ticketing, dynamic pricing, yield, fan journeys, and the data underneath them.
In February 2026 Jump announced an agentic AI suite in the open. The marketing claim is a move past dashboards and insights, into execution inside that system of record. Public launch coverage names Denver Summit FC, North Carolina Courage, and the Minnesota Timberwolves and Lynx. That list is the company’s launch story. It is not a personal client roster.
Problem
An insight does not change tonight’s inventory. The public problem is execution with control: teams need the agent to do the work, and they need a person to preview it before anything goes live. The pattern Jump markets is stage, preview, confirm.
Approach
These are public product names, paraphrased from Jump’s open pages. They are industry context. They are not a claim that I am the named author of each agent.
- Pricing Manager. An agent for ticket ops that models scenarios and executes pricing changes within an event and across events. The public loop is forecast, update, detect outliers, and validate with a stage-preview-confirm step.
- Inventory Manager. An agent that reallocates inventory inside the workflow. The public example is moving holds to available without clicking a seat map one section at a time.
- Fan Intelligence. An AI overview for sales: purchase history, resale and transfer, attendance, and upgrades redeemed, so outreach can be prioritized.
- Campaign Generator. Marketed as coming soon. An agentic path from an idea to a launched campaign.
On the resume, my scope is the engagement around this kind of work. I own the full lifecycle for a portfolio of about ten enterprise clients: go-to-market and C-suite alignment, then execution, governance, and value realization. I write PRDs for enterprise AI agents, run customer discovery, and influence the roadmap. Delivery sits with cross-functional squads across AI engineering, data science, and design. Responsible AI governance is part of the engagement, including human review when a person has to own the decision.
Earlier in 2023 I led Project Mosaic, the requirements and domain foundation for the enterprise product. That is its own case study. It is the foundation later product work could stand on, not a claim that one project became the whole roadmap.
Outcome
Resume-cleared
45%
Reduction in client operational workflows
Resume-cleared
$26M
Series A the agent proof point contributed to
Resume-cleared
~10
Enterprise clients in the portfolio
Those three figures are resume-cleared and they belong to this portfolio of work. The 45% is a flagship proof point. It is not a number I am hanging on one team logo.
Role
Public title: Director of Product, Enterprise AI Strategy at Jump Platforms. LinkedIn lists the same chapter as Principal PM, Enterprise AI Strategy. I joined on February 22, 2023 as Director of Implementation. I work at Jump now. This site is a portfolio, not an exit note.
Artifacts
The prototypes on this site are shells for the public shape of the work: an ops console with approve and reject, and a fan-intelligence card. They are not production.
Notes
Product names and the suite description come from Jump’s open marketing and the February 2026 announcement. Personal figures come from the resume. Company case-study numbers stay in the company-context block.