en.Wedoany.com Reported - Brazil's free energy market officially opened on August 5, ushering the power sector into a new phase defined by scale, efficiency pressure, and deep digital transformation. The expansion of the free market to smaller consumers requires energy suppliers to make structural adjustments to their operating models—user numbers are growing exponentially while average ticket sizes and profit margins are narrowing, making automation a necessity rather than an option.
Against this backdrop, artificial intelligence has been placed at the core of the new operational architecture. Once serving millions of small consumers, a business model without automation support is economically unviable. What the industry needs is not just automation, but intelligent automation that can operate while maintaining decision quality and customer trust.
The market opening will multiply the number of eligible consumers, requiring suppliers to have the capacity for massive data processing, large-scale simulation generation, and fully digital customer journeys. This process coincides with global efficiency pressures, with the industry needing billions in investment by 2050 to sustain the energy transition.
Boston Consulting Group (BCG), in its cost transformation study (the Drastic Cost-Out methodology), states that well-executed digitalization and automation projects can reduce operational and administrative costs by 15% to 20% within approximately 18 months. A series of real-world cases in customer service illustrates the boundaries of this potential.
International cases simultaneously reveal the potential and risks of transformation. Fintech company Klarna used AI to automate approximately two-thirds of its customer service operations, expecting efficiency gains of $40 million; however, in more complex service scenarios, the company saw a notable decline in customer satisfaction, and due to negative impacts on customer experience, had to reintroduce human support.
iFood, in turn, demonstrates a relatively balanced approach. The company uses AI as a triage and support mechanism rather than a full replacement for human involvement, automating about 45% of interactions while reducing delivery-related operational costs by over 70%.
In the financial sector, Nubank has built a more sophisticated architecture. The institution resolves approximately 55% of initial customer interactions through AI, cutting response times by about 70%, and maintains high security standards and customer experience by combining automation with multi-layered verification.
In the energy sector, Octopus Energy, through its proprietary Kraken platform, has reduced customer service costs by up to 40% and expanded its customer base to tens of millions of users globally, demonstrating how a vertically integrated, data-driven technology architecture supports business scale.
The difference between these cases lies in how AI is deployed. When used solely to cut costs, AI delivers short-term efficiency but undermines long-term value; when used to enhance operational capabilities, AI creates sustainable competitive advantage. This is directly linked to the concept of AI as an "invisible layer"—the elements that do not appear directly in operational metrics but ultimately determine business success, including customer trust, decision quality, and process governance.
Investment misallocation often exacerbates these issues. BCG's model indicates that AI investment should allocate only 10% to algorithms, 20% to data and infrastructure, and 70% to operating models and governance; yet many companies still concentrate resources on technology procurement without restructuring processes.
The customer journey in the power sector involves multiple layers of complexity, and without a well-structured operating model, AI cannot solve problems—it will only accelerate them. Under this model, artificial intelligence acts as a scaling engine, completing invoice analysis, consumption assessment, simulations, and proposal generation within seconds; meanwhile, key decisions involving long-term and more complex contracts undergo human verification.
An implementation approach combining automation with human oversight can reduce algorithmic errors, improve conversion rates, enhance customer retention, and ensure greater regulatory security. AI will not replace advisors; rather, by eliminating operational tasks, it allows humans to focus on areas that truly create value, thereby transforming the role of advisors.
