Data Strategy at Mercedes-AMG Petronas F1: Balancing Innovation and Operational Control

The Mercedes-AMG Petronas Formula 1 team manages one of the world’s most data-intensive operations, processing over one million data points per second from 300 vehicle sensors during a race. According to Michael Taylor, the team’s IT Director, the core challenge for the organization lies in maintaining high-speed decision-making while ensuring operational stability and data governance within a high-stakes environment.

The Dual-Mode Operational Framework

The Mercedes-AMG Petronas team operates under a distinct dual-mode structure that separates research and development from race-weekend execution. Based at a 60,000-square-meter technology campus in Brackley, U.K., the team focuses on design, engineering, and experimentation during the periods between races.

As the team approaches a race weekend, the operational priority shifts to performance and execution. Taylor emphasizes that this transition is critical; the team avoids making technological changes that could disrupt engineers during the race window. This strategy creates a constant tension between the need for rapid innovation and the necessity for rigorous system stability. Taylor notes that the team relies heavily on personnel with deep industry experience to manage the risks associated with these shifts, as they must balance agility with the need to prevent technical failures during competition.

Modernizing Core Infrastructure with RISE with SAP

To support its complex lifecycle—spanning design, procurement, manufacturing, and track-side assembly—the team recently modernized its enterprise resource planning (ERP) systems. The team transitioned to the RISE with SAP platform, a cloud-based ERP solution.

The migration was treated with the same precision as a Formula 1 pit stop. The team initiated the process in December 2024 with a target completion date of August 2025, strategically aligned with the sport’s mandatory two-week summer break. By maintaining a lean IT team of 18 people, Mercedes-AMG Petronas manages the majority of its systems internally. They utilize SAP’s expertise for specific technical challenges but retain direct control over the management and implementation of the platform, allowing them to complete the migration eight weeks ahead of the projected timeline.

Security and AI Governance in a High-Performance Culture

Protecting intellectual property is a primary concern for the team, as the competitive advantage of a Formula 1 car relies on proprietary simulations, modeling, and failure-mode analysis. To address this, the team employs an offensive security strategy where the Chief Information Security Officer (CISO) reports directly to the IT Director. The team actively tests its own defenses, simulating hacker methodologies to identify vulnerabilities without impeding the workflow of end users.

Regarding the integration of Artificial Intelligence, the team is currently conducting pilot projects, including machine learning for simulations and AI-assisted coding tools. However, Taylor remains cautious about premature scaling. The current organizational stance prioritizes:

* Human-in-the-loop oversight: AI is intended to complement human expertise rather than replace it.
* Data Governance: The team has increased requirements for visibility, permission clarity, and defined data ownership to ensure that AI models only access authorized information.
* Outcome-Based Adoption: Decisions regarding which AI tools to scale will be made based on performance results gathered over a six-to-ten-month pilot period.

Framework for CIOs: Prioritizing Consequences

For leaders managing high-pressure technical environments, Taylor suggests that the most effective approach is to start with the consequences of a failure rather than the technology itself. By identifying the specific risks—such as the loss of competitive advantage in F1, regulatory non-compliance in finance, or patient safety in healthcare—CIOs can determine where to apply strict controls versus where to allow for flexibility and faster development. This methodology allows the organization to establish a “good enough” governance model that remains practical and enforceable, rather than pursuing a level of perfection that creates unnecessary bottlenecks.