From billions of race simulations in Formula One to monitoring sensors across a fleet of sailing boats, data informs decisions before, during and after competition.

Reporting from SuiteWorld and AIWorld 2026 in Las Vegas, sharing insights from Jack Harrington, Head of Technical and Performance Partnerships at Oracle Red Bull Racing, and Matt Lord, Group Technology Director at SailGP. The discussion covers race strategy, equipment maintenance and talent development, alongside Heather’s question: with so much technology involved, where does that leave a young person dreaming of becoming a sporting superstar?

Key points

• AI agents: Red Bull Racing engineers can query racing regulations, while SailGP is developing tools to help coaches interrogate performance data.
• Data sharing: Formula One teams closely protect their performance information. SailGP gives competing teams access to shared data to help improve performance across the fleet.
• Equipment maintenance: SailGP uses sensor data and anomaly detection to identify potential component failures before they disrupt racing.
• Race strategy: Red Bull Racing runs billions of simulations to assess pit-stop timing, changing conditions and competitors’ possible responses.
• Talent development: Sim racing creates alternative routes into motorsport, while both organisations explore how technology can support training and widen participation.

The session offers a practical look at technology already being used in elite sport, alongside projects still in development. Engineers, coaches and athletes use these tools to inform decisions, with human judgement central to how the information is applied.

Episode resources and links:

• https://www.oracle.com/
• https://www.netsuite.com/portal/home.shtml
• https://www.redbullracing.com/int-en 
• https://sailgp.com/ 
• https://www.linkedin.com/in/greg-lunsford-2a401a1/

Heather attended SuiteWorld and AIWorld 2026 as hosted media, as a guest of Oracle NetSuite.

Hello, I’m Heather Smith, and welcome to the Accounting Apps Podcast, where we explore how technology is transforming the world of accounting and business.

I’m currently in Las Vegas, attending SuiteWorld and AIWorld 2026, and I attended a session on the topic AI in Sport looking at how artificial intelligence, data and technology are being used in professional sport.

The session featured two speakers:

Jack Harrington, Head of Technical and Performance Partnerships at Oracle Red Bull Racing. And Matt Lord, Group Technology Director at SailGP.

It was moderated by Greg Lunsford, VP Corporate and Industry Communications, Oracle.

The conversation explored how both organisations use Oracle technology to improve performance, make faster decisions, manage costs and develop talent.

Formula One: Eight Billion Race Simulations

Jack Harrington started by explaining the scale of technology used in Formula One.

Unlike many other motorsports, Formula One teams design, develop and manufacture their own cars within a set of regulations.

The cars generate enormous volumes of data. Jack explained that Oracle Red Bull Racing collects approximately two terabytes of data from each track session.

This information is analysed to understand vehicle performance, identify improvements and inform racing strategy.

One example Jack shared was how the team determines the best time to make a pit stop.

A Formula One pit stop can take less than two seconds. Red Bull Racing’s fastest four-tyre pit stop was completed in just 1.82 seconds.

But deciding when to make that stop involves analysing numerous variables.

Some are known, such as tyre degradation and expected lap times.

Others are unpredictable, including accidents, safety cars, unexpected overtakes and changing track conditions.

To manage this complexity, the team uses Monte Carlo simulations, a mathematical technique also used in financial services and telecommunications.

Before a race weekend, Red Bull Racing runs approximately eight billion race simulations.

During practice sessions, the team collects additional information about track conditions and vehicle performance.

That data is incorporated into another eight billion simulations.

During the race itself, the models are updated as circumstances change.

The team also simulates how competitors might respond to its strategic decisions.

Jack explained that working with Oracle had enabled Red Bull Racing to increase the number of simulations it runs by 25%.

The result is more information available to the race strategists when making decisions under pressure.

AI Agents on the Pit Wall

Another example involved the use of AI agents during races.

Jack explained that Red Bull Racing has developed an AI agent that engineers can consult when interpreting racing regulations.

The system has access to sporting regulations, technical regulations and previous decisions.

During a race, situations can arise where the correct interpretation of a regulation isn’t immediately clear.

For example, what happens if a driver leaves the circuit while overtaking another car? Can they retain their position?

Previously, an engineer might need to search through lengthy documents to find the relevant guidance.

Now they can ask the AI agent a question.

The system identifies relevant information and recommends a course of action.

The engineer reviews that recommendation and makes the final decision.

Jack emphasised that the purpose of the technology is to allow engineers to concentrate on the race rather than searching through documentation.

He also explained that the system had already proved useful during the racing season.

SailGP: Sharing Data Between Competitors

Matt Lord then explained how SailGP approaches technology.

SailGP operates a global racing competition using F50 catamarans.

These boats can reach speeds of approximately 100 kilometres per hour, powered entirely by wind.

One important difference between Formula One and SailGP is that SailGP provides all competing teams with identical boats.

And, unusually for professional sport, SailGP shares performance data between competing teams.

Every team has access to information about how the other boats are performing.

They can examine which settings competitors used, how they responded to conditions and where performance improvements were achieved.

The aim is to raise performance standards across the entire competition.

The boats are equipped with extensive sensors that capture information about their operation and performance.

Matt explained that this data is made available through Oracle-powered dashboards.

SailGP is also developing AI agents that allow coaches to ask questions about performance across the fleet.

For example, a coach might ask why one boat was travelling faster than another or why a competitor chose to sail in a particular direction.

The AI can help analyse the data and provide information to support coaching decisions.

This approach is also being used to train new athletes and support new teams joining the competition.

Matt noted that the data is available to the public through Oracle Insights, allowing fans to explore team performance.

Predictive Maintenance and Equipment Reliability

SailGP also uses data to maintain its boats.

Matt explained that the league is responsible for manufacturing and maintaining the F50 catamarans.

Equipment failure can prevent a team from competing, so identifying potential problems early is important.

Working with Oracle, SailGP has developed an anomaly detection system that analyses sensor data from the boats.

The system identifies unusual patterns that might indicate a component is approaching failure.

For example, it might detect potential problems with a pump or valve before that component stops working.

Matt said the system regularly identifies one or two areas of concern at each event, allowing components to be inspected or replaced.

The organisation is now exploring how to move from overnight analysis to real-time monitoring during racing.

Another project combines equipment data with voice recognition.

If an athlete reports that something doesn’t feel right, the system could examine the sensor data to investigate whether there is evidence of a mechanical issue.

SailGP is also developing AI-based wind mapping to help athletes understand changing wind conditions across the racecourse.

Historical data, simulations and live sensor readings could help predict how wind patterns will affect the boats.

Developing Engines, Managing Budgets and Supporting Staff

Jack also discussed how technology supports the wider operations of Red Bull Racing.

The team has undertaken a major project to develop and manufacture its own Formula One engines.

He explained that the engine programme had grown from having no staff or facilities approximately six years earlier to employing more than 700 people.

Oracle technology has supported the engineering simulations and infrastructure needed to develop the programme.

Another area of focus is financial management.

Formula One operates under a cost cap, restricting how much teams can spend on specified development activities.

Jack explained that Red Bull Racing was working with Oracle Fusion applications to explore how AI could support budget simulations and resource allocation.

Deciding where to invest development funding has become an important part of the team’s competitive strategy.

He also discussed using AI to support employee development.

Red Bull Racing employs people across numerous technical and professional disciplines.

The team is exploring how AI can help personalise training and development based on individual roles and requirements.

Matt also shared that SailGP uses NetSuite for financial forecasting across the league, including the manufacturing of its boats.

With a relatively young sporting competition expanding internationally, managing expenditure and allocating resources remain important priorities.

Making Sport Easier to Understand

Both speakers discussed how AI could make their respective sports more accessible to audiences.

Matt explained that sailing can be difficult for new spectators to understand.

Boats might travel in different directions across a racecourse, and the reasons behind those decisions aren’t always obvious.

SailGP already uses an augmented reality broadcast system called LiveLine.

It overlays information on the television broadcast, showing course boundaries, boat positions, speeds and other performance information.

The organisation is exploring how AI, computer vision and conversational interfaces could improve the viewing experience.

The idea is that spectators could ask questions about what is happening during a race and receive explanations in everyday language.

For example, why did a boat choose a particular direction? Why was one team faster in certain conditions?

SailGP is also considering how these tools could help commentators explain the sport to audiences across different countries and languages.

Identifying the Next Generation of Talent

Another project Jack discussed involved using AI to identify potential Formula One drivers.

Traditionally, teams scout talent through established motorsport pathways, including karting, Formula 4, Formula 3 and Formula 2.

Red Bull Racing is exploring whether AI can help identify promising drivers across a broader range of racing competitions.

By analysing additional sources of performance data, the team hopes to discover talent that might otherwise be overlooked.

Jack made it clear that the project was still in development.

Which brings me to the question I asked during the audience Q&A.

My Question: Can Technology Make Anyone a Superstar?

I introduced myself as Heather Smith from the Accounting Apps Podcast and asked:

“Where does that leave the 14-year-old who is wanting to become a race car driver or a sailor when you have all of that technology? Do they? Can you make anyone a superstar?”

Jack Harrington responded first.

He pointed out that Formula One drivers generally begin their training well before the age of 14.

Max Verstappen, for example, started karting when he was approximately four years old.

But Jack explained that technology is creating alternative pathways into motorsport.

Traditional motor racing is expensive, and the cost of vehicles, equipment and competition can make participation difficult.

Sim racing provides a more accessible starting point.

Red Bull Racing operates a professional sim racing team and uses digital racing technology to identify and develop talent.

Jack shared the example of Sebastian Job, who progressed from the Red Bull sim racing team into a simulator driver role supporting the Formula One team.

During race weekends, simulator drivers receive data from the track and use sophisticated simulators to test different vehicle setups.

They can work through the night analysing potential improvements, with their findings sent back to the race engineers.

Jack explained that this provides an alternative opportunity for people to develop skills and potentially enter professional motorsport.

He summarised his response by saying:

“I think it will be an enabler for people to get into the sport rather than being a blocker.”

He also mentioned that visitors to the exhibition could try the racing simulators used by their sim racing team and compare their performance with Max Verstappen.

Matt Lord approached my question from a different perspective.

For SailGP, one of the main challenges is increasing awareness of sailing and creating more opportunities for athletes to compete.

He explained that when he joined SailGP, the competition had six teams and six events.

It has since expanded to 13 teams and 13 events.

This growth provides more opportunities for athletes, coaches and technical specialists to participate.

Matt also discussed the league’s efforts to encourage female participation, including having female athletes represented across the teams.

SailGP’s near-shore racing format is another important part of making the sport accessible.

Spectators can watch the boats racing at high speed close to shore, creating a different experience from traditional offshore sailing.

Combined with television broadcasts and social media, this helps introduce sailing to people who might not otherwise encounter the sport.

Matt’s hope is that more young people will see sailing as something they can participate in, even without an established background in the sport.

Both responses focused on technology and new competition formats creating additional opportunities for people to enter and progress in elite sport.

Closing

A few things stood out to me from this session.

First, the scale of data analysis taking place in both sports. From billions of race simulations in Formula One to monitoring sensors across a fleet of sailing boats, data informs decisions before, during and after competition.

Second, the practical applications of AI. Engineers are using agents to interpret regulations, coaches are exploring performance data, and maintenance teams are identifying potential equipment failures.

Third, the different approaches to sharing information. Formula One teams closely protect their performance data, while SailGP makes data available across competing teams to help raise the standard of competition.

And finally, the discussion about developing talent.

Both organisations are exploring ways to use technology to improve training, identify potential and create new opportunities for people to participate.

It was particularly useful to hear how these technologies are being applied in operational settings, including the projects already in use and those still being developed.

That’s my summary of the AI in Sport session at AIWorld and SuiteWorld 2026 in Las Vegas.

I’m Heather Smith, and you’ve been listening to the Accounting Apps Podcast.

Thank you for listening.