Motorsports Pioneers the Digital Twin and Industrial AI Playbook for Engineering

Modern racing has quietly become one of the most demanding proving grounds in engineering. Success on track now hinges on data — how it's captured, connected and acted upon — and the technologies being refined in motorsports are pointing the way for the wider manufacturing world.

"In modern racing, winning and climbing to the top podium depends as much on precision digital engineering as it does on raw speed."

That observation comes from Royston Jones, Global Head of Automotive & Transportation at Siemens Digital Industries Software, and it captures a shift that manufacturing professionals would do well to watch closely.

Real-Time Data as the Foundation

Racing engineers use onboard sensors to monitor vehicles and components in real-time, feeding accurate performance data directly to engineering teams. The constraints of the sport — tight turnarounds, relentless iteration — have made racing engineers exceptionally disciplined about data acquisition. As the thinking goes:

"Innovation loves constraint, after all."

But acquiring data is only the first step. With thousands of parts generating enormous volumes of information, traceability and accessibility become the real engineering challenge.

One Source of Truth

This is where the comprehensive Digital Twin earns its keep. It connects data from different aspects of a vehicle — mechanical, electrical, thermal and more — serving as a single source of truth for racing teams across design, manufacturing and simulation.

The Digital Twin also enables high-fidelity simulation, and with fresh data from the track, these simulations can model up to a tenth of a second on a track. The payoff is measurable: Oracle Red Bull Racing achieved a 300 percent improvement in part design cycle time and made aerodynamic design iterations 1,000 percent quicker per iteration.

Industrial AI Enters the Pit Lane

Industrial artificial intelligence (AI) differs from the generative AI dominating headlines. Integrated into engineering models and automated systems, it enhances data analysis and ensures safety and reliability in engineering recommendations — all while keeping data secure and in-house.

Critically, industrial AI can identify component fatigue or reliability issues before they occur, giving engineers time to address problems proactively rather than reactively.

Beyond the Racetrack

The implications extend well past motorsports:

"The same combination of the comprehensive Digital Twin and industrial AI that helps racing teams gain a competitive advantage is helping automotive manufacturers develop software-defined vehicles faster, aerospace companies validate increasingly complex systems and manufacturers optimize operations while reducing cost, risk and waste."

The lessons learned in motorsports are being applied to develop the next generation of vehicles, products, factories, and industrial systems. For manufacturers, the takeaway is clear: the ability to rapidly transform data into actionable insights is a core competitive differentiator across industries.

Jones brings deep credibility to this view. With over 40 years of experience, he has been instrumental in developing cutting-edge solutions for automotive, including methodologies to reduce vehicle weight and optimize battery pack performance. He previously served as both CTO of Product Design and Senior Vice President of the Global Automotive Vertical at Altair prior to its acquisition by Siemens Digital Industries Software, which remains actively involved in digital innovation in motorsports.