The AI Stack We’d Use If We Owned A Sports Team

Clawd bot. AI Voice Agents. MCP. RAG. Fine-tuning. n8n. Agentic Workflows. 

If the line above doesn’t sound like English, you should read this week's piece. 

Every day, it feels like new AI jargon drops, and somehow, even more AI products hit the market. While some of you might be up to date - experimenting with every new tool that comes out - most sports teams still run their businesses on email and Excel.

The reality is that the sports industry as a whole is not adopting artificial intelligence at the same pace as other sectors. 

So in today’s piece, we’re outlining the AI stack we would deploy if (when) we run our own professional sports team. 

For the sake of brevity, we’re focused strictly on business use cases. We’ll cover on-field and on-court applications in a future piece. We also expect to revisit this topic as the landscape continues to shift rapidly.

After reading this, if you run or own a team and want help figuring out where to start in this AI race, feel free to reach out. We’re happy to help.

What Our Dream AI Implementation In Sports Organizations Looks Like:

Most teams use AI like a fancy email drafter. We’re talking about something entirely different:

An “AI Operating System” for the franchise - a layer that sits on top of your CRM, ticketing system, email, social channels, and data lakes… and quietly executes thousands of micro-tasks every day that humans currently handle manually.

Use cases we’d be looking to implement today:

Think of it like hiring 10 ticket sales reps, 3 sponsorship analysts, a social media team that never sleeps, and an operations team that never drops a detail…but it’s all software.

What’s The Problem To Start With:

It’s no secret that most sports franchises are running on legacy technology:

An AI agent is a piece of software powered by a large language model (like OpenAI or Anthropic’s Claude) that can take a goal, plan the steps required, and execute multi-step tasks using tools and data with minimal human input.

AI agents are finally good enough to remove the bottlenecks in the messy workflows above - not by “being smart,” but by doing the unsexy work consistently.

And startups are starting to build toward this “operating system” that sit on top of existing software, and use AI to connect tools like Slack, email, ticketing, and CRM:

After interviewing with Y-Combinator two years ago with this exact idea and getting rejected (Sid), it’s interesting to see this concept come to fruition. 

According to this Sportico article, in 2024, ticket sales accounted for anywhere from ~25-50% of a league’s revenue. Ticket sales is still an ‘Excel + CRM babysitting’ job more than a selling job.

Across sales broadly, reps spend only ~28% of their week actually selling - the rest is swallowed by admin like deal management and data entry.

Day-to-day work at a team includes manually routing leads into a CRM, entering data into a CRM, taking call notes (by pen & paper in some instances!), blanket outreach, and fragmented customer service handling.

For this specific use case, AI can ensure cold customer lists are warmed up through personalized outbound, CRMs are dynamically updated, follow-ups are automatic, and customer support handling is central. All while saving the ticket salesperson for the most human (and important) part: closing the deal. 

Leading voice AI lab, ElevenLabs, ended 2025 with $330M ARR, and closed a Series D led by Sequoia Capital valuing the company at $11B Valuation. Outside of an AI-language dubbing partnership it has within Formula One and cricket, we haven’t seen this tool properly integrated into sports. 

ElevenLab’s Voice Agent can take and receive phone calls, all while sounding like a realistic human being. We think there’s a bigger opportunity to use this tech outside of pure AI language translation.

What we haven’t really seen yet: a pro team using this tech as the core engine of ticket pipeline creation (cold → warm → meeting booked → rep closes). That’s the gap.

The second layer: AI notes that turn every call into searchable memory

On top of the voice layer, AI call transcription tools like Granola AI & Otter AI are quietly eliminating another manual tax: note-taking and recap writing.

Instead of “hope the rep remembered the objections,” every call becomes:

That’s how you turn ticketing from an artisanal craft into an actual scalable machine.

The $70B global sports sponsorship market still operates the same ways as it always has: cold calling, static slide decks, and pricing based on “vibes”.

A typical sales cycle looks like this:

A rep spends weeks prospecting brands that might fill open inventory (e.g., LED screens, title sponsorships, social integrations). Once there’s interest, it’s time to wine-and-dine the prospect and pitch what’s available. If momentum builds, the sales team huddles and spends hundreds of hours building and updating custom media kits. Eventually, both sides negotiate towards a number that feels right.

In that entire process:

It’s relationship-driven, manual, and inefficient.

If we were running a team, we’d consider selling inventory through a liquid, data-driven marketplace - which now exists.

Anvara enables teams to sell digital and physical ad inventory directly on a marketplace. Brands browse available assets based on audience demographics and objectives, similar to filtering for a villa in Greece by price and location on Airbnb instead of scrolling through thousands of listings. 

Instead of over-indexing on cold-calling, brands can come to you.

On the team side, the platform recommended brands that best align with your audience and have the highest likelihood of conversion. Inventory is dynamically priced using AI models that factor in demand and performance, ensuring your team is capturing true market value.

There’s already over $1B in sponsorship inventory available on the platform, with brands like Snapchat and Delta are participating. 

As more buyers and sellers join, the marketplace naturally becomes smarter which makes it even easier to identify the right partner and price.

Sports teams have to meet the next generation of fans where they are - and they’re on social media.

If you want to keep fans engaged all season, you have to treat socials like MrBeast: constantly publishing moments in and around the game that maximize attention. 

Right now, teams are trying to solve this by hiring more personnel to handle manual tasks like video clipping and post scheduling - averaging 31 hours per week on work that can now be automated. But there’s simply too much content. More bodies don’t scale.

Social media teams are still drowning. They’re cutting thousands of clips per game and posting what-could-have-been-viral moments hours too late.

To remove the bottleneck, we’d implement an AI “highlight factory”.

Products like WSC Sports and Magnifi use AI to automatically clip live games, identifying key moments (goals, dunks, celebrations) in real time. Content is instantly formatted for all social media platforms (Instagram, TikTok, etc.), with auto-generated captions and metadata that social managers would otherwise take 1,000+ hours creating manually. 

It’s been a huge success for the Cleveland Cavaliers. Their team integrated WSC Sport into their app to personalize highlight feeds and capture valuable first-party data they wouldn’t have otherwise. During the ‘22-’23 season:

Athletes and sponsors need content too. But navigating through millions of photos, videos, and graphics for shareable assets is a nightmare. What’s needed is a true digital asset management layer.

Which is why we’d use Scoreplay - it automatically tags content by player, highlight type, sponsor reference, and context, distributing them to athletes and partners instantly. Through advanced AI analytics, the platform tracks usage and engagement across assets, helping teams understand what’s a hit (and what’s a miss) before doubling down.

At Athlos 2025, Scoreplay: 

The race was literally all over socials while watching it live!

As private equity and sophisticated ownership groups push deeper into sports, “value creation” will increasingly mean the same thing it means everywhere else:

increase revenue, reduce cost, improve efficiency.

We’re in the middle of an AI revolution - and the sports team that builds an AI-native operating model will create an unfair advantage.

Eventually, we’ll see a profitable sports team that’s truly “running on AI,” and that blueprint will spread fast.

That opportunity is currently present. We’ll see who makes the first moves.