In Formula One, the difference between victory and defeat often comes down to hundredths of a second. Teams invest hundreds of millions of dollars in car design, aerodynamics, and engine performance, but the modern paddock is also a battle of data and decisions. The most successful organizations combine cutting-edge technology with the judgment of highly skilled professionals, ensuring that every piece of information is transformed into action before rivals can respond.

This human-centered approach to digital transformation is central to the strategy at Aston Martin Aramco Formula One Team. Executives and partners say the rise of generative AI and agentic systems does not diminish the need for experience. Instead, AI gives engineers and strategists the tools to explore more options, automate mundane tasks, and focus on the creative and analytical work that machines cannot replicate.

Key takeaways

• F1 success depends on blending digital innovation with human expertise.
• Experienced professionals guide AI exploration to solve complex racing challenges.
• Human handcraft remains essential in a sport defined by marginal gains.
• Agentic AI can automate routine work and empower engineers to make faster decisions.

People remain at the center of the loop

Fabrizio Pilotti, chief information officer at Aston Martin Aramco F1, explained that IT is not just a support function. It is a performance-enhancing department. The goal is to provide engineers and designers with the right tools and data so they can make better decisions at every stage of car development and race weekend operations.

"If an idea is good, go through the process as fast as possible, and then get all the data back for the next iteration," Pilotti said. "This is where you win, and this is what we're focusing on."

That philosophy is visible at the team's technology campus near Silverstone. During a tour ahead of the British Grand Prix, visitors watched engineers refine components both digitally and by hand. One of the most striking images was Adrian Newey, the renowned aerodynamicist and managing technical partner, sketching new design ideas manually. His ability to visualize airflow and interpret complex data is a reminder that even in a highly automated sport, individual talent matters.

Pilotti noted that new joiners often expect a fully automated environment and are surprised to discover how much detailed handcraft is involved. "They see what a team is like from the inside and they say, 'That's not what I thought it would be like. It involves more detailed handcraft.'"

This insight is crucial for the broader conversation about AI and jobs. While many industries worry about automation replacing human workers, F1 offers a different lesson: technology amplifies expertise rather than replacing it.

Why handcraft still matters in a data-driven sport

Formula One has always been a testbed for innovation. From carbon fiber monocoques to hybrid power units, the sport pushes engineering boundaries. In recent years, data analytics and simulation have become just as important as physical testing. Thousands of sensors on a car generate terabytes of information during a single race weekend, covering tire temperatures, aerodynamic loads, fuel consumption, and driver behavior.

But raw data is useless without interpretation. An engineer with years of experience can spot patterns that an algorithm might miss. They know when a sensor reading is reliable, when a simulation is unrealistic, and when a small design tweak will produce a meaningful performance gain. This tacit knowledge is difficult to codify, which is why human judgment remains invaluable.

Pilotti described IT in an F1 team as a way to increase the capability of engineers to operate. "Our work is not exactly about the performance of the car, but we are increasing the capability of the engineers to operate. And this is happening continuously via better management of faults and better management of how they understand the performance of the cars."

The focus is on continuous improvement. Every race provides new data, and every new data point offers a chance to learn. Teams that can rapidly iterate and implement lessons often gain a competitive edge over the course of a season.

Exploring the potential of agentic AI

Aston Martin Aramco F1 is currently working on a range of digital projects, including enterprise applications, trackside systems, and the use of AI agents in software development. The team is also exploring how AI can improve optimization across ERP systems and other core business functions.

Pilotti said the ultimate goal is a seamless experience for users. "It's where the end user uses data without any interaction with IT. That's the end goal -- being seamless," he explained. "Whatever resources they need for their rear wing design, for example, they're immediately available exactly in the format and the density they require, without having to wait three months for new systems. The future is about modular flexibility, so that the infrastructure can adapt to the team's data requirements."

This approach is being developed in collaboration with technology partners. Cohere, an AI specialist, is working with the team to explore how sovereign AI models and agents can draw insights from telemetry, diagnostics, and simulation data. Ryan Lewis, head of UK and Northern Europe at Cohere, described the goal as empowerment. "We want to give people the power to automate the mundane things that are slowing them down from making executive decisions."

One of the biggest challenges in deploying AI in F1 is security. Teams guard their data carefully, and trade secrets can be worth millions. Lewis explained that Cohere builds systems that can be deployed within the team's own infrastructure, reducing the risk of data leakage while still providing useful results. This approach allows the team to use AI without compromising confidentiality.

Human experience drives value from AI

While technology partners provide crucial tools, they do not replace the deep technical knowledge that exists within the team. Eric Ernst, commercial technology ambassador at Aston Martin F1, said AI offers cognitive scalability but not judgment.

"With AI, we can't outsource the experience," Ernst said. "The experience is still with the team, but AI gives our people the cognitive scalability to do more than they can today."

This distinction is important as more companies try to adopt AI. A model can generate options, but a human must evaluate those options using experience and context. In F1, an engineer might be faced with three possible strategies for a race. The engineer's experience helps them choose the right one, and AI helps them explore the potential consequences of each choice more quickly.

"The engineer is the person who's able to say, 'OK, I've got three options, and my experience tells me that's the option we should be choosing,'" Ernst said. "It's that experience that's crucial to driving value from AI."

This collaborative model is not limited to F1. Many industries are discovering that the most effective use of AI involves human oversight and domain expertise. In healthcare, doctors use AI to analyze medical images but make the final diagnosis. In finance, analysts use AI to identify market trends but decide how to act. In every case, the technology works best when it supports skilled professionals.

How F1 teams balance innovation and reliability

Formula One teams operate under strict regulations and tight deadlines. A new car is designed and built over several months, and upgrades are delivered throughout the season. This environment demands a high degree of reliability. New digital tools must be tested and validated before they are used in race conditions.

Pilotti and his team work closely with engineers to understand their needs and develop solutions that are practical and robust. The IT department is responsible for the digital foundations that underpin everything from car design to race strategy. This includes high-performance computing, data storage, network connectivity, and software tools.

One area of focus is simulation. Before a car hits the track, engineers run millions of simulations to test different configurations and predict performance. These simulations generate enormous amounts of data, and AI can help identify the most promising design directions. However, the final decisions are still made by senior engineers who understand the nuances of the car's behavior.

Another area is trackside operations. During a race weekend, teams have only a few hours of practice to set up the car. Data from the car is streamed to the garage in real time, and engineers use that data to make adjustments between sessions. AI-powered analytics can highlight anomalies or suggest setup changes, but the engineers decide whether to implement them.

Building a culture of continuous learning

The technological landscape in F1 is constantly evolving. New rules, new materials, and new competitors all require teams to adapt. This culture of continuous learning extends beyond the engineering department. The IT team, for example, must stay up to date with emerging technologies such as generative AI, edge computing, and advanced analytics.

Pilotti said the key is to move quickly when an idea shows promise. "If an idea is good, go through the process as fast as possible, and then get all the data back for the next iteration." This mindset encourages experimentation and reduces the risk of becoming stuck in a long development cycle.

At the same time, teams need to avoid chasing every new trend. A successful F1 organization knows which investments will deliver the greatest performance gains. This requires strong collaboration between technical partners and in-house experts.

Ernst described the ecosystem of partners as essential to the team's success. "It's about unleashing intelligence and using every partner not to adapt to the future, but to actually architect it -- taking complex challenges and providing clarity and momentum at each layer and each line of code at a time."

That ambition is reflected in the way F1 teams now operate. They are no longer just car manufacturers; they are technology companies that happen to make racing cars. The same transformation is happening across many industries, where digital capabilities are becoming a source of competitive advantage.

Looking ahead: humans and machines working together

The next few years will likely see more widespread use of agentic AI in F1 and beyond. These systems will be able to perform tasks independently, such as monitoring data streams, generating reports, and even proposing design modifications. However, they will still need human supervision to ensure that their actions align with the team's goals and values.

Pilotti and his colleagues believe the best results come from a partnership between people and technology. The IT department's role is to create an environment where engineers can work at their best. This means providing reliable tools, high-quality data, and the freedom to explore new ideas.

The emphasis on human expertise is not a rejection of AI. Rather, it is an acknowledgment that AI is most powerful when it is guided by people who understand the context. As Ernst put it, the experience is still with the team. AI provides the cognitive scalability to do more, but it cannot replace the judgment that comes from years of working in a complex, high-stakes environment.

For anyone watching F1, the sight of a champion team celebrating on the podium is the result of thousands of small decisions made by talented people. The same principle applies to organizations in every sector. Technology can provide an edge, but it is the human in the loop who decides how to use it. In F1, that balance of digital power and professional handcraft will continue to define success for years to come.

Source: ZDNET News