For many large organisations, cloud computing still sits in the background. It runs internal systems, supports analytics teams, and scales storage when needed. What is changing is where cloud shows up in the work itself — including in performance-critical environments like Formula 1 or F1, where Mercedes is using cloud systems to support real-time decisions under pressure.
That shift is becoming clearer as performance-driven organisations move more critical workloads into 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 reporting by Windows Central, the team will use Microsoft’s Azure cloud and AI services to process large volumes of data tied to car performance, race conditions, and engineering decisions.
While Formula 1 may sit outside traditional enterprise sectors, the way Mercedes operates looks familiar to many large companies. It runs complex systems, depends on real-time data, and makes decisions under pressure. That makes it a useful case for understanding how cloud is moving beyond back-office IT and into the core of operations.
From support system to decision engine in F1 cloud operations
A modern Formula 1 car produces large amounts of data during a race weekend, from telemetry and sensor readings to simulation outputs and track conditions. Teams use this data to adjust strategy in near real time, weighing factors such as tyre wear, weather changes, and competitor behaviour.
Cloud infrastructure plays a growing role in handling this workload. Instead of relying only on local systems at the track, teams can push data to the cloud, run simulations at scale, and feed results back to engineers and strategists. The value is not speed alone, but the ability to test more scenarios, faster, using shared data across locations.
This mirrors a trend seen across large enterprises. Manufacturing firms use cloud-based simulation to test production changes before rolling them out. Logistics companies model routing decisions based on live inputs. Financial institutions run stress tests and risk models continuously rather than on fixed schedules.
Research from McKinsey shows that companies using cloud and advanced analytics together are more likely to embed data into day-to-day decisions, rather than keeping it within specialist teams. The same pattern applies here. Cloud becomes part of how work gets done, not just where systems live.
Why latency, reliability, and scale matter
What separates these workloads from standard enterprise applications is their tolerance for delay. In a race environment, a late insight is often useless. The same holds true for trading desks, supply chains, or large customer service operations reacting to spikes in demand.
That raises questions many enterprises now face. Can cloud systems deliver consistent performance under pressure? How should workloads be split between on-premise systems, edge devices, and central cloud platforms? What happens when connectivity drops or systems fail?
According to Gartner, by 2026 more than 75% of enterprise data will be created and processed outside traditional data centres or central clouds, driven by the need for faster response times and local decision-making. Formula 1 teams already work this way, combining on-site systems with cloud resources that extend computing capacity when needed.
The Mercedes case shows that cloud adoption at this level is less about cost savings and more about control. Organisations want to decide which workloads belong where, based on performance needs rather than architecture trends.
Cloud as part of organisational design
Another lesson for large enterprises is that cloud adoption here is not limited to IT teams. Engineers, analysts, and strategists all rely on the same systems and data. That requires shared standards, clear data governance, and trust in the tools being used.
The World Economic Forum has noted that organisations struggle when cloud and AI systems are added on top of existing workflows without redesigning how teams work. High-pressure environments force that redesign to happen faster. Processes adapt because they have to.
Enterprises outside motorsport face less visible pressure, but the underlying challenge is similar. As cloud supports more operational decisions, failures become more costly, and governance becomes harder to ignore.
What the Mercedes F1 case means for enterprise cloud strategy
For companies watching these developments, the takeaway is not to copy Formula 1 technology choices. It is to recognise how cloud is being used when performance matters.
First, cloud is increasingly tied to decision speed, not just efficiency. Second, hybrid models are becoming the default, not a compromise. Third, cloud success depends as much on organisational alignment as on 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. That shift helps explain why cloud is showing up in places once considered too sensitive or complex.
The Mercedes example fits into this broader pattern. Cloud is no longer just a place to run systems. It is becoming part of how organisations think, decide, and act — especially when the margin for error is thin.
See also: Why cloud spending keeps rising as AI moves into daily operations
Want to learn more about Cloud Computing from industry leaders? Check out Cyber Security & Cloud Expo taking place in Amsterdam, California, and London. The comprehensive event is part of TechEx and is co-located with other leading technology events, click here for more information.
CloudTech News is powered by TechForge Media. Explore other upcoming enterprise technology events and webinars here.
