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 in the enterprise, 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.

The Brazilian Retail Media market 42.3% is experiencing a growth boom. The market reached the number of R$ 3.8 billion last year, a jump of 42.3% over 2023 2023 20.3%.

And this movement has happened precisely because retailers and the industry are embracing this trend with speed so much so that the expectation is that this media channel ends 2025 with a significant growth compared to 2024. This demonstrates that the national retail is determined to become a protagonist in digital advertising, surfing the “third wave of online media (as retail media networks have been called. In other words, there is a growing consensus that retailers will become advertising powerhouses, with a central role in the connection between brands and consumers.

At least 64% of the major Brazilian brands already work with retail media, according to the Retail Media Insights survey, 2024. On the side of retailers, 5% claim to already operate their own media network (from supermarkets to pharmacies and marketplaces, several segments are creating structures to monetize their audiences.

The segmentation power of Retail

Behind the rise of retail media networks is a valuable asset of the retailer: the primary data (first-party data) of its consumers. Unlike other vehicles, retail holds rich information about buying behavior 2 transaction history, items viewed, frequency of visits, preferences and even loyalty program data. This information allows extremely accurate segmentation of the audience. Retailers can leverage the buying insights of their customers to offer hyper-targeted advertising solutions, reaching the right consumer with the right message and at the most opportune time.

This ability to segment based on own data gains strategic importance in a context of greater restriction to the use of third-party cookies and demand for privacy. Retailers, acting as “owners of the” audience, something difficult to find in other media on the same scale.

For example, a pharmacy chain can target vitamin ads only to customers who have recently purchased health products, or an online supermarket can promote organic foods to consumers looking for fitness items.The intelligent use of purchase history, searches and demographic profile makes ads much more relevant to the consumer, increasing sales and brand loyalty . Studies highlight that retail media offers just this possibility of mass customization, combining reach with tailored content for each customer.

In addition, the quality of retail data allows more robust performance metrics. As retail media networks operate within the retailer's own systems, it is possible to directly attribute the result of a campaign to sales made, closing the full measurement cycle. This assignment “closed loop ” in which it is possible to connect the impression of the ad to the transaction in the cashier 'is a great differential. The wealth of purchase data and the ability to attribute the return on investment directly make retail media a strategy highly valued by brands.

For advertisers, this means that investing in the retail channel is not a leap in the dark: on the contrary, sales results can be proven quickly and accurately, making it easier to justify investment and optimize campaigns in near real time.

Integration between digital and offline: direct impact on POS

An important aspect of retail media networks is the integration between the online and offline worlds. Some of the largest retailers operating in Brazil have a huge customer base both online and offline. This allows these companies to make a unique combination of channels to engage the consumer at multiple touch points during their purchase journey.

Another example: a customer may be impacted by a product banner in the mobile app of the supermarket and, when visiting the physical store, come across a personalized offer on a digital screen in the gondola or near the box. This synergy onoffline takes the advertising message up to the “ultimate mile” of the decision process, literally when the consumer has the product in hand.No wonder, experts see retail media as a way to influence consumer choice at the critical moment of purchase.

Inside stores, in-store digital media has been gaining ground as an extension of retail chains.Smart screens, interactive totems, electronic shelf panels (ESLs) and even monitors in the shopping cart become advertising inventory. Retailers can strategically position these screens near checkouts or high-circulation aisles to stimulate last-minute purchases.

It is logical that, from an operational point of view, the integration between online and offline requires a technological effort of measurement: unifying the two means. This has still been a challenge for retailers, whose solution has been customization through increasingly improved loyalty campaigns. Even if there are still technological issues, the direction is clear: the future of retail media is to offer a cohesive omnichannel experience, where it matters little whether the interaction happened in the virtual world or in the physical world 5 both environments complement each other to engage the consumer and generate results for brands.

Paradigm shift: from sales channel to media channel

The emergence of Retail Media Networks represents a paradigm shift in how retail is viewed in the marketing mix. Historically, retailers were seen only as distribution channels and points of sale, while brand building and advertising were in charge of traditional media vehicles, more recently of digital platforms. With the turn to retail media, this separation falls apart: retail is now also a vehicle for mass communication, starting to compete for advertising budgets that would previously go to other media.

In practice, large retail chains have become true publishersmonetizing your websites, apps, and stores just like a news portal lives off ads or a TV station sells commercial space.

For the advertising brands, this represents a reconfiguration of the strategies. A part of the investment that was previously intended for trade marketing actions at the physical point of sale migrates to media actions in the digital properties of the retailer. Another portion, which would go to generic mass media, can now be allocated more focused via retail media, reaching exactly the shopper in the“moment of the truth of the purchase.

This convergence makes marketing and trade come together, requiring managers to think in an integrated way: selling and communicating have become facets of the same consumer journey. As a result, large global advertisers are already reorganizing teams and budget to contemplate this new pillar. Some call this movement “mediafication” retail (that is, retail is no longer just distribution and also becoming media.

If before supermarkets, pharmacies and department stores were only the stage for other media strategies, now they have their own spotlight. This model redefines not only investment flows, but also requires new approaches from all market players. Brands need to be more data-driven and performance-oriented, agencies need to incorporate new knowledge and skills, and retailers take on media company responsibilities, ensuring consumer experience also in terms of content and ad relevance.

The advertising ecosystem expands and becomes more complex (but at the center of this transformation, there is a clear logic: those who are closer to the consumer in the buying journey gain voice and value in the media game. Retail, with its own platforms, has proven to be in the right place and at the right time to capitalize on this dynamic. It remains for other parts of the market to adapt to this new paradigm, integrating retail media in their strategies so as not to be left behind in this evolution that, it seems, has come to stay.

In a scenario marked by the acceleration of digital transformation, the systems of Enterprise Resource Planning (ERP) consolidate as strategic foundations to boost operational efficiency. More than management tools, these platforms evolve into intelligent ecosystems, integrating disruptive technologies such as cloud, Internet of Things (IoT) and Artificial Intelligence (AI) to respond to the demands of a hyperconnected market.

Initially focused on transactional stability and data integrity, ERP has become a strategic element, shaping the digital transformation journey of companies.In a combined scenario of historical robustness and with new analytical capabilities, embedded intelligence and automation journeys, ERP becomes a cog for innovation, making room for a new approach to services.

Transition to cloud-based ERP

Migration to models cloud-based gartner data indicates that 85% of large enterprises will adopt cloud ERP by the end of 2025, driven by advantages such as dynamic scalability, reduced operating costs and continuous upgrades. Eliminating hardware investments and ensuring remote access with integrated disaster recovery transforms business agility, enabling organizations of all sizes to adapt to market fluctuations in real time.

Universal mobile access

Demand for ubiquitous access requires ERPs to transcend physical boundaries.Rugged mobile features, with intuitive interfaces similar to consumer-grade applications, allow employees to approve production orders, track financial metrics, or manage supply chains directly from smartphones. This portability not only eliminates logistical bottlenecks, but also synchronizes critical decisions with the speed of modern business.

Embedded Business Intelligence

The era of intuition-based decision-making is gradually coming to an end. Contemporary ERP platforms incorporate predictive analytics and interactive dashboards, consolidating as single sources of truthby integrating data visualizations and self-service reports, they eliminate fragmentation between systems and deliver actionable insights from cost optimizations to demand forecasts. According to Grand View Research, this move will contribute to the ERP market reaching US$ 64.83 billion by 2025, with annual growth of 11.7%.

AI and Machine Learning in process autonomy

Machine learning algorithms are rewriting the logic of ERPs. By analyzing historical and behavioral patterns, these solutions not only automate repetitive tasks, but anticipate failures in production lines, customize workflows, and refine tax forecasts with increasing accuracy.Forbes projects that by 2025, more than 90% of enterprise applications will integrate AI, a leap that redefines human-machine interaction, transferring reactive functions to cognitive systems.

Connecting smart businesses with IoT

The convergence between ERP and Internet of Things materializes the vision of smart enterprisesensors embedded in physical assets, from industrial machines to logistics vehicles, feed systems with real-time data, allowing algorithms to detect anomalies, adjust delivery routes or optimize energy consumption autonomously. This interaction between physical and digital worlds not only eliminates manual intermediaries, but creates virtuous cycles where each operation generates intelligence for the next.

The future is already contextual

Even with all the benefits, ERP transformation still presents a key challenge, which is perceived cost x delivered value. There are still challenges of perception about return on investment (ROI), especially for companies that adopt migration in a partial or conservative way.

Looking ahead, the tools that support the upgrade with the growing maturity and consolidation of practices such as clean core and cloud-first strategy, the scenario becomes more promising for companies that decide to move forward.

While traditional ERPs were limited to recording transactions, the new generations of these systems act as digital orchestratorsthe combination of cloud computing, ubiquitous mobility and prescriptive analytics draws a picture where efficiency ceases to be metric to become a continuous, adaptive, proactive and, above all, invisible process. For companies that aim for digital maturity, the message is clear: integrate or lag behind.