Smarter operations, faster decisions: the impact of agentic AI on IT routines

The new dynamics of IT operations

The routine of technology departments has changed significantly in recent years. However, it wasn't just cloud adoption or process digitization that shifted the center of operations. The main factor was the increase in the volume of decisions required in real time.

Today, in distributed environments with multiple applications and integrations, the challenge is no longer just seeing what's happening. Now, the problem is deciding fast enough to keep up with this complexity.

Moreover, this scenario puts direct pressure on teams. That's because operational speed has become as important as technical capability.

The new role of artificial intelligence in IT

Given this context, the discussion about artificial intelligence has gained a new layer. In other words, it is no longer just about analyzing data or generating responses.

Now, the focus is on reducing the time between detecting an event and acting on it. This is exactly where agentic AI begins to generate real impact on operations.

According to McKinsey studies, about 60% of business activities can already be automated with current technologies. Therefore, more than productivity, this figure reveals something important: much of the work in IT depends on fast, consistent execution.

The problem of microdecisions in operations

When we look at the IT routine, it becomes clear that most of the time is not spent on strategic decisions. On the contrary, it is consumed by repetitive microdecisions.

For example:

• Classifying alerts
• Correlating events
• Assessing impacts
• Executing runbooks
• Adjusting cloud resources

Although these tasks are individually simple, the problem lies in the volume and frequency with which they occur.

As a result, this generates fragmented attention. Teams end up reacting to tickets, dashboards, and multiple tools, which increases operational noise and reduces efficiency.

Agentic AI: a new operational logic

This is precisely where agentic AI stands out. Unlike traditional automation, it does not just execute isolated tasks. Instead, it connects steps, understands context, and makes chained decisions.

In practice, this transforms operations.

A simple alert stops being just a signal. It starts to trigger a complete flow, which can include:

• Automatic correlation
• Root cause identification
• Action suggestion
• Automated execution

Thus, what once required multiple systems and human intervention now happens in a single cycle.

Time reduction and efficiency gains

As a result, there is a significant reduction in the time between understanding and acting. In critical environments, this directly impacts indicators such as:

• Response time (MTTR)
• Service availability
• User experience

In addition, there is an important benefit: the reduction of cognitive load.

In other words, by eliminating repetitive tasks and centralizing context, agentic AI allows teams to work with more focus and fewer interruptions.

Shift in the role of IT teams

With this evolution, the role of teams also changes. Instead of acting only in execution, professionals start focusing on:

• Process definition
• Governance
• Operational policies

In other words, the goal is no longer to “solve everything manually” but to “ensure the system solves it correctly.”

Autonomy with control: the point of attention

Despite the benefits, adopting agentic AI requires maturity. After all, it's not just about automating.

It is essential to ensure:

• Traceability
• Execution control
• Well-defined limits

Therefore, the question is not whether AI can act, but under what conditions that action is reliable.

Gradual evolution, continuous impact

Another important point is that this transformation does not require disruption. On the contrary, agentic AI integrates with existing systems.

Initially, it operates on simple tasks, such as triage and correlation. Over time, however, it evolves toward:

• Runbook execution
• Continuous optimization
• Automated operational decisions

Thus, the impact is progressive, yet cumulative.

Conclusion: speed as a competitive advantage

In the end, the discussion about agentic AI is not just technological. It is operational.

Companies that manage to reduce the time between identifying and resolving problems operate with greater efficiency, lower costs, and more predictability.

Therefore, in a scenario of growing complexity, this capability stops being a differentiator. It becomes essential.

The transformation has already begun. Now, the real challenge is the speed at which each organization will be able to adopt it without losing control over its operations.