Across the Sport Industry, artificial intelligence is everywhere in the conversation. However, many leaders privately ask me an important question: is my organization truly ready for AI? If we want to achieve major productivity gains at scale, where should we start?
Boards discuss it. Innovation teams explore it, often in silos. Vendors present new solutions every week. Almost every organization is experimenting with tools such as generative AI, predictive analytics or automated workflows. Yet from our experience, very few companies have a clear vision or strategy for the roadmap they should follow to make the most of this extremely powerful technology.
The challenge in the Sport Industry is not the technology itself. Today, powerful AI tools are widely accessible and increasingly affordable. The real challenge is organizational readiness, the ability to integrate AI into how the organization works, how decisions are made, and how teams operate.
Some important questions can help you assess your organization’s AI readiness:
● Are your competitors already integrating AI into their operations while you’re still experimenting with tools?
● Do you have a clear AI strategy, or mostly pilots and vendor demos?
● Have you identified which roles and workflows must evolve to work effectively with AI?
● Is your organization building hybrid human-AI teams, or expecting current structures to adapt on their own?
● If AI adoption accelerated in your industry tomorrow, would your organization be ready to scale?
Why becoming an AI-ready organization matters? According to BCG’s Build for the Future x AI study, only a small percentage of organizations currently capture significant financial value from AI. However, those that do outperform their peers across several metrics, including growth and operational efficiency.
What differentiates these organizations is not simply technology investment. It is their ability to align technology, talent, and operating models around AI capabilities.
Elon Musk recently suggested in an interview that “AI-run companies will crush companies run by humans.” While that statement may sound extreme, the underlying message is difficult to ignore: organizations that learn to work effectively with AI will gain structural advantages in productivity, speed, and innovation.
What being AI-ready actually means
An AI-ready organization is not one that has simply purchased AI tools. It is one that has created the internal conditions required to adopt, integrate, and scale AI effectively.
Typically, this means several things:
● Leadership understands how AI can affect the business.
● Teams have basic AI literacy.
● Workflows are redesigned to incorporate automation and data-driven insights.
● Technology initiatives are aligned with clear strategic priorities.
AI-ready organizations move beyond experimentation. They begin to embed AI into the way the organization works.
Where most sport organizations are today
Many sport organizations are currently in what might be called the AI experimentation phase.
Teams test tools. Innovation departments launch pilots. Vendors demonstrate new capabilities. A few quick productivity gains appear.
But the organization itself often remains unchanged. Processes remain manual. Roles remain defined around pre-AI workflows. AI remains an interesting tool rather than a structural capability.
This is a natural phase in any technological transition. But organizations that remain here too long risk falling behind competitors that move toward systematic integration.
The next step is truly becoming AI-ready.
How sport organizations can start becoming AI-ready
Preparing an organization for AI does not require a massive transformation overnight. In fact, the most effective transitions often happen progressively.
Several practical steps can help organizations move from experimentation to readiness.
1. Build AI literacy across leadership and teams
One of the biggest barriers to AI adoption is not technology, but understanding its transformative capabilities.
Leaders need to understand what AI can realistically do, where it can create value, and where its limitations remain.
Teams need to know how to collaborate with AI tools, interpret outputs, and integrate them into workflows.
Organizations that successfully scale AI typically invest in AI literacy programs that help employees move beyond curiosity toward practical application.
Just as digital literacy became essential during the internet era, AI literacy is quickly becoming a fundamental capability.
2. Identify high-impact use cases before scaling
Another common mistake is trying to deploy AI everywhere at once.
AI-ready organizations typically start with a few high-impact use cases aligned with strategic priorities.
In sport organizations, these might include:
● fan engagement and personalization
● commercial and sponsorship analytics
● automated content production
● ticket pricing optimization
● performance analytics
Starting with focused initiatives allows organizations to learn quickly and build internal confidence before scaling more broadly.
3. Redesign workflows, not just “play with tools”
AI rarely creates meaningful value when it is simply added on top of existing processes.
Instead, organizations need to rethink how workflows operate across teams.
For example:
● If AI can personalize fan engagement at scale, how should clubs and leagues rethink their entire fan experience strategy?
● If sponsorship analytics can be automated and predicted with AI, how should commercial teams rethink how they sell partnerships?
● If AI can generate and distribute sports content instantly, what should media and marketing teams focus on to remain competitive?
● If AI can analyze performance data in real time, how should coaching, analytics, and performance teams evolve their roles?
● If AI dramatically reduces the cost of content production, how should sport organizations rethink their media and storytelling strategy and teams?
These questions require organizational redesign, not just technology implementation.
4. Develop hybrid AI-Human teams
One of the most important shifts organizations must make is moving toward human-AI collaboration.
As discussed in my previous article on hybrid teams, the most effective organizations combine human expertise with AI-driven capabilities.
● AI handles data-heavy analysis, pattern recognition, and automation.
● Humans focus on strategy, creativity, judgment, and relationships.
The goal is not replacing people with technology.
It is enabling teams to work differently and more effectively.
Emerging examples across the Sport Industry
Across the sport ecosystem, we are already seeing organizations experimenting with AI-enabled operating models.
Formula 1: data-driven decision making
Formula 1 teams analyze massive amounts of telemetry data during races. Machine learning models help simulate race strategies and predict optimal decisions.
But the real competitive advantage comes from how engineers and strategists integrate these insights into real-time decision-making during races.
Technology alone does not win races.
The organization designed to use that technology effectively does.
Real Madrid & FC Barcelona: AI-enhanced fan engagement
Real Madrid and FC Barcelona have explored AI tools to analyze fan behavior and personalize digital experiences across global audiences.
By combining data analytics with AI-driven insights, the clubs can tailor content and engagement strategies to different fan segments.
The clubs remain fundamentally sports organizations, but they are increasingly becoming data- and AI-enabled organizations.
The NBA: scaling content creation with AI
The NBA has experimented with AI technologies that automatically generate highlights from game footage.
This allows the league to distribute content much faster across digital platforms.
Rather than replacing creative teams, the technology allows them to focus more on storytelling, strategy, and creative direction.
Human creativity remains central — but AI dramatically increases production capacity.
The window of opportunity
At the moment, most organizations are still experimenting with AI. Few have fully redesigned their operating models around it. This creates a fantastic window of opportunity, likely two to three years, for organizations willing to prepare early.
During this period, companies can build internal capabilities, develop AI literacy, and redesign workflows before AI becomes deeply embedded across the industry. Once that shift happens, catching up will become far more difficult.
Organizations that become AI-ready early will benefit from learning curves that compound over time.
Preparing for the next era of sport innovation
Artificial intelligence will not replace the passion, emotion, and human relationships that define sport. Sport will always be about people.
But the organizations that succeed in the coming decade will be those that combine human expertise with intelligent systems.
Not every organization needs to become AI-first. But every organization should begin the journey toward becoming AI-ready. Because the next era of sport innovation will not be defined by technology alone.
It will be defined by how well organizations adapt their teams, operating models, and strategies to work alongside it.
And the organizations that begin that preparation today will be the ones shaping the next era of the Sport Industry.
This blog post is part of a thought leadership series featuring reflections and insights from Silvia Dvorak, Founder of The Future Talent and GSIC collaborator, exploring the evolving landscape of talent, leadership, and the future of work in sport and beyond.
