March 12, 2021 By Margaret Doyle 5 min read

From transforming marketing strategies to delivering thrilling and engaging digital fan experiences, find out how sports organisations can adjust to a digital-first future. Explore what’s possible now and how the AI ladder can help you develop the enterprise capabilities required to bring technology-driven initiatives to life.

The world is undergoing tremendous change that impacts all industries, and the sports ecosystem is no different. The current health crisis caused training sessions, matches and sports competitions to be postponed or even cancelled entirely, disrupting all the stakeholders in the industry and leaving fans everywhere disconnected from the teams and the sportspeople they love. Now, organisations are left to navigate the implications of these extreme measures, while planning for a future that may look very different from the past.

When technology meets sport

Sport, in all its forms, is driven by – and produces, in turn – copious amounts of data. Purposefully exploiting that abundance of data can enable sports organizations to predict and shape business outcomes, automate decisions and experiences and reimagine a highly personalised business model where the fans are at the core.

In pursuit, of these outcomes, enterprises in sport are increasingly turning to one of the defining technologies of our time: Artificial Intelligence – or AI, in short. But, implementing AI isn’t a one-time task. It’s rather like shifting the focus from winning a game to winning the Championship: organisations must rethink their perspective and see broad AI implementation as a strategic core business journey.

Three transformative ways in which AI makes a difference in sport

1. Creating better fan experiences

As the world changes, so do fans – and they have grown to demand greater convenience, personalisation and immersion from every interaction.

In 2018, at the FIFA World Cup, fans had an AI-based platform that enabled them to create and share their customised soccer highlight videos. The platform also enabled users to browse and filter FIFA’s archive using numerous filters, compiling footage in seconds at the user’s command.

But fan experiences changed immensely over the last year. For the 2020 edition of the US Open, held without in-person spectators, the USTA implemented a first-of-a-kind, AI-powered tennis experience to bring fans gameplay insights and an opportunity to debate some of tennis’ hottest topics, using IBM® Watson Discovery® natural language processing and AI-powered search capabilities.

For the 2020 Masters Tournament, patrons could create customised feature groups of golfers in the app, and access video of every shot by those players throughout the Tournament. These personalised groups featured features highlights and regular updates from the Tournament leaders, and must-see shots from other golfers, as they were happening. The new feature enables patrons, anywhere in the world, to watch every shot, on every hole, from all of their favourite golfers competing in the event.

2. Maximising broadcasting and streaming

Because it can quickly process large amounts of data, AI-enabled video highlights can deliver stats and results speeding the time to market for the production crew. In 2017, the AI system implemented for Wimbledon created highlights that generated 14 million views and delivered relevant content up to 15 minutes quicker.1 The tennis-afficionado AI was programmed to gather and analyse data courtside, taking cues from an ace at 100 mph, spotting gestures by spectators such as fist pumps to learn which moments to keep for the highlights reel and which to discard.

Building on that technology, AI can be used to do play-by-play and colour commentary of pre-selected videos for soccer matches. The commentary incorporates observations based on statistics and rankings drawn from a database, matching the information to whatever is happening in the video. Moreover, AI technologies can enhance the quality of old footage, in order to make it more appealing for younger generations. IBM did this when the Wimbledon grand slam tennis tournament was cancelled in 2020 due to the COVID-19 crisis and the organisers needed to bring their decades-old content to modern digital platforms.

3. Optimising revenue opportunities

Getting to know fans while they’re in a location can help with maximising safety and comfort for visitors and optimising income for sport organisations in the same way it helped enterprises in other industries.

It’s what Denmark’s premier music festival did in 2019, when it implemented a cognitive system that helped with everything from queue management and overcrowding strategies to analysing logistics patterns to identify areas eligible for improvement. The AI leveraged data from wearable devices to enable guests to securely pay for tickets, VIP access, food, drinks and accommodation amounting to 745,495 cashless transactions.

By analysing alternative data sources such as geolocation, playlists, bar and restaurant transactions, the AI system can potentially help organisations understand their audience better. Moreover, an AI-powered sales forecasting software capability leveraging real-time data can help enhance efficiencies in the supply chain and generate further savings.

How to reap the benefits of successful AI implementation 

Artificial intelligence, fuelled by data and powered by cloud, is probably the biggest opportunity of our time. In fact, AI has the potential to add almost 16 trillion dollars to the global economy by 2030.2

However, artificial intelligence is not the magic wand that organisations think it to be. In order to add value to businesses, AI needs a well thought-out and well-designed approach, particularly in today’s cloud-driven world. With a unified, prescriptive, and open approach to AI implementation, organisations can modernise and make their data ready for an AI and for a multicloud future. Take this quick assessment, to discover where you are in your AI journey and how you can be better prepared for the future.

The AI Ladder is a comprehensive framework developed by IBM to provide sport entities with an understanding of where they are in their AI journey as well as help them modernise their information architecture to connect and democratise data from multiple sources. It is a guiding principle for organisations looking to transform their business, by providing four key areas to consider: how they collect data, organise data, analyse data, and then, ultimately, how they infuse AI into their organisation. For example, the vast majority of AI failures are due to data preparation and organisation, not the AI models themselves. Find more information in this introductory report that explores AI’s drivers, value, and opportunity, as well as the adoption challenges organisations now face.

Spanning the four steps of the AI ladder is the concept of modernising the infrastructure, which is how clients can simplify and automate how they turn their data into valuable insights. Modernisation contributes to unifying the collection, organisation and analysis of data, regardless of where it lives, within a multi-cloud data platform named Cloud Pak for Data. The platform follows the guiding principle of running everywhere and can be integrated with existing infrastructure investments. Moreover, this can be deployed on-premise, which is extremely relevant for organisations focused on enhancing business flexibility with strategies for seamless communication between the private and public cloud.

The key takeaway here is that AI is not magic. It’s hard work. It requires the proper tools, methodologies, and mindset to overcome the gaps that companies are facing in terms of data, skills, and trust. But once organisations learn how to extract the maximum value from their data, they have the power of turning AI aspirations into outcomes.

Discover where you are in your AI journey and explore strategies and next steps for broad AI implementation across the enterprise.


¹ – Steve McCaskill, Wimbledon 2018: AI Marries Tennis Tradition With Digital Innovation, 2018, Accessed November 2020

² – Hemanth Manta, The AI Ladder : IBM’s Prescriptive Approach, 2020, accessed November 2020

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