The Artificial Intelligence market is valued at over $136 billion currently, set to rise to $309.6 billion by 2026. AI and ML models have more data than ever to sift through. Inferring recommendations and gleaning insights from big data become easier than ever as AI becomes more advanced.
See below for some practical implications on how we see AI impacting the world of sports tech in 2023.
Generative AI
One of the biggest technology topics in 2023 will be the continued emergence and improvement of AI tools that generate effective content and media with simple user prompts and inputs.
The emergence of tools like OpenAi’s ChatGPT (reportedly valued at $29 billion) and GPT-3, language processing AI models are generating a ton of excitement, not only because of their capabilities, but also because of the access—anyone can start experimenting with this technology by tapping into the available API.
Natural language processing (NLP) and natural language generation (NLG) are two aspects of AI that help drive content production and the automation of content output for sports media outlets. NLP can sift through comments and engagements from customers or viewers to discover what fans are getting excited about and what they’re engaging with. This helps create topics and talking points, while NLG can create content based on these themes.
Beyond language generation, text-to-image technologies such as OpenAI’s DALL-E 2, Stable Diffusion and Midjourney make it possible to generate high-quality images of varying artistic styles, depending on the prompt.
Automating these processes allows sports organizations to have constant streams of content generated. Consumers love visual content more than ever yet Hubspot stated that in 2022, 23.7% of content marketers cite design and visual content marketing as one of their biggest challenges. One answer to this could be generative algorithms that output both text and images, producing high-quality, comprehensive content at a very low cost. In the future, we could see the pairing of predictive analytics and AI-generated content creating images of a quarterback scoring the winning touchdown before it even happens.
These advancements in generative AI also open the door to enhanced chatbot activations for fans, going beyond simple interactions to create fully conversational experiences.
Features and benefits could include:
- Hyper-personalization; communication style that adapts to the individual
- Curated content for individual users
- Recommendations provided in a conversational and relatable manner
- Increased engagement and data collation by starting conversations rather than answering closed questions
Ethical Concerns
A recent ethical consideration with the use of generative algorithms is that no content is off limits unless original creators, such as photographers and artists, take steps to make their work unavailable. This means AI-generated imagery could breach copyrights or steal intellectual property. One workaround could be to ensure the AI models sports organizations use come from a carefully curated pool of copyright-free data.
We predict AI will make analytics better and faster, immerse fans in the game more than ever and create far more monetization opportunities than currently exist. Most major organizations seem to agree, with business growth experts Forrester predicting 10% of Fortune 500 companies will generate content via AI and that it will become “an intrinsic part of what makes a successful enterprise.”
Predictive Analytics
Content generation aside, there are other applications of AI that are very exciting.
AI and ML empower companies like nVenue to provide predictive analytics that give fans information less than a second after a particular play has occurred. AI promotes a zero-latency environment aimed at increasing engagement with sports media and betting providers. These types of analytics are also useful for training sports teams, especially when utilized within the context of historical data.
AI/ML Ops
MLOps combines Development Operations (DevOps) and AI, bringing data science into the realm of enterprise operations. Effective MLOps need reliable AI infrastructure, which is where we see investment heading in 2023. Deploying ML models will become a structured, rigorously monitored part of everyday operations. Any sports organization utilizing AI will eventually have to adopt MLOps best practices to ensure accurate, efficient curation of their data.
Challenges Ahead
While the advancement of generative AI tools is undoubtedly exciting, widespread adoption of these technologies will lead to an even more crowded landscape. Content will be easy to produce and perfectly optimized for clicks but will likely be more generic.
As the technology behind generative AI content advances, we will increasingly question the origin and validity of any piece of content. This emerging paradigm will put the onus back on media organizations and content creators to continue to build trust with their consumers and fans through original thinking and critical analysis.
