If we compare a modern data center to a car factory, the differences are staggering. In an automotive plant, processes are orchestrated down to the millimeter: robots assemble parts with surgical precision, assembly lines follow predictable routines, and operators supervise more than they execute. Automation isn’t a promise — it’s been a reality for decades.

And yet, in the data center industry — where critical processes, massive volumes, and constant efficiency demands are also the norm — we still rely on spreadsheets, manual tickets, and word-of-mouth procedures. So, what’s going wrong?

Over 100 years ahead

The automotive industry has spent more than a century exploring, refining, and consolidating its processes. In 1913, Henry Ford revolutionized production by introducing the first moving assembly line at the Highland Park factory in Michigan. This innovation drastically reduced assembly times, standardized tasks, and laid the groundwork for industrial automation.

Since then, the sector has made unstoppable progress toward increasingly complex and intelligent automation. Vehicle manufacturing has become a symphony of precision, where every part and every action are perfectly coordinated. The culture of "doing it better, faster, and with fewer errors" is embedded in its DNA.

In contrast, the term "data center" didn’t gain popularity until the 1990s, when enterprise computing began consolidating and centralizing its tech resources. That means the automotive industry has almost an 80-year head start in automation compared to the data center world — and that gap matters..

An industry that learned to repeat (and automate)

Automotive succeeded in automating because it understood something fundamental: repeating the same thing, many times, allows optimization. The standardization of components, the assembly line, and a mindset of continuous improvement created an ecosystem where every step could be measured, adjusted, and automated.

It’s not just about technology. It’s an industrial culture — the belief that anything can be systematized if it’s repeated enough, if the process is well understood, and if there's a clear vision of efficiency.

In data centers, however, this vision hasn’t yet taken hold. While there are efforts to standardize physical design — from cabling layouts to energy efficiency standards — day-to-day operations remain largely unstructured. There are no widely accepted standards for how to manage a change, execute an intervention, or model capacity. Every organization defines “operating a data center” in its own way, which makes even simple tasks unnecessarily complex.

This lack of standardization has a direct effect on technology: without a common foundation, it’s much harder to build automated tools that work across the board. Every piece of software ends up tailored to each client’s specific quirks — increasing complexity and reducing real impact.

What’s going on in data centers?

In data centers, every infrastructure seems to be an exception. One has legacy servers, another hybrid deployments, the next is in the cloud but still running on-premise processes. No two are alike. And if every environment is unique, how do you repeat? How do you automate?

Add to that the fragmentation of responsibilities. Facilities, IT, and operations often work in silos, using different tools and lacking a shared view of the environment. In some data centers, everyday tasks like checking electrical capacity or coordinating a rack change still require emails, phone calls, and manual validations. Meanwhile, companies that have integrated management and operations platforms can cut these processes down to minutes — thanks to automation driven by real-time data.

And the cost of not automating isn’t always measured in euros — but it shows up in time, errors, and frustration. Overworked shifts, ignored alerts, slow decisions. Valuable technical talent wasting hours on repetitive tasks that could be handled by a well-implemented basic logic. This isn’t just inefficiency — it’s a lost opportunity for innovation.

Automation isn’t magic — it’s design

Automating doesn’t mean “running a script” or “using a trendy tool.” It means designing processes that are repeatable, scalable, and observable. And that requires understanding the data, defining clear flows, and breaking the idea that every task is a handcrafted one-off.

The funny thing is, the data is already there. Sensors, monitoring platforms, logs, management systems — they all generate information. What’s missing is connecting the dots, seeing the full flow, and acting accordingly.

There’s a different way to do things

At BJumper, we believe this industry can make the leap. That we’re not doomed to fragmentation and perpetual manual work. We promote a different vision: automation driven by data, deep understanding of the environment, and the ability to generate intelligent, contextualized actions.

Is it easy? No. Is it possible? Absolutely.

And the sooner we stop accepting Excel-based operational procedures as “normal,” the sooner we can have serious conversations about real efficiency, scalability, and resilience.

The future? Choosing whether we still want to build cars by hand

The question isn’t whether we can automate data centers. It’s whether we’re willing to. Whether we’ll accept that this industry can be just as precise, reliable, and agile as any modern factory — or whether we’ll continue treating every operation as if it’s the first time.

Because automation isn’t just about technology. It’s a design decision. And it’s in our hands..