*By Gustavo Karman, Chief Product Officer (CPO) at Lead Energy
Aug. 5 – The opening of Brazil’s Free Energy Market marks a new phase for the electricity sector, characterized by scale, pressure for efficiency, and the need for deep digital transformation. The expansion of the free market environment to smaller consumers requires a structural shift in the operating model of energy suppliers. The challenge is now to serve an exponentially larger customer base, with lower average ticket values and tighter margins, making automation not only relevant but essential.
In this context, Artificial Intelligence establishes itself as a central pillar of this new operational architecture.
AI is fundamental to the future of the free energy market. Without automation, the model simply does not become economically viable once you begin serving millions of smaller consumers. The point is that automation alone is not enough. It must be intelligent automation, preserving the quality of decision-making and customer trust.
The market opening is expected to multiply the number of eligible consumers, requiring the ability to process large volumes of data, generate simulations at scale, and deliver fully digital customer journeys. This movement is taking place alongside global pressure for greater efficiency, in a sector that will require billions in investments by 2050 to sustain the energy transition.
Within this scenario, according to a Boston Consulting Group study on cost transformation (the Drastic Cost-Out methodology), well-executed digitalization and automation programs can reduce operational and administrative costs by 15% to 20% over a period of approximately 18 months. In customer service, the following cases illustrate this potential in practice.
International cases highlight both the potential and the risks of this transformation. The fintech company Klarna, for example, managed to automate around two-thirds of its customer service operations with AI and projected US$40 million in efficiency gains. However, the company experienced a significant decline in customer satisfaction in more complex cases and had to reintroduce human support after negative impacts on the customer experience.
On the other hand, companies such as iFood demonstrate a more balanced approach. The company has automated approximately 45% of interactions and reduced delivery-related operational costs by more than 70% by using AI as a triage and support mechanism, rather than as a complete replacement for human involvement.
In the financial sector, Nubank has built an even more sophisticated architecture. The institution is able to resolve around 55% of initial customer interactions through AI and has reduced response times by approximately 70%, while maintaining high standards of security and customer experience by combining automation with multiple layers of validation.
In the energy sector, Octopus Energy, through its proprietary Kraken platform, has managed to reduce customer service costs by up to 40% and scale its customer base to tens of millions of users globally, demonstrating the impact of a vertically integrated, data-driven technology architecture.
The difference between these cases lies in how AI has been applied. When it is used solely to reduce costs, it creates short-term efficiency while destroying long-term value. When it is used to enhance operations, it creates a sustainable competitive advantage.
This point is directly connected to the concept of AI’s “invisible layer.” It refers to elements that do not appear directly in operational metrics but ultimately determine business success: customer trust, decision-making quality, and process governance.
This misalignment is often worsened by an inadequate allocation of investments. The model developed by BCG indicates that only 10% of AI efforts should focus on algorithms, 20% on data and infrastructure, and 70% on the operating model and governance. Even so, many companies concentrate resources on acquiring technology without restructuring their processes.
In the electricity sector, the customer journey involves multiple layers of complexity. Without a well-structured operating model, AI does not solve problems; it only accelerates them.
Within this model, Artificial Intelligence acts as a scaling engine, performing invoice analysis, consumption assessments, simulations, and proposal generation within seconds. At the same time, critical decisions, especially those involving long-term and more complex contracts, undergo human validation.
This balance delivers measurable benefits. Implementations that combine automation with human oversight reduce algorithmic errors, increase conversion rates, and improve customer retention, while also ensuring greater regulatory security. AI does not replace the consultant. It transforms the consultant’s role by eliminating operational tasks and allowing humans to focus on where they truly generate value.
Brazil Energy Insight 
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