GUEST OPINION: In high-performance sports, a millisecond can be the difference between a podium finish and the back of the pack. Incremental enhancements can reap big rewards, so teams are obsessed with uncovering data insights that may ultimately change the game.
The 18 Olympic medals the Australian Swimming team brought home recently weren’t only a result of elite athlete performance, Artificial Intelligence (AI) is a secret weapon the team has utilised for years. Along with Formula 1 team Scuderia Ferrari, Swimming Australia is part of a small pool of pioneering brands investing in the tech and reaping its step-change capabilities.
Despite wide agreement on the benefits of AI, most businesses are slow to embrace it. Our recent State of AI in Australia Report found that only 20.7% described their organisation as mature in AI. So how do businesses harness AI to deliver outcomes like those achieved by high-performing sports teams? Observing and applying their learnings can help other businesses accelerate AI implementation and avoid known pitfalls. Herein we unpack these real-world applications, underline the importance of agility and data quality and unveil optimal use cases across industry sectors.
The sports organisations applying AI to get ahead of competitors
Jess Corones, General Manager Performance Support and Olympic Campaign, Swimming Australia says AI is used to perfect training approaches in swimming. At first, the use of AI was disruptive, contradicting some traditional ways of thinking. However, it soon spurred the organisation to completely change fundamental aspects of its approach, including team compositions and the ordering of swimmers in relays. This adjustment, coupled with the dedication and commitment of the athletes and teams, was one aspect that contributed to the achievement of 4th on the Olympics medal table.
Adrian De Luca, Director of Cloud Acceleration, Asia Pacific at Amazon Web Services (AWS), shares how Ferrari leverages data and AI for competitive advantages in Formula 1 racing for team Scuderia Ferrari. With hundreds of real-time sensors feeding data into a vast data lake, they have built a system that runs several machine learning models to identify optimal racing strategies, reducing the cognitive load of engineers and helping to make decisions to inform drivers. In the world of F1, these seemingly marginal gains in performance can make a big difference in how high up the tables they end up in the championship.
AI is also used to enhance the fan experience. F1 enthusiasts may have noticed screen pop–us during TV broadcasts of the race, showing insights and predictions about strategy, competitor analysis and car performance. This information is digitally delivered with real-time data feeds at speeds humans are incapable of operating.
Business agility and data integrity drive insights that accelerate AI-enabled innovation
Data is often called “the new oil” because of its immense value as a resource. When effectively harnessed and paired with AI, it can become a substantial business asset. Clean, high-quality and accurate data are essential for identifying precise business insights that will inform the best AI use cases. Therefore, businesses need the agility to source data quickly before it becomes outdated.
Heavy infrastructure-centric industries that involve civil engineering, can take decades to steer in a new direction and assess their use cases and ROI on their investments. In contrast, startups in IT have very short cycles, taking a more product-centric focus and iterating with high agility to quickly adopt new technology.
Integrating AI can be more complex for some industries, as machine learning models may not always have access to machine-readable formats – for example, analog dials on devices. Therefore, the role of AI in supporting humans is both imperative and enduring as the data availability and sophistication around it constantly evolves.
Uncovering use cases that with the force of AI, will propel brands forward
Pinpointing optimal application areas is key for organisations to move beyond mere experimentation to truly realise the value of AI. Those who excel often work backwards from the desired outcomes.
The optimal AI use case varies depending on the sector. In Healthcare, predictive analytics and generative AI contribute to improved wellbeing of patients by increasing clinician availability, patient routing to the nearest medical services, and suggesting the next most appropriate medical tests to speed up diagnosis. In Finance, AI is most effective when used to detect fraud, and to automate next-step resolution customer experience actions.
Within the transport sector, constraint optimisation is a key area where machines are speeding up logistics, such as loading and unloading containers. In Shipping and Logistics, AI-driven automation and data analysis enhance operational efficiency and reduce costs by having systems look beyond the next step to assess end-to-end logistic benefits.
In many of these cases, it is not always GenAI but often predictive machine learning (ML) and constraint optimisation algorithms coming together in concert for business benefit.
Assessing risks to avoid common traps
Risk management should be a key priority when planning, building and operating any AI solutions to prevent businesses from being exposed in new ways.
For example, AI hallucinations happen when AI provides responses that are convincing but factually incorrect. Failing to detect these can jeopardise business processes and potentially harm reputations. This issue may result from insufficient testing, data quality problems, inadequate model maintenance, persona inconsistencies or poorly considered controls.
Risk management requires ensuring humans have a detailed understanding of the datasets involved. Different user personas have different interests, so it’s important to be mindful that one dataset can carry different levels of ROI for different roles, teams, business units and the wider organisation.
In Summary
An AI project delivers significant benefits when it has a strategic and specific use case, reliable data, clear ROI outcomes (such as increasing revenue, boosting productivity, or reducing costs), a thorough risk assessment, and the business’s support to move it into production.
Iconic sports brands like Scuderia Ferrari and Swimming Australia as well as trailblazing businesses in other industries are turning to AI to boost performance and are quickly realising it’s a game changer. AI is a strategic and imperative business tool that can drive significant business value and competitive advantage. Businesses that sit on the sidelines and do not take steps to leverage AI effectively will be exponentially outpaced by those who do.
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