Sports Has Adopted AI. The Leadership Model Hasn’t Caught Up.

For many sports organizations, AI still sits somewhere between experiment, efficiency tool, and strategic priority. It may be helping a marketing team generate content ideas, a sales team prepare for partner conversations, a performance staff interpret player data, or an operations team identify patterns that would have taken weeks to find manually. For others, it is already part of the operating system, influencing strategic planning, ticketing, sponsorship intelligence, and fan engagement. 

What has not kept pace is the leadership infrastructure underneath. Many sports businesses still have not answered the questions that determine whether AI actually moves the organization forward: Who owns the strategy? How will it affect the workforce? What changes when a relationship-driven business begins relying more heavily on AI-supported decisions? And what kind of leadership capabilities will be required when AI moves from experimentation to enterprise-wide execution? 

The industry does not need another argument for why AI matters. It needs a clearer view of what using it well will actually require: the right leadership model, governance structure, talent strategy, and operating discipline. 

The Cost of Waiting Has Changed 

Sports organizations have often been second movers on technology, waiting to see how other industries adopted new tools before adapting those lessons to their own environment. That approach has worked when technology changed one function at a time. AI is different because it moves across functions at once. 

The shift is already visible across the industry. In 2025, 75% of NFL teams were using some form of AI as part of their weekly preparation, while the league has explored applications such as Digital Athlete to help predict injury risk. Teams like the Tampa Bay Buccaneers have hired a Chief AI Officer (CAIO) to own these efforts. FIFA’s use of AI-enabled tools around the World Cup shows the same pattern at a global event level, with technology touching officiating, match analysis, broadcasts, and fan engagement at once. Commercially, the Golden State Warriors’ jersey patch partnership with AI cloud provider Iren shows that AI is also becoming part of the sports sponsorship economy, not just an internal technology conversation. 

Yet in our conversations with Presidents, Chief Administrative Officers, CHROs, and other senior leaders across the industry, one issue surfaces repeatedly: how to deploy AI responsibly while driving real business outcomes without undermining the trust, judgment, creativity, and personal connection that sports businesses depend on. 

That tension is particularly important in sports. Revenue is often built through long-term relationships. Partnerships are shaped by nuance, reputation, and timing. Fan loyalty is emotional. Internal cultures are highly visible and often deeply personal. AI can strengthen all of that if it is used with discipline. Used carelessly, it can flatten what makes sports organizations distinctive. 

The real risk isn’t moving too slowly on tools. It’s delaying the leadership decisions that determine whether those tools create value or simply create the appearance of progress. An organization that treats AI as a narrow IT initiative, or something to monitor and revisit next planning cycle, may find that employees, vendors, and competitors have already moved ahead without a clear plan. 

AI’s Ownership Dilemma 

The most important question many sports organizations still have not answered is a simple one: who owns this? 

AI’s reach extends well past the technology function. Workforce planning, employee training, revenue strategy, legal exposure, data governance, and fan experience are all affected. That brings CHROs, commercial leaders, legal teams, and operators into a conversation that cannot be resolved by the CTO alone. 

Some organizations are responding by appointing dedicated AI or technology leaders, such as a CAIO responsible for developing strategy, coordinating governance, and connecting AI investment to business priorities. Others are building cross-functional councils that bring senior leaders together across functions. Neither model will fit every organization. What matters is that accountability is clear, that it’s tied to outcomes the business actually cares about, and that the structure can scale. 

Without that, most organizations default to something that looks like progress: scattered pilots, vendor conversations, and enthusiastic experiments that lack the ownership and alignment to deliver lasting results. This is where leadership selection and development become central to the AI conversation. 

The Talent and Culture Challenge Underneath the Technology 

For sports organizations, AI readiness is also a talent and culture challenge. Sports runs on relationships, and that raises the stakes. In a conversation with the CHRO of an established professional sports team, one theme stood out: the importance of being intentional about where AI should support the business without replacing the judgment, trust, personal connection, and goodwill that drive revenue, partnerships, and fan loyalty. 

Entry-level employees may find that the work that once taught them the business has been automated before they had the chance to learn from it. AI may create new opportunities across the workforce, but only if organizations are deliberate about reskilling, role design, and how they develop the next generation of leaders. 

What Sports Can Borrow from Other Industries 

As outlined in JM Search’s recent article, “Hiring an AI Leader in Retail Is Uncharted Territory. Here’s the Map,” sports organizations can learn from that work without starting from scratch. 

1. Start with business problems, not tools. AI investments should connect to clear priorities: ticketing efficiency, sponsorship intelligence, premium sales, fan engagement, content production, venue operations, or workforce productivity. Organizations that start by asking what problem they are solving tend to get more out of AI than those that start by evaluating what AI can do. 
2. Assign clear ownership. Ownership without accountability is just a title. Whoever leads AI strategy, whether a CAIO or a cross-functional council, needs a direct line to business outcomes, not just technology milestones. 
3. Clarify decision rights. Clear reporting lines and escalation paths keep AI from becoming a collection of disconnected experiments. Governance around data usage, security, privacy, and legal exposure is especially important in sports, where organizations manage fan data, partner information, athlete data, and brand reputation at the same time. 
4. Prepare managers before AI scales. Managers translate strategy into daily work, identify skill gaps, set expectations, and help employees understand where AI supports their judgment rather than replaces it. Organizations that invest in manager readiness early tend to move faster and with less friction. 
5. Measure business impact, not activity. Tracking tool adoption or pilot count is not a measure of progress. Adoption rates, vendor demos, and pilot volume only matter if they connect to revenue growth, efficiency, risk reduction, better decision-making, or a stronger employee and fan experience. 

Questions Worth Answering Before You Scale 

Before scaling AI, sports leadership teams should be able to answer four questions: 

• Where can AI solve a real business problem in our organization? Not where it could theoretically apply, but where a specific problem exists today that AI could address better than the current approach. 

• Who owns AI strategy, governance, and accountability? If the answer is unclear, or if multiple people would give different answers, that is the first thing to fix. 

• What leadership capabilities are missing today? AI will require executives to make decisions they haven’t had to make before, about workforce impact, data governance, vendor relationships, and the line between automation and judgment. 

• How will AI affect roles, development, and workforce planning? The organizations that handle this well will be deliberate about it early, before roles change without explanation and before entry-level development paths erode without replacement.

The sports organizations that get the most from AI will not simply be the ones that move first. They will be the ones that build the leadership discipline, talent strategy, and operating structure to make AI useful, responsible, and worth the investment.