With the accelerated advance of digitalization and the exponential growth of corporate data, networks are no longer just technical infrastructure to become vital centers of the operation and strategy of Brazilian companies.Recent data from Gartner indicate that by 2027, more than 70% of large organizations in Brazil will depend 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 differential, but a strategic requirement for companies seeking resilience, agility and sustainable growth.And this movement paves the way for the era of Operational Intelligence (IO) (OI) IS a scenario in which 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 and tracking server metrics, network traffic, applications and security O.E., the IO concept today 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 agility in the response: problems and opportunities can be addressed at the exact moment they arise DO or even anticipated, as in the case of predictive maintenance. That is, instead of reacting to network incidents only after they impact users or operations, companies start to act in a preventive and data-driven way.
This posture decreases downtime, improves user experience and avoids operational losses. For example, in an IO-guided corporate network, a sudden latency spike at a critical link can generate an immediate alert and even trigger automatic routing adjustments before it becomes a major problem. Similarly, anomalous usage patterns can be detected continuously indicating need for extra capacity or potential security threats, allowing for instant corrective actions.
This concept aligns with what the IT market has called AIOps (Artificial Intelligence for IT Operations), integrating AI and automation to optimize IT operations and networks in an integrated and autonomous way.
AI, machine learning and automation in real-time network management
Integrating AI and machine learning with network automation enables enterprise 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 predictively and reinforce security in an automated way. AI tools analyze the volume of traffic data and adjust settings dynamically 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 at peak times. At the same time, intelligent systems can identify early signs of failure 'an atypical increase in packet loss or anomalous behavior in a router & act before the problem affects users, whether by restarting a piece of equipment, isolating a network segment or alerting support teams with an accurate diagnosis.
Security is also amplified by IO and intelligent automation. AI solutions monitor cyber threats in real time, filtering malicious traffic and applying mitigation measures automatically when they detect suspicious behavior.
Projections indicate that by 2026 at least 30% of companies will automate more than half of network management activities ^ a considerable leap from less than 10% that did so in 2023. This advance reflects the perception that only with intelligent automation will it be possible to manage the increasing degree of complexity of modern networks and meet the demands of the business in real time.
Implementation challenges
Despite the clear benefits, implementing and sustaining large-scale operational intelligence poses significant challenges for large enterprises. One of the main obstacles is technological in nature: the lack of data integration between legacy systems and tools. Many organizations still deal with “silos” of isolated data, 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 journey of operational intelligence. Another obvious barrier is the shortage of specialized labor. AI, machine learning and automation solutions require professionals with advanced technical skills I.E.T.E., data scientists capable of creating predictive models to network engineers able to program complex automations. According to market estimates, at least 73% of companies in Brazil do not have teams dedicated to AI projects, and about 30% attribute this absence directly to the lack of experts available in the market.
Another aspect that makes its implementation quite complex is the heterogeneity of enterprise environments, which can include multiple clouds (public, private, hybrid), a proliferation of Internet of Things (IoT) devices, distributed applications, and users connecting from multiple 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 analytics reflect the full reality of the network.
Resilience and evolution driven by operational intelligence
Given 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 disruptions can generate millionaire losses, and where agility and customer experience are competitive differentiators, the ability to monitor, learn and react in real time emerges as a major strategic 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 ability to continuously adapt: in the face of new market demands, advances such as 5G, or unexpected events, the smart network can evolve and rebuild quickly, sustaining innovation instead of slowing it down. Ultimately, dealing with the era of operational intelligence in networks is not only a matter of technical efficiency, but of ensuring that the company's digital infrastructure is able to learn, strengthen and guide the business into the future, with robustness and agility.
