With the accelerated advancement of digitalization and the exponential growth of corporate data, networks have ceased to be merely technical infrastructure and have transformed into vital hubs for the operation and strategy of Brazilian companies. Recent data from Gartner indicates that by 2027, over 70% of large organizations in Brazil will rely directly on operational intelligence applied to networks to maintain their competitive edge and operational security.
In this context, the intelligent use of automation, machine learning, and real-time analytics is no longer just 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 (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 OI now extends to practically 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 response agility: issues and opportunities can be addressed the 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 can take preventive, data-driven actions.
This approach reduces downtime, improves user experience, and prevents operational losses. For example, in an OI-driven corporate network, a sudden latency spike in a critical link can trigger an immediate alert and even automate routing adjustments before it escalates into a larger problem. Similarly, anomalous usage patterns can be continuously detected – indicating the need for extra capacity or possible 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 and network operations in an integrated and autonomous manner.
AI, machine learning, and automation in real-time network management
The integration of AI and machine learning with network automation allows corporate infrastructure to become more intelligent 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, detect failures predictively, and reinforce security automatically. AI tools analyze traffic data volumes 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 early signs of failure – such as atypical packet loss or anomalous router behavior – and act before the issue impacts users, whether by rebooting a device, isolating a network segment, or alerting support teams with a precise diagnosis.
Security is also enhanced by OI and intelligent automation. AI-powered solutions monitor cyber threats in real-time, filtering malicious traffic and automatically applying mitigation measures when suspicious behavior is detected.
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% in 2023. This advancement reflects the realization that only with intelligent automation will it be possible to manage the growing complexity of modern networks and meet business demands in real-time.
Implementation challenges
Despite the clear benefits, implementing and sustaining operational intelligence at scale poses significant challenges for large companies. One major obstacle is technological: 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 operational intelligence journey. Another evident barrier is the shortage of specialized talent. 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 lack dedicated AI teams, and about 30% attribute this directly to the lack of available experts in the market.
Another aspect that makes implementation highly 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 OI platforms into this fragmented environment requires not only investment in compatible tools but also careful architectural planning to connect diverse data sources and ensure 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 disruptions can result in 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 strengthens the organization’s capacity for continuous adaptation: faced with new market demands, advancements like 5G, or unexpected events, an intelligent network can evolve and recover quickly, supporting innovation rather than hindering it. Ultimately, navigating the era of operational intelligence in networks is not just a matter of technical efficiency but ensuring that the company’s digital infrastructure is capable of learning, strengthening, and guiding the business toward the future with robustness and agility.
