Sports organizations at the college and pro levels are putting AI to work across the business. According to a new report from Teamworks and Front Office Sports, 90% of sports organizations expect their use of AI to grow over the next two to three years, and they’re backing that with a defined budget.
Spending isn’t the same as results, though. Buying a handful of disconnected tools doesn’t produce a return, and few organizations are seeing one: just 15% of respondents report a clear, measurable ROI.
The ones that do are doing a few things differently, starting with how they decide on AI. When AI is deployed against an organization-wide strategy, 37% see clear ROI. When it’s deployed one silo at a time, 2% do.
The State of AI in Sports report draws on a survey of more than 200 global sports organizations. Respondents include executives, leaders, and coaches across college athletic departments, pro league offices, multi-club ownership groups, and national governing bodies. The data shows where AI is paying off, where it isn’t, and how leaders can set their organizations up to benefit.
Here are the key takeaways:
HOW SPORTS ORGANIZATIONS ARE USING AI
Among organizations already using AI, more than two-thirds increased their investment over the past year, and 69% of the organizations that started a pilot are still running them. Data and analytics leads, followed by marketing and communications.
North American pro organizations are furthest ahead. More than half have AI systems deployed, and 87% increased their AI investment over the past year.
College programs are earlier in the curve. About half report using AI, and most adopt it case by case, which leaves them with scattered tools: 40% of those programs run four or more systems. Another 14% plan no meaningful AI investment in the next year, twice the overall rate, partly because of cost.
The college programs that have made AI an organization-wide decision tell a different story. 54% of them report clear ROI, while those that haven’t report none.
HOW ORGANIZATIONS PAY FOR AI
About a quarter of respondents have a dedicated AI budget. Another 38% pay for it out of existing software and operating budgets. And 29% don’t know how their AI spending is funded.
Many organizations are buying platforms without a specific use case in mind. The result is AI that lives in one department instead of working across the organization. When asked who makes AI decisions, the most common answer was that each department decides for itself.
The organizations with the strongest returns do it differently. They have a dedicated AI budget, a named owner, and a connected athlete record instead of one split department by department. The first two are decisions any organization can make. The third is where returns are strongest.
THE ROI GAP
The distance between what organizations expect from AI and what they get back is the widest gap in the survey. Only 15% report a clear, measurable ROI, which means about eight in ten organizations that have started AI tools or pilots still can’t point to one.
North American pro organizations are furthest along, and the least sure. When asked whether AI has delivered measurable ROI, 23% of NA pro respondents answered “unsure,” compared with 20% in college and 6% internationally. That suggests many organizations aren’t measuring what AI is doing for them, which means they can’t make the case for it.
The gap isn’t about who’s spending. It’s about what organizations have decided, and whether their data can back it up.
HOW DOES YOUR ORGANIZATION STACK UP?
The report tracked three things: a dedicated budget, a named owner, and a connected record. Most organizations can’t tell whether they have any of these. Answer the three questions below to see where your organization stands.
The scorecard
➤ Does AI have its own budget line? A line approved for AI that someone can spend, not a share of the software budget. 24% of the field says yes.
➤ Who owns AI decisions? One person or role accountable for AI across the organization. “No one,” “each department decides,” and “unsure” mean nobody does. Half the field has an owner.
➤ How many systems does it take to see one athlete? Count every system you’d check to see one athlete whole, including those outside your department. Among organizations close enough to athlete data to answer, 91% check two or more, 37% check four or more, and 9% can do it in one.
The first two questions are decisions you can make. The third shows how fragmented your data and tech are. Here is what the market told us:
Read each row as the odds of a return, not a verdict on your organization. The largest group, 44% of respondents, sits in the first row, where 4% show clear ROI. With one decision, it’s 13%, about three times higher. With both on a fragmented record, it’s about one in eight, no better than one decision. With both on a connected record, it’s two in three.
Here’s what to do next, based on your answers:
If you’ve made neither decision: You’re probably already spending — 79% of the field increased investment in the last 12 months. Make one decision this quarter. Name an owner now and give them a budget line at the next cycle. An owner with no line shows 10% clear ROI. An owner with a line shows 47%.
If you’ve made one decision: You likely have an owner but no dedicated funding. Give them their own AI budget line, then ask the third question before they spend it, so the money doesn’t go to tools that can’t see the whole athlete.
If you have an AI owner and a budget, but fragmented tech: You’ve done what most of sport hasn’t, and returns haven’t followed. Your owner is likely spending their time reconciling four or more systems. Connect the athlete records before the next pilot. Integration into existing systems is already this group’s top planned investment, at 75%.
If you have budget and ownership on a connected athlete record: You’re in the 9%, and the outliers this report describes. Build what’s uniquely yours into the shared system: your playing philosophy, valuation logic, and tactical judgment. Your risk isn’t falling behind. It’s adding systems and backsliding on your integration.
ONE RECORD, ONE OWNER, ONE BUDGET
Every department bought the tech it needed, and it worked because people filled the gaps between systems. AI can’t. It only knows what it can see, and in most organizations in this survey, it can’t see the whole athlete in one place.
The scorecard comes down to the same three things: a dedicated AI budget, someone accountable for it, and a record they can see. The survey can’t say which comes first. It shows they arrive together, and none does much alone. They meet in the system of record, where every system that touches an athlete describes them in the same terms, so what one system calls speed and another calls load add up to one picture someone can act on.
The best returns don’t require a bigger budget, a better model, or a head start. They require knowing what you’re trying to achieve and building backward from there.
WEBINAR OPPORTUNITY
Want to go deeper? Join Teamworks CTO Reed Shaffner on Tuesday, Oct 27 at 1pm ET as he walks through the findings. What separates the 15% who can point to clear, measurable ROI? Reed unpacks this, and more. Register for the webinar.
FROM TEAMWORKS
Teamworks has spent 20 years building the operating system that connects how teams acquire, develop, and deploy talent, for more than 7,000 organizations worldwide. We’re now embedding sport-native AI into it to understand the whole athlete.
Teamworks is one of the partners this report describes. Whichever route you take, the expensive part is the same: connecting the data and keeping every record tracking back to the same athlete. Most organizations underestimate both.
You don’t need to have made either decision yet. Start with the scorecard above. The full findings, including an expanded scorecard walkthrough and industry benchmarks, are in the report. To dig deeper into your AI strategy, connect with us here.