AI Agent Operational Lift for On3 | Rivals in Nashville, Tennessee

Why AI matters at this scale

On3 | Rivals operates at the intersection of digital media and college sports recruiting, a space defined by high content velocity and an insatiable fan appetite for real-time information. With a team of 201-500 employees and an estimated revenue around $45 million, the company is large enough to invest in sophisticated technology but agile enough to deploy it rapidly without the bureaucratic inertia of a legacy media giant. AI is not a futuristic concept here; it is a competitive necessity to scale content operations, personalize user experiences, and protect margins in a crowded subscription landscape.

Three concrete AI opportunities with ROI framing

1. Automated video intelligence for recruiting footage. The lifeblood of recruiting coverage is high school game film. On3 likely ingests thousands of hours of raw footage. A computer vision pipeline can auto-detect touchdowns, tackles, and other highlights, instantly clipping and tagging them with the athlete's profile. This turns a manual, hours-long editing process into a near-instant one. The ROI is twofold: a dramatic reduction in video production labor costs and a surge in video ad inventory, as more clips mean more pre-roll and mid-roll placements against highly engaged audiences.

2. Hyper-personalized content feeds to reduce churn. A fan of the Tennessee Volunteers cares little for Oregon Ducks updates. By deploying a recommendation engine trained on user behavior, favorite team data, and recruiting interest, On3 can transform its homepage and newsletters into a personalized dashboard. This directly attacks the primary revenue risk: subscriber churn. Increasing retention by even 5% through better engagement can add millions to the top line over the customer lifetime, far outweighing the cost of a cloud-based personalization API.

3. Generative AI as a journalist's co-pilot. The transfer portal and commitment flips create a firehose of news that is often formulaic. A large language model, fine-tuned on On3's editorial style and fed structured data (player ratings, school lists), can draft initial reports. Journalists then shift from writing commodity news to adding insider analysis and breaking exclusive stories. This can double content output without doubling headcount, directly improving the cost-per-article metric and keeping the site dominant in search rankings during peak recruiting cycles.

Deployment risks specific to this size band

A mid-market company like On3 faces a classic "build vs. buy" dilemma. Building custom models requires scarce, expensive ML engineers, while buying off-the-shelf APIs risks commoditizing their unique voice. The primary risk is data leakage: feeding proprietary recruiting intel or subscriber data into public AI models could erode their competitive moat. A strict policy of using private, contracted AI instances is mandatory. Additionally, the editorial culture may resist automation. A phased rollout, starting with internal tools that assist rather than replace, is essential to gain buy-in and avoid a public relations backlash about "robot reporters" covering high school athletes.