Retail has fully entered the Artificial Intelligence era but has yet to build the necessary foundation to sustain this transformation. The combination of new generative models, advanced automations, and the emergence of so-called “superagents” has accelerated the race for efficiency and personalization. However, this advancement is happening at a much faster pace than companies' ability to organize governance, security, and architecture. It is this mismatch that concerns Marcos Oliveira Pinto, Global Software Engineering Manager at Jitterbit, who directly oversees e-commerce operations for major retailers in Brazil and Latin America.
In his view, the sector is experiencing a moment of enthusiasm, but not maturity. “We are seeing many companies trying to get ahead, but leaving essential pieces behind,” he states. Although companies with established structures enterprise are somewhat more prepared, most of retail still operates on the logic of “act first, organize later.” According to Marcos, this path has a short timeframe before it exacts its price. “I would say something between 12 and 18 months. I believe that, certainly, within 12 months we will already begin to face a problem with the management and security of this,” he exclaims.
And Marcos's warning is not abstract. With the ease of creating AI agents within business areas, and not just in IT, companies are beginning to accumulate automations without traceability, documentation, or control. Different teams create agents disconnected from each other, without standards and without technical supervision. In a short time, no one knows how many agents exist, what data they access, how they interact with each other, or what risks they represent. The situation resembles the period of explosion of improvised integrations at the beginning of iPaaS, when disorganized environments became costly and difficult to sustain. Now, this scenario is repeating on a much larger scale, amplified by the autonomy brought by AI.
The speed of technological evolution also aggravates the problem, meaning companies may be operating agents with outdated implementations and techniques without even realizing it. Furthermore, the threat of attacks known as prompt injection, is growing, where criminals send malicious prompts with the intent to cause unintended behaviors and force unwanted actions within critical retail operations.
Despite the risks, AI's potential to transform retail operations is enormous. Marcos cites, for example, situations where an e-commerce faces checkout instabilities. Instead of losing sales, a superagent can analyze the customer's history, authorize the continuation of the purchase, and process the data later, avoiding disruptions and reputation damage. In another scenario, an agent can absorb orders while the internal system is down, ensuring operational continuity and preventing momentary failures from turning into revenue loss.
There are also established applications in dynamic pricing and competitor monitoring, where agents analyze the market in real-time and suggest adjustments that keep brands competitive. And, in customer service, agents specialized in sentiment analysis already allow for identifying customer mood, frustration, and expectations, paving the way for preventive actions in the customer experience.
To avoid risks and operate with guaranteed safety and efficiency, Marcos explains that Jitterbit has been investing in superagent architectures capable of orchestrating different expert agents—from inventory, logistics, finance, marketing, or customer service—within a single intelligent and secure environment. The response reaches the end user in an integrated manner, without them realizing which agent is operating. According to Marcos, no other company currently delivers this approach in the market, which accelerates the adoption of complex use cases and reduces technical dependencies. Regarding the constant updating of technology, the global company, which also operates in Brazil, has a group of engineers focused on AI, who are constantly testing tools and new solutions.
Even so, he emphasizes that technology does not replace maturity. Part of retail's strategic delay lies in the difficulty of clearly defining which problem it wants to solve. Therefore, the adoption of quick win, projects is growing, which solve specific pains, deliver quick value, and help companies gain clarity on where to scale AI for more complex use cases. Simultaneously, the need for new roles in the market is emerging, such as the “AI validator,” a professional responsible for reviewing agents' critical decisions, preventing hallucinations, and ensuring systems act within company policies.
The accelerated adoption of AI is not an optional choice, but a competitive urgency, as the expert warns. However, the lack of governance and planning can turn this race into a significant operational liability. In Marcos's words, the sector has up to 18 months to structure its foundation, before the overload of uncontrolled agents, outdated technologies, and security risks become visible and costly problems. Maturity, therefore, is not just the next step, but the fundamental condition for AI to deliver the value it promises.
