Com o avanço acelerado da digitalização e o crescimento exponencial de dados corporativos, as redes deixaram de ser apenas infraestrutura técnica para se transformar em centros vitais da operação e da estratégia das empresas brasileiras. Dados recentes do Gartner indicam que até 2027, mais de 70% das grandes organizações no Brasil dependerão diretamente de inteligência operacional aplicada às redes para manter sua vantagem competitiva e segurança operacional.

Nesse contexto, o uso inteligente de automação, aprendizado de máquina e análise em tempo real torna-se não apenas um diferencial, mas uma exigência estratégica para empresas que buscam resiliência, agilidade e crescimento sustentável. E esse movimento abre caminho para a era da Inteligência Operacional (IO) – um cenário em que decisões e ajustes ocorrem em tempo real, guiados por dados abrangentes e automação inteligente dentro das redes corporativas.

Inteligência Operacional: decisões em tempo real

Originalmente aplicado à esfera de TI – acompanhando métricas de servidores, tráfego de rede, aplicações e segurança –, o conceito de IO se estende, hoje, a praticamente qualquer atividade operacional da empresa, graças à proliferação de sensores, dispositivos conectados e fontes de dados diversas.

O principal benefício dessa inteligência em tempo real é a agilidade na resposta: problemas e oportunidades podem ser abordados no exato momento em que surgem – ou até mesmo antecipados, como no caso de manutenção preditiva. Ou seja, em vez de reagir a incidentes de rede somente depois que eles impactam usuários ou operações, as empresas passam a atuar de forma preventiva e orientada por dados.

Essa postura diminui tempos de indisponibilidade, melhora a experiência dos usuários e evita prejuízos operacionais. Por exemplo, em uma rede corporativa guiada por IO, um pico súbito de latência em um link crítico pode gerar um alerta imediato e até acionar ajustes automáticos de roteamento antes de se tornar um problema maior. Da mesma forma, padrões de uso anômalos podem ser detectados continuamente – indicando necessidade de capacidade extra ou possíveis ameaças de segurança –, permitindo ações corretivas instantâneas.

Esse conceito se alinha ao que o mercado de TI tem chamado de AIOps (Artificial Intelligence for IT Operations), integrando IA e automação para otimizar operações de TI e redes de forma integrada e autônoma.

IA, machine learning e automação na gestão de redes em tempo real

A integração de IA e machine learning à automação de redes permite que a infraestrutura corporativa se torne mais inteligente e autônoma, ajustando parâmetros em tempo real para otimizar desempenho e segurança.

Com a IA, a automação de rede atinge um novo patamar de sofisticação. As redes dotadas de algoritmos inteligentes conseguem otimizar o próprio desempenho, detectar falhas de forma preditiva e reforçar a segurança de modo automatizado. Ferramentas de IA analisam o volume de dados de tráfego e ajustam configurações dinamicamente para maximizar a eficiência, sem necessidade de intervenção humana direta.

Isso significa, por exemplo, calibrar larguras de banda, prioridades de tráfego ou rotas alternativas conforme as condições da rede, garantindo alta performance mesmo em momentos de pico. Ao mesmo tempo, sistemas inteligentes conseguem identificar antecipadamente indícios de falha – um aumento atípico de perda de pacotes ou um comportamento anômalo em um roteador – e agir antes que o problema afete os usuários, seja reiniciando um equipamento, isolando um segmento da rede ou alertando as equipes de suporte com um diagnóstico preciso.

A segurança também é amplificada pela IO e pela automação inteligente. Soluções com IA monitoram ameaças cibernéticas em tempo real, filtrando tráfego malicioso e aplicando medidas de mitigação automaticamente quando detectam comportamentos suspeitos.

Projeções indicam que até 2026 pelo menos 30% das empresas vão automatizar mais da metade das atividades de gerenciamento de rede – um salto considerável frente a menos de 10% que faziam isso em 2023. Esse avanço reflete a percepção de que somente com automação inteligente será possível administrar o crescente grau de complexidade das redes modernas e atender às demandas do negócio em tempo real.

Desafios de implementação

Apesar dos benefícios claros, implementar e sustentar a inteligência operacional em larga escala traz desafios significativos para grandes empresas. Um dos principais entraves é de natureza tecnológica: a falta de integração de dados entre sistemas e ferramentas legadas. Muitas organizações ainda lidam com “silos” de dados isolados, o que dificulta obter uma visão unificada das operações da rede.

Integrar sistemas heterogêneos e unificar fontes de dados é um passo obrigatório na jornada da inteligência operacional. Outra barreira evidente está na escassez de mão de obra especializada. As soluções de IA, machine learning e automação exigem profissionais com competências técnicas avançadas – desde cientistas de dados capazes de criar modelos preditivos até engenheiros de rede aptos a programar automações complexas. De acordo com estimativas de mercado, ao menos 73% das empresas no Brasil não possuem equipes dedicadas a projetos de IA, e cerca de 30% atribuem essa ausência diretamente à falta de especialistas disponíveis no mercado.

Outro aspecto que torna sua implementação bastante complexa é a heterogeneidade dos ambientes corporativos, que podem incluir múltiplas nuvens (pública, privada, híbrida), uma proliferação de dispositivos da Internet das Coisas (IoT), aplicativos distribuídos e usuários conectando-se de vários locais e redes (especialmente com o trabalho remoto e híbrido).

Integrar plataformas de IO a esse ambiente fragmentado requer não só investimento em ferramentas compatíveis, mas também planejamento arquitetural cuidadoso para conectar fontes de dados diversas e garantir que as análises reflitam a realidade completa da rede.

Resiliência e evolução impulsionadas por inteligência operacional

Diante de tudo isso, fica claro que a inteligência operacional não é apenas mais uma tendência tecnológica; se tornou um pilar essencial para a resiliência e a evolução das redes corporativas.

Em um ambiente de negócios onde interrupções de serviço podem gerar prejuízos milionários, e onde a agilidade e a experiência do cliente são diferenciais competitivos, a capacidade de monitorar, aprender e reagir em tempo real desponta como fator estratégico de grande peso. Ao adotar análises em tempo real, automação e IA de maneira coordenada, as empresas podem elevar suas operações de rede a um novo nível de inteligência e resiliência.

Esse é um investimento que reforça a capacidade de adaptação contínua da organização: diante de novas demandas de mercado, de avanços como 5G, ou de eventos inesperados, a rede inteligente consegue evoluir e se recompor rapidamente, sustentando a inovação em vez de freá-la. Em última análise, lidar com a era da inteligência operacional em redes não é apenas uma questão de eficiência técnica, mas de garantir que a infraestrutura digital da empresa seja capaz de aprender, se fortalecer e guiar o negócio rumo ao futuro, com robustez e agilidade.

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 network 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 if 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 the way 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, systems Enterprise Resource Planning (ERP) are consolidating themselves as strategic foundations for driving operational efficiency. More than just management tools, these platforms are evolving into intelligent ecosystems, integrating disruptive technologies such as the cloud, the Internet of Things (IoT), and Artificial Intelligence (AI) to meet the demands of a hyperconnected market.

Initially focused on transactional stability and data integrity, ERP has become a strategic element, shaping companies' digital transformation journeys. In a combined scenario of historical robustness and new analytical capabilities, embedded intelligence, and automation journeys, ERP is transforming itself into a driving force for innovation, paving the way for a new approach to services.

Transition to cloud-based ERP

Migration to models cloud-based redefines business infrastructure. Gartner data indicates that 851% of large companies will adopt cloud-based ERP by the end of 2025, driven by advantages such as dynamic scalability, reduced operating costs, and continuous updates. Eliminating hardware investments and ensuring remote access, with integrated disaster recovery, transform business agility, enabling organizations of all sizes to adapt to market fluctuations in real time.

Universal mobile access

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

Business Intelligence Embedded

The era of intuition-based decision-making is gradually coming to an end. Contemporary ERP platforms incorporate predictive analytics and interactive dashboards, cementing their place as single sources of truthBy integrating data visualizations and self-service reporting, they eliminate system fragmentation and provide actionable insights, from cost optimization to demand forecasting. According to Grand View Research, this movement will contribute to the ERP market reaching US$1.4T 64.83 billion by 2025, with annual growth of 11.7T 3T.

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 also anticipate production line failures, personalize workflows, and refine fiscal forecasts with increasing accuracy. Forbes projects that by 2025, more than 90% of enterprise applications will integrate AI, a leap that redefines the interaction between humans and machines, transferring reactive functions to cognitive systems.

Connecting Smart Businesses with IoT

The convergence between ERP and the 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 the 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: perceived cost versus value delivered. Perceived return on investment (ROI) remains a challenge, especially for companies that adopt migration only partially or conservatively.

Looking ahead, the tools that support the update with increasing maturity and the 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, new generations of these systems act as digital orchestratorsThe combination of cloud computing, ubiquitous mobility, and prescriptive analytics paints a picture where efficiency ceases to be a metric and becomes a continuous, adaptive, proactive, and, above all, invisible process. For companies striving for digital maturity, the message is clear: integrate or be left behind.