The Algorithm Doesn’t Know Our Fans. We Do.

Orange County Soccer Club sells out a 5,500-seat stadium every Saturday night in a market with the Angels, the Ducks, LAFC, the Galaxy, Disneyland, and a beach. They do it the same way every week: by knowing their audience. The season member who gets the text before the public announcement drops. The post-match walkout where players stop and talk. The call that comes from a salesperson who remembers your name.

That’s not community-as-consolation-prize for being a second-division professional club. It’s the strategy. Recognition — the kind where fans feel known, not tracked — is the competitive advantage. And it’s chosen, not inherited.

Which is why AI was a harder question for OCSC than it looks from the outside.

The right question

Most organizations adopting AI aren’t asking the right question. The question isn’t whether. The technology is here, the tools are cheap, and every sports league is watching what its peers are doing with it. The question is how to adopt it without quietly dismantling the thing that made you a fan in the first place.

Done on autopilot, AI optimizes for efficiency and scale. Those are fine goals for a CPG brand. For a club whose whole premise is that this team shows up differently than the big ones do, they’re dangerous. The AI-generated email with a fan’s first name and a “based on your past purchases” recommendation starts to quickly feel like mass impersonalization. The fans can tell a fake. They always can.

OCSC’s actual question: how do we use AI to expand what makes us different instead of flattening it?

That question isn’t unique to soccer. Every organization has something that brings its customers back — the expertise that lives in one person’s head, the relationship no one else could replicate, the quality of judgment your best people carry. AI adoption done without naming that thing first tends to flatten it. OCSC named theirs. That decision shaped everything that followed.

What we walked into

A team of two people ten years ago with Excel spreadsheets that had grown into three full revenue teams — Sales, Marketing, and Partnerships — selling into overlapping legacy fan and sponsor relationships with little to no shared view of the customer. The same company might exist in Sales spreadsheets, Marketing’s email platform, and Partnership’s personal trackers: different contact info, different status, different history. Nobody saw the full picture; not from a lack of effort, but because the structure didn’t surface what the organization already understood.

One person handled every data pull for every team. If Marketing needed a segment, she built it. If Sales needed a prospect list, she pulled it. When something got figured out in a meeting, it evaporated if not acted on quickly. Every meeting seemed to start from scratch.

Partnership ROI reports — the documentation that tells sponsors what they actually got — were built by hand. Hours per report, every time.

The real opportunity wasn’t outbound automation. It was making the organization’s own knowledge visible — across three teams that couldn’t completely see each other’s work.

The rules first

The first move wasn’t technology. It was alignment.

We shadowed each revenue team, mapped workflows, and interviewed champions and skeptics alike. The first deliverable wasn’t a tool recommendation. It was an AI Manifesto — a document that named, explicitly, what OCSC would and wouldn’t do with AI.

The Manifesto took positions. AI adoption is not optional, it’s a directive from ownership. Freed time goes to relationship-building and new products, not headcount reduction. AI gets embedded in workflows, not bolted on. Champions lead adoption, not mandates.

James Keston, the club’s owner and CEO, set the tone: “I don’t see this as a replacement tool. I see this as an enhancement tool.” And the question that guided everything after: “What do we not know about what we could possibly do?”

That question — asked with genuine curiosity, not anxiety — is the difference between AI adoption that expands an organization’s identity and AI adoption that erodes it. James’s reaction when the Manifesto was complete: “I can’t believe every company isn’t doing this today, right now.”

“I can’t believe every company isn’t doing this today, right now.”

James Keston, Owner & CEO, Orange County SC

Once the Manifesto existed, technology decisions changed character. Instead of evaluating tools against features and price, the team evaluated them against principles they’d already agreed on. Does this give our best people more time to do what only they can do? Greenlight. Does it simply automate the human part without improving it? Pass.

What got built — and what didn’t

The sales team adopted meeting transcription without being asked — no mandate, no training program. Institutional memory started compounding instead of evaporating between meetings.

The marketing manager found her footing with the AI tools available to her and, over the course of the engagement, migrated her entire personal workflow from ChatGPT to Claude on her own initiative. The tooling discipline reached the personal-productivity layer, not just the enterprise stack.

Installing Salesforce across the organization was determined to be the best path forward, and the single source of data sharing and truth. An AI and Data Ops roadmap mapped the jobs-to-be-done to two internal candidates, giving OCSC a path to build the capability from within rather than hire externally.

The team identified $43,000 to $108,000 per year in tool consolidation — contracts for platforms either redundant or underperforming against what they already had.

And equally important: what didn’t get built. Every club in the league has been pitched Opus Clip. OCSC evaluated it, ran the math, and passed — because ClipPro, the system they were already running, was already doing the job well enough. A club whose competitive advantage is discernment cannot afford to adopt every new thing because a vendor flew to Irvine.

One person on the Partnerships team started the engagement as a skeptic and finished it as a champion. Not because anyone mandated it. Because the evidence was in the room. Demonstrated value compounds. Mandates don’t.

What OCSC owns now

A shared language for AI decisions that outlives any tool or vendor. A champion network across all three revenue teams. Institutional memory that compounds. A roadmap for the AI and Data Ops role mapped to internal talent.

The decisions about AI are being made by OCSC people, in OCSC’s voice, with OCSC’s competitive advantage in their heads. That’s the only way this was ever going to work.

How the work happened

The engagement ran about three months — timed deliberately to OCSC’s pre-season window. Professional soccer is a seasonal business. They had a market to capture and a calendar starting to fill. We worked inside that urgency: discovery, alignment, the Manifesto, tool evaluation, and the first wave of capability-building before the season was underway.

When the strategic foundation was in place, the next phase of work — Salesforce configuration, data migration, systems integration — required a technology partner, not a strategy one. We made the referral, bridged the transition, and stepped back.

OCSC runs its AI strategy independently now. That’s the measure.

For other organizations watching this

Whether you run a soccer club, a mid-market services firm, or a PE-backed company — a few things worth taking.

• Write down what makes your organization worth coming back to. Name the thing explicitly, and run every AI decision through that lens. OCSC’s version was authenticity; their fans can feel the difference between a message that knows them and one that looked them up. Yours is something else. Find it before you deploy anything. The Manifesto that came out of this engagement is the document James Keston said every company should have — and he’s right.
• Champions lead adoption. Mandates don’t. Find the person already curious, already frustrated by the old workflow, and give them the budget and cover to pilot. They’ll move the rest of the team further than any top-down initiative ever could.
• Your data problems are your AI problems. Fragmented sources of truth produce confident errors at scale. Fix the foundation before you invest in the interesting stuff.
• The best AI investment gives your best people more time to do the part of the job only they can do. Everything else is noise.

Parable Labs is run by people who have spent careers building new ventures inside large organizations — at Mach49, PayPal, Opower, and elsewhere — and then doing it ourselves with AI-native infrastructure from day one. We don’t show up with a platform to sell or a playbook to execute. We show up with operating judgment, and we stay until the team can run without us.