For many large organizations, cloud computing is still in the background. Operates internal systems, supports analytics teams, and scales storage as needed. It is changing where the cloud manifests itself in the work itself – including performance-critical environments such as Formula 1 or F1, where Mercedes uses cloud systems to support real-time decision-making under pressure.
This shift is becoming more apparent as performance-driven organizations move more critical workloads to the cloud. One example comes from Formula 1, where the Mercedes-AMG Petronas team is expanding its use of cloud infrastructure to support race strategy, simulation and data analysis ahead of the 2026 season. According to reports from Windows Centralthe team will use Microsoft’s cloud and AI services to process large volumes of data related to car performance, race conditions and technical decisions.
While Formula 1 may stand outside traditional corporate sectors, the way Mercedes operates is familiar to many large companies. Operates complex systems, depends on real-time data and makes decisions under pressure. This makes it a useful example for understanding how the cloud is moving beyond the IT back-office and into the core of operations.
From support system to decision engine in F1 cloud operations
A modern Formula 1 car produces a large amount of data during a race weekend, from telemetry and sensor data to simulation outputs and track conditions. Teams use this data to adjust strategy in near real time, weighing factors such as tire wear, weather changes and competitor behavior.
Cloud infrastructure plays an increasingly important role in handling this workload. Instead of relying solely on local systems at the track, teams can transfer data to the cloud, run simulations at scale, and provide results back to engineers and strategists. The value is not speed itself, but the ability to test multiple scenarios faster using shared data across sites.
This reflects a trend seen in large enterprises. Manufacturing companies use cloud simulation to test changes in production before they are implemented. Logistics companies model routing decisions based on live inputs. Financial institutions perform stress tests and risk models on an ongoing basis, rather than on fixed schedules.
McKinsey research shows that companies using the cloud and advanced analytics together are more likely to incorporate data into day-to-day decisions rather than keep it in specialized teams. The same formula applies here. The cloud is becoming part of how work is done, not just where systems live.
Why latency, reliability, and scale matter
What sets these workloads apart from standard enterprise applications is their latency tolerance. In a racing environment, late insight is often useless. The same applies to sales counters, supply chains or large customer services responding to an increase in demand.
This raises questions that many businesses are now facing. Can cloud systems deliver consistent performance under pressure? How should workloads be distributed between on-premise systems, edge devices and central cloud platforms? What happens if the connection drops or the system crashes?
According to Gartner, by 2026, more than 75% of enterprise data will be created and processed outside of traditional data centers or central clouds, driven by the need for faster response and local decision-making. Formula 1 teams already operate this way, combining local systems with cloud resources that expand computing capacity when needed.
The Mercedes case shows that cloud adoption at this level is less about cost savings and more about control. Organizations want to decide where workloads belong based on performance requirements rather than architecture trends.
Cloud as part of organizational design
Another lesson for large enterprises is that cloud adoption is not limited to IT teams here. Engineers, analysts, and strategists all rely on the same systems and data. This requires shared standards, clear data management and trust in the tools used.
The World Economic Forum noted that organizations struggle when cloud and artificial intelligence systems are added to existing workflows without reshaping the way teams work. The high-pressure environment forces faster rework. Processes adapt because they have to.
Businesses outside of motorsport face less visible pressure, but the underlying problem is similar. As the cloud supports more operational decisions, failures become more costly and management more difficult to ignore.
What the Mercedes F1 case means for enterprise cloud strategy
For companies following this development, the goal isn’t to copy Formula 1’s technology choices. It’s to see how the cloud is used when performance matters.
First, the cloud is increasingly tied to decision-making speed, not just efficiency. Second, hybrid models become the default, not the compromise. Third, the success of the cloud depends on both the organizational arrangement and the technical design.
According to IDC, more than half of large enterprises now say their cloud strategy is driven by business resilience and operational flexibility, rather than cost reduction alone. This shift helps explain why the cloud is emerging in places that were previously considered too sensitive or complex.
The Mercedes example fits into this broader pattern. The cloud is no longer just a place to run systems. It becomes part of how organizations think, decide and act – especially when there is little room for error.
See also: Why cloud spending continues to grow as AI moves into everyday operations
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