With the accelerated advancement of digitalization and the exponential growth of corporate data, networks have ceased to be just technical infrastructure and have become vital centers of operation and strategy for Brazilian companies. Recent data from Gartner indicates that by 2027, more than 70% of large organizations in Brazil will rely directly on operational intelligence applied to networks to maintain their competitive advantage and operational security.
In this context, the intelligent use of automation, machine learning, and real-time analysis becomes not only a differentiator but a strategic requirement for companies seeking resilience, agility, and sustainable growth. And this movement paves the way for the era of Operational Intelligence (OI) – a scenario where decisions and adjustments occur in real time, guided by comprehensive data and intelligent automation within corporate networks.
Operational Intelligence: real-time decisions
Originally applied to the IT sphere – monitoring server metrics, network traffic, applications, and security – the concept of IO now extends to virtually any operational activity of the company, thanks to the proliferation of sensors, connected devices, and diverse data sources.
The main benefit of this real-time intelligence is the speed of response: problems and opportunities can be addressed at the exact moment they arise – or even anticipated, as in the case of predictive maintenance. In other words, instead of reacting to network incidents only after they impact users or operations, companies start to act proactively and data-driven.
This posture reduces downtime, improves user experience, and prevents operational losses. For example, in an IO-driven corporate network, a sudden spike in latency on a critical link can trigger an immediate alert and even initiate automatic routing adjustments before it becomes a bigger problem. Similarly, abnormal usage patterns can be continuously detected – indicating the need for additional capacity or potential security threats – enabling instant corrective actions.
This concept aligns with what the IT market has been calling AIOps (Artificial Intelligence for IT Operations), integrating AI and automation to optimize IT operations and networks in an integrated and autonomous manner.
AI, machine learning, and automation in real-time network management
The integration of AI and machine learning into network automation allows corporate infrastructure to become smarter and more autonomous, adjusting parameters in real time to optimize performance and security.
With AI, network automation reaches a new level of sophistication. Networks equipped with intelligent algorithms can optimize their own performance, detect failures proactively, and enhance security automatically. AI tools analyze traffic data volume and dynamically adjust settings to maximize efficiency without the need for direct human intervention.
This means, for example, calibrating bandwidths, traffic priorities, or alternative routes according to network conditions, ensuring high performance even during peak times. At the same time, intelligent systems can identify early signs of failure—such as an atypical increase in packet loss or anomalous behavior in a router—and act before the problem affects users, whether by restarting equipment, isolating a network segment, or alerting support teams with an accurate diagnosis.
Security is also enhanced by IO and intelligent automation. AI solutions monitor cybersecurity threats in real time, filtering malicious traffic and automatically applying mitigation measures when suspicious behaviors are detected.
Projections indicate that by 2026, at least 30% of companies will automate more than half of their network management activities – a significant jump from less than 10% doing so in 2023. This advance reflects the perception that only with intelligent automation will it be possible to manage the increasing complexity of modern networks and meet business demands in real time.
Implementation challenges
Despite the clear benefits, implementing and sustaining large-scale operational intelligence presents significant challenges for large companies. One of the main obstacles is technological in nature: the lack of data integration between systems and legacy tools. Many organizations still deal with isolated data "silos," which makes it difficult to obtain a unified view of network operations.
Integrating heterogeneous systems and unifying data sources is a mandatory step in the operational intelligence journey. Another obvious barrier is the shortage of specialized labor. AI, machine learning, and automation solutions require professionals with advanced technical skills – from data scientists capable of creating predictive models to network engineers skilled in programming complex automations. According to market estimates, at least 73% of companies in Brazil do not have dedicated AI project teams, and about 30% directly attribute this absence to the lack of available specialists in the market.
Another aspect that makes its implementation quite complex is the heterogeneity of corporate environments, which can include multiple clouds (public, private, hybrid), a proliferation of Internet of Things (IoT) devices, distributed applications, and users connecting from various locations and networks (especially with remote and hybrid work).
Integrating IO platforms into this fragmented environment requires not only investment in compatible tools but also careful architectural planning to connect diverse data sources and ensure that analyses reflect the complete reality of the network.
Resilience and evolution driven by operational intelligence
In light of all this, it is clear that operational intelligence is not just another technological trend; it has become an essential pillar for the resilience and evolution of corporate networks.
In a business environment where service interruptions can cause millions in losses, and where agility and customer experience are competitive differentiators, the ability to monitor, learn, and react in real time emerges as a strategically significant factor. By adopting real-time analytics, automation, and AI in a coordinated manner, companies can elevate their network operations to a new level of intelligence and resilience.
This is an investment that reinforces the organization's continuous adaptability: in the face of new market demands, advances like 5G, or unexpected events, the intelligent network can evolve and recover quickly, supporting innovation rather than hindering it. Ultimately, dealing with the era of operational intelligence in networks is not just a matter of technical efficiency, but of ensuring that the company's digital infrastructure is capable of learning, strengthening itself, and guiding the business towards the future with robustness and agility.
