With the accelerated advance of digitalization and the exponential growth of corporate data, networks have ceased to be just technical infrastructure to 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 directly depend 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. This movement paves the way for the era of Operational Intelligence (IO) – 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 practically any operational activity of the company, thanks to the proliferation of sensors, connected devices, and various data sources.
The main benefit of this real-time intelligence is agility in response: problems and opportunities can be addressed the moment they arise – 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 preventively and data-driven.
This posture reduces downtime, improves user experience, and prevents operational losses. For example, in an IO-guided corporate network, a sudden latency spike on a critical link can trigger an immediate alert and even activate automatic routing adjustments before it becomes a major issue. Similarly, anomalous usage patterns can be continuously detected – indicating the need for extra capacity or possible security threats – allowing for instant corrective actions.
This concept aligns with what the IT market has termed AIOps (Artificial Intelligence for IT Operations), integrating AI and automation to optimize IT and network operations in an integrated and autonomous way.
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 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, predictively detect failures, and autonomously enhance security. AI tools analyze traffic data and dynamically adjust configurations 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 signs of failure in advance – such as a sudden increase in packet loss or anomalous behavior in a router – and take action before the issue affects users, whether by restarting equipment, isolating a network segment, or alerting support teams with a precise diagnosis.
Security is also enhanced by IO and intelligent automation. AI solutions monitor cyber threats in real-time, filtering malicious traffic and automatically applying mitigation measures when detecting suspicious behavior.
Projections indicate that by 2026, at least 30% of companies will automate more than half of network management activities – a significant leap from less than 10% doing so in 2023. This advancement reflects the understanding 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 clear benefits, implementing and sustaining operational intelligence on a large scale poses significant challenges for large companies. One of the main obstacles is technological in nature: the lack of data integration between legacy systems and tools. Many organizations still deal with isolated data ‘silos,’ making 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 evident barrier lies in the scarcity of specialized workforce. AI, machine learning, and automation solutions demand professionals with advanced technical skills – from data scientists capable of creating predictive models to network engineers adept at programming complex automations. According to market estimates, at least 73% of companies in Brazil do not have dedicated teams for AI projects, and around 30% attribute this absence directly to the lack of available specialists in the market.
Another aspect that makes its implementation quite complex is the heterogeneity of corporate environments, which may 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 various data sources and ensure that the analyses reflect the complete 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 interruptions can lead to million-dollar losses, and where agility and customer experience are competitive differentiators, the ability to monitor, learn, and react in real-time emerges as a strategic factor of great importance. 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 enhances the organization’s capacity for continuous adaptation: faced with new market demands, advancements such as 5G, or unexpected events, the intelligent network can evolve and recompose quickly, sustaining innovation rather than hindering it. Ultimately, dealing with the era of operational intelligence in networks is not just a matter of technical efficiency but ensuring that the company’s digital infrastructure can learn, strengthen, and guide the business towards the future with robustness and agility.
