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In Formula One, every decision impacts performance - from car setup to pit strategy and energy deployment. Engineering teams at the track and factory process massive amounts of data to extract insights and make real-time decisions in high-pressure conditions.
AI is changing the way teams operate; enhancing speed, consistency, and precision in decision-making.
But where does AI add the most value?
The answer lies in a structured approach to assessing AI’s role in engineering workflows.
Turning Data into Competitive Advantage
To successfully integrate AI into their operations, Formula One racing teams must identify how decisions are made, what data is available, and how AI-driven insights will be leveraged within engineering frameworks.
1. What Decisions Need to Be Made?
The first step in applying AI is identifying critical decisions that shape race performance. AI should be used where it enhances accuracy, efficiency, or adaptability.
Strategy: When is the optimal time to pit? How can we predict race scenarios to maximise pace and track position?
Power Unit: Where should we deploy battery power for the best advantage? How can we predict and manage energy usage for performance and efficiency?
Aerodynamics: Before the race, how should we set up downforce and drag levels to suit circuit demands?
Tyres: How do we predict and manage degradation for the ideal stint length? What’s the best compound selection based on track evolution and weather forecasts?
2. What Data Powers AI-Driven Decisions?
AI depends on high-quality, relevant data—but not all data is equally valuable. To ensure AI delivers useful, actionable insights, teams must identify the right data sources for their specific engineering challenges.
Historical Performance Data – Past races, strategy outcomes, tyre performance, and weather trends. Used to train AI models and refine predictions.
Live Telemetry Data – Sensor readings from the car (temperatures, pressures, loads). Used for real-time analysis and in-race adjustments.
Simulation & Testing Data – CFD simulations, wind tunnel tests, and driver-in-the-loop models. Useful for pre-race setup optimisation.
Competitive & Track Data – Sector times, pit windows, track evolution. Used to understand race dynamics and refine strategy models.
3. What Analysis is Required?
AI models must be tailored to the type of decision they are supporting. Understanding the right analytical approach ensures AI delivers useful and reliable insights rather than unnecessary complexity.
Pattern Recognition: Identify key trends (e.g. in tyre degradation, fuel usage, and track evolution) without predefined objectives, surfacing unexpected correlations in the data.
Predictive Modelling: Estimates future outcomes, such as undercut effectiveness, tyre life, or energy deployment efficiency, allowing teams to act proactively.
Optimisation Algorithms: Run AI models in simulations to determine the best possible race strategies, balancing multiple variables such as tyre wear, fuel consumption, and traffic conditions.
4. When Should AI Deliver Insights?
AI models must be aligned with the time constraints of decision-making. Not all insights need to be delivered instantly - some are useful over different time horizons.
Real-time AI: Supports in-race decisions, such as adjusting energy deployment, or responding to safety cars.
Near real-time AI: Analyses lap-by-lap data, refining strategy models and performance optimisation of each stint.
Offline AI: Runs pre-race simulations, aerodynamic studies, and long-term performance modelling to guide race preparation.
5. Should AI Assist or Automate Decisions?
AI can either support engineers by providing insights, or automate repeatable adjustments where regulations allow. The level of AI integration depends on how structured, predictable, repeatable the decision is, to decide on:
AI-Assisted Decision-Making: AI processes vast amounts of data to generate real-time recommendations, with engineers making the final call.
AI-Driven Automation: For high-frequency, repetitive processes, AI can execute predefined optimisations (e.g., within control systems), enabling autonomous operation and adaptive optimisation of specific components.
Next Steps for Your Formula One Team
At SIG Machine Learning, we help racing teams assess, integrate, and optimise AI solutions to enhance performance across strategy, vehicle dynamics, and operations.
Whether you're exploring AI for the first time or looking to improve existing models, we recommend a structured approach to ensure AI is practical, effective, and aligned with real-world engineering needs.
We work in collaboration through a proven framework to:
1. Define the Business Case
What decisions have the biggest impact on performance?
Where can AI improve accuracy, efficiency, or decision speed?
2. Identify & Structure the Right Data
What data sources (historical, telemetry, simulation) are available?
Is the data high-quality, relevant, and structured for AI-driven insights?
3. Select the Right AI Techniques
Does the challenge require pattern recognition, predictive modelling, or optimisation?
How will AI integrate into engineering tools and workflows?
4. Align AI with Decision Timing
Does the decision require real-time, near real-time, or offline AI insights?
How will AI-generated insights be delivered to engineers and decision-makers?
5. Define AI’s Role in the Decision Process
Should AI assist engineers by surfacing insights?
Can AI automate structured, repetitive decisions within regulations?
AI in Formula One is about enhancing, not replacing, engineering expertise.
By structuring AI into workflows effectively, teams can improve precision, efficiency, and adaptability - turning data into a competitive advantage.
🚀 Interested in exploring AI-driven optimisation in your team? Let’s discuss how AI can enhance performance, from the factory to the track.
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