For years, the message has been clear: automate.
Automate processes, automate decisions, automate operations. And in the Data Center, where everything is critical, that message has taken deep root.
Today we have more tools than ever: systems that predict failures, platforms that adjust loads, and algorithms that promise to optimize everything with almost no human intervention. And yet, many critical infrastructure managers feel exactly the opposite: more complexity, more noise, and a greater sense of losing control.
This is where the hyperautomation paradox appears.
When automating stops being simplifying
Hyperautomation is not just about automating repetitive tasks. It goes much further: it combines artificial intelligence, advanced analytics, machine learning, and process automation to orchestrate end-to-end decisions.
On paper, it sounds perfect.
In a Data Center, this should translate into fewer human errors, faster response times, and a more efficient operation.
The problem arises when automation grows faster than the ability to understand and govern it.
According to IBM, hyperautomation brings real value only when there is a clear strategy for the integration and governance of automated processes. Otherwise, the risk is creating complex systems that act without a coherent global vision.
The modern Data Center: automated, but fragmented
In many of today's data centers, the following coexist:
- Infrastructure monitoring tools.
- Energy management systems.
- Predictive analysis platforms.
- Automations that execute corrective actions.
- Dashboards that promise a "global vision."
Each works well within its own scope. The problem is that they don’t always speak the same language.
When each system makes decisions from its own silo:
- Contradictory actions are generated.
- Alerts appear that are difficult to interpret.
- The context of why something happened is lost.
Automation, instead of simplifying the operation, fragments it.
Hyperautomation is a strategy that requires coordination and orchestration between technologies. Without that orchestration, the risk is adding layers of complexity instead of removing them.
More automation, more pressure on the infrastructure
There is another less visible, but very real, effect.
Advanced automation in the Data Center usually goes hand in hand with:
More AI-related workloads.
Higher rack density.
Increased energy consumption.
New thermal challenges.
Artificial intelligence and advanced systems don’t just optimize… they also consume. And that forces a rethink of design, operation, and energy management.
As various industry analyses point out, the adoption of AI is even changing ithe physical architecture of data centers, increasing operational complexity if not managed holistically.
Automating without a global vision can simply move the problem from one point of the system to another.
The real risk: automating without context
Automation does not fail on its own.
It fails when it relies on:
- Incomplete or inconsistent data.
- Poorly defined processes.
- Lack of traceability in decisions.
- Absence of an "operational awareness" of the whole.
An automated system is only as good as the context it understands.
And in a Data Center, context is everything.
When an automation acts without explaining why, when no one can reconstruct the chain of decisions, trust is broken. And without trust, automation ceases to be an ally.
So… to automate or not to automate?
The answer is not to slow down automation. It is to govern it.
In critical infrastructures, the key question should not be:
“What else can I automate?”
But rather:
“What makes sense to automate, with what data, and within what operational vision?”
Hyperautomation adds value when:
- Processes are clear and standardized.
- Data is reliable and consistent.
- Automated decisions can be explained.
- There is a unified vision of the Data Center as a whole.
Without that, the risk is clear: more technology, more chaos.
Final Notes
The Data Center of the future will not be the most automated one. It will be the one that best understands what is happening inside its own "box."
Technology should help simplify the operation, not hide it behind increasingly complex layers. Balancing automation and understanding is one of the great challenges for critical infrastructures in the coming years.