Impact of Artificial Intelligence on Competitive Strategy: Case Study in a Brazilian Banking Institution

Matheus Correia Naletto; Ingrid de Matos Martins

DOI: 10.22167/2675-6528-202603282

Article derived from a Final Course Work (TCC), with content based on the student’s original work and adapted to the editorial format of the E&S Magazine with the support of the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege for textual synthesis and organization.

Abstract

Artificial intelligence has been consolidating itself as a transformative force in the Brazilian banking sector, directly impacting how institutions structure their strategies and position themselves in a highly competitive market. The study analyzed the impacts of implementing artificial intelligence on the competitive strategy of a Brazilian banking institution. To this end, a single case study with a qualitative approach was used, involving unstructured interviews with seven employees from different hierarchical levels involved in AI initiatives. The collected data were subjected to content analysis. The results revealed that the implementation of AI in the institution was transversal, gradual, and strategic, positively impacting operational efficiency, customer experience, competitiveness, and innovation in products and services. Despite the benefits, cultural and organizational, systemic, and regulatory challenges were identified in the adoption process. The research also pointed to a significant potential of AI to improve decision-making and reconfigure competitiveness in the sector. It was concluded that customer experience emerged as the central axis of the competitive strategy driven by AI. Artificial intelligence acts as a strategic resource that requires continuous organizational adaptation to balance innovation and governance, with the human factor being crucial for the sustainability of digital transformation.

Keywords: Organizational adaptation; Competitive strategy; Artificial intelligence; Banking sector; Digital transformation.

1. Introduction

Technological advancement, especially in the field of artificial intelligence (AI), has caused significant transformations in various sectors of the global economy. In the context of the banking sector, artificial intelligence has consolidated itself as one of the main driving forces of change, directly impacting how financial institutions structure their strategies and position themselves in a highly competitive market. This reconfiguration is not limited to operational aspects but extends to the strategic sphere, requiring constant adaptation of business models.

The growing popularization of artificial intelligence in the daily lives of individuals and organizations has driven its integration into banking processes and services. This integration is manifested in the automation of tasks and processes, resulting in substantial productivity gains. AI allows institutions to optimize their operations, from customer service to internal management, freeing up human resources for activities of higher added value and complexity. The transformation in the customer experience, for example, is a direct reflection of this integration, aiming for greater efficiency and personalization of services.

Beyond productivity gains, artificial intelligence represents an undeniable growth trend and a crucial competitive advantage for the banking sector. Investments in AI seek not only to optimize costs but also to foster innovation and generate superior results. An institution’s ability to incorporate AI into its strategy can determine its position in a dynamic market environment, where agility and responsiveness to customer demands are decisive factors. AI-driven innovation enables the development of new products and services, as well as the enhancement of existing ones, adding value for customers and the organization itself.

Faced with this scenario of profound transformation, it becomes essential to understand the multiple impacts of the implementation of artificial intelligence in banking institutions, especially regarding their competitive strategy. Although the adoption of AI is a reality, a deep understanding of how this technology influences strategic decisions and the organizations’ competitive capacity still requires specific investigation. There is a gap in the literature on case studies detailing the experience of Brazilian banking institutions in integrating AI and its practical and strategic effects.

The relevance of this study lies in the need to provide insights into the dynamics of artificial intelligence in the national banking context, contributing to managers and policymakers being able to make more informed decisions in the face of the challenges and opportunities presented by this technology. The analysis of the impacts of AI on the competitive strategy of a Brazilian banking institution offers a valuable perspective on organizational adaptations and new approaches to maintaining relevance in the market. Thus, the present study is justified by the urgency to deepen the understanding of the topic, with the objective of analyzing the impacts of the implementation of Artificial Intelligence in the Brazilian banking sector, focusing on the strategic and competitive sphere.

2. Material and Methods

The present study adopted a qualitative methodological approach, based on the principles of Yin (2010), which emphasizes a deep understanding of phenomena in their natural contexts. This methodological choice was crucial for exploring the complexities and multiple dimensions of the impacts of artificial intelligence on the competitive strategy of a banking institution. The qualitative approach allowed for the capture of perceptions, experiences, and interpretations of the individuals involved, offering a rich and detailed view of the investigated topic, rather than focusing on quantifications or statistical generalizations.

Regarding the research objectives, it was configured as a single case study, according to the methodology detailed by Yin (2010). This strategy was considered the most appropriate for investigating a contemporary phenomenon, such as the implementation of artificial intelligence, within a real and specific context. The case study allowed for an intensive and holistic analysis of a single institution, enabling the exploration of its particularities and a deep understanding of how AI integrates and influences its strategy. The unit of analysis was a large Brazilian banking institution, selected for its relevance in the national financial landscape and for its proven track record at the forefront of implementing artificial intelligence initiatives.

The research participants were seven employees of the studied banking institution, who occupied different hierarchical levels and were directly involved in projects and initiatives related to artificial intelligence. The selection of these participants occurred through convenience sampling, according to the guidelines of Gil (2019), prioritizing individuals who possessed in-depth knowledge and practical experience on the central theme of the study. The selection criteria focused specifically on active participation in areas or projects impacted by AI, ensuring that the interviewees could provide relevant information and diverse perspectives on the adoption and effects of the technology in the organization.

For the primary data collection, the technique of unstructured interviews was used, following the methodological guidelines of Mattos (2005). The interviews were conducted based on a semi-structured script, composed of seven main guiding topics. These topics covered aspects such as the AI implementation process, the challenges faced, the perceived impacts on the institution’s competitive strategy, and future perspectives related to the technology. This interview format provided the necessary flexibility to explore emerging themes during the conversations and deepen discussions according to the participants’ own narratives and insights, enriching the information collection.

The interviews were conducted individually, adapting to the participants’ availability, and were carried out both in person and via videoconference. Before the start of each session, the general objectives of the research were clearly presented to the interviewees, and verbal consent was formally obtained, ensuring voluntary participation and the guarantee of confidentiality for all shared information. Although the original TCC does not specify an exact period for data collection, the procedures were carried out systematically to ensure the consistency and richness of the data obtained, which are fundamental for subsequent analysis. No specific software was used for collection, beyond communication platforms for videoconferences.

The qualitative data collected through the interviews were submitted to the content analysis technique, rigorously following the methodological principles established by Bardin (2011). This technique was applied in sequential stages, starting with pre-analysis, where a floating reading of the material was performed to form a first impression and organize the corpus. Then, the material was explored, which involved coding and categorizing the participants’ statements, identifying units of record and context. The treatment of results, inference, and interpretation were the final phases, where the data were synthesized for the construction of analysis categories that reflected the impacts of AI on the institution’s competitive strategy, without presenting empirical results or findings in this section.

3. Results and Discussion

The implementation of artificial intelligence in the studied banking institution proved to be a multifaceted process, characterized by its transversal, gradual, and strategic nature. It was observed that AI was not adopted in isolation, but integrated into various areas of the organization, reflecting a holistic approach. This progressive incorporation into the daily banking operations demonstrates a continuous effort of adaptation and evolution, aligned with the institution’s business objectives. The analysis of the collected data allowed for the identification of the main impacts of this implementation on the competitive strategy, as well as the challenges and opportunities inherent in this digital transformation.

The cross-cutting nature of AI was evident in its presence across different departments, from customer service to risk management, indicating a capillarity that permeates operations. The gradual nature of implementation suggests a continuous learning and adaptation curve, where technology is progressively incorporated, allowing the organization to assimilate changes and adjust its processes. Finally, the strategic dimension of AI was highlighted by its direct association with business objectives, aiming to enhance competitiveness and innovation in the financial market, as pointed out by the research participants.

Operational efficiency has emerged as one of the most significant impacts of AI, with five interviewees highlighting its contribution to the optimization of daily activities. One participant emphasized that AI allows “to optimize our day-to-day, knowing how to use it, I think you have a time benefit, you have more efficiency in your actions.” This perception indicates that the technology not only automates tasks but also frees up human resources for activities of greater complexity and added value, aligning with the pursuit of greater productivity and reduction of operational costs.

Customer experience was another pillar impacted by artificial intelligence, being mentioned by four interviewees as a crucial factor for competitiveness. One of the participants emphasized AI’s ability to “improve the customer experience, making interactions more agile and personalized”. This personalization and agility in service are key elements for customer satisfaction and for building lasting relationships, which translates into a competitive advantage in an increasingly consumer-focused market.

The institution’s strategic competitiveness was also directly influenced by AI, with four interviewees pointing out that the technology is fundamental to “maintain the bank’s competitiveness in an increasingly digital and technology-driven financial environment”. The ability to innovate and adapt quickly to new market demands, driven by artificial intelligence, allows the institution to position itself advantageously against competitors, especially digital banks that already have an advanced technological infrastructure.

In data and risk management, AI has proven to be a valuable tool, with four interviewees highlighting its contribution to “optimizing our day-to-day, especially in technology areas, we can get great help in manipulating data with more time efficiency”. Artificial intelligence’s ability to process large volumes of data and identify complex patterns enhances risk analysis and strategic decision-making, providing greater security and precision to banking operations.

Innovation in products and services was identified by three interviewees as a direct benefit of AI. One of the participants stated that “AI is not new in the bank; since way back, we already had a tool that was being piloted to provide product inputs on a daily basis. We were pioneers.” This perspective highlights that artificial intelligence is not just an optimization tool, but a catalyst for the development of new offerings and improvement of existing ones, allowing the institution to explore new market opportunities and meet customer expectations more effectively.

The AI initiatives at the institution presented diverse origins, classified as institutionalized and decentralized. Institutionalized initiatives, as reported by an interviewee, “come in a more structured way, from the top, mainly from senior leadership,” reflecting strategic demands and multi-sectoral development. In contrast, decentralized initiatives “arise in a decentralized manner, in small hubs,” indicating a more spontaneous and autonomous approach, originating in smaller cores of the organization. This duality in the origin of initiatives demonstrates an organizational culture that allows for both top-down and bottom-up innovation.

The prioritization of AI initiatives was analyzed across four main dimensions: adaptive, economic, technical, and strategic. The adaptive dimension, cited by four interviewees, refers to the scalability and organizational impact of AI. The economic dimension, mentioned by four participants, focuses on costs, revenues, and expense reduction. The technical dimension, also pointed out by four interviewees, considers data maturity and the necessary infrastructure. Finally, the strategic dimension, cited by three participants, assesses the alignment of initiatives with the organization’s overall objectives, highlighting the complexity of the decision-making process.

The implementation of AI in the banking institution is not without challenges, which were categorized as cultural and organizational, systemic, and regulatory. Cultural and organizational challenges were the most highlighted, with seven interviewees mentioning employee resistance, the need for adaptation to changes, and a lack of training. One participant expressed that “the biggest challenge is the cultural aspect, people still have a lot of blockages, fear of changes, job reallocation, and even layoffs,” underscoring the importance of managing the transition and the human impact of technology.

The systemic challenges, pointed out by four interviewees, include the technical demands and the necessary infrastructure for integrating new technologies. One of the participants highlighted “the systemic challenges, such as the integration of legacy systems, data quality and governance, and model scalability”, indicating the complexity of harmonizing AI with existing systems and ensuring data robustness and security. Overcoming these technical obstacles is fundamental for the scalability and effective performance of artificial intelligence solutions.

The regulatory challenges, cited by three interviewees, involve data protection, copyright, and the demands of regulatory bodies. One participant specifically mentioned “regulatory challenges, in compliance with LGPD and data protection,” emphasizing the importance of adhering to legal and ethical norms. Compliance with the General Data Protection Law (LGPD) and other regulations is crucial for the institution’s credibility and customer trust, requiring constant monitoring and adaptation of AI practices.

The impacts of AI on competitive strategy were detailed, with customer experience being the most cited, by six interviewees. One participant stated that “the biggest strategic impact is in offering a frictionless, fast, and intelligent experience. Whoever manages to deliver fast and effective solutions will have a market advantage, especially against digital banks that are already advanced in this aspect”. This reinforces the idea that AI is a competitive differentiator by providing more fluid and personalized interactions, meeting the expectations of an increasingly demanding consumer.

Operational efficiency, mentioned by five interviewees, also stood out as a strategic impact. One participant reported that “AI used to automate tasks, resolving about 80% of operational cases, frees up team time to focus on more complex and innovative demands”. This efficiency gain not only optimizes resources but also allows the team to dedicate itself to higher strategic value activities, driving innovation and improving the institution’s responsiveness to market dynamics.

Innovation, cited by five interviewees, is another crucial impact of AI on competitive strategy. One participant observed that “if you have an AI that identifies opportunities and improvements, you can be much more assertive in approaching the client, to generate hot leads, for example. The company that does not invest in this today is already behind.” This perspective emphasizes that AI is not just an optimization tool, but an engine for creating new products, services, and market approaches, granting the institution a sustainable competitive advantage.

Decision-making support, although cited by only two interviewees, was recognized as a fundamental strategic impact. One participant explained that “AI should not just be an operational tool, but an artifice to redefine business strategy, personalize value propositions, and increase customer centricity, which directly impacts the institution’s profitability and competitiveness.” This suggests that artificial intelligence, by providing more accurate and real-time insights, empowers managers to make more informed decisions aligned with strategic objectives, strengthening the organization’s market position.

The transformative potential of AI in the competitive landscape was explored on three fronts. Changes in products and services, mentioned by seven interviewees, include the incorporation of AI into solutions for data segmentation and analysis, allowing for greater precision in defining audiences and offers. Changes in external customer experience, also cited by seven participants, translate into faster and more efficient service, offering immediate responses and increasing the probability of sales conversion. Finally, changes in internal customer experience, pointed out by four interviewees, refer to AI’s assistance to strategists and analysts in technical tasks, such as code generation and process automation, optimizing internal work.

The future prospects of AI in the banking sector reveal a scenario of opportunities and risks. Among the opportunities, personalization and customer experience were highlighted by seven interviewees, as well as the expansion of AI and automation use, also cited by seven participants. Organizational capacity and culture were mentioned by four interviewees as areas of opportunity for the continuous development of AI. These opportunities indicate a path to improve customer relationship and expand the application of technology.

Regarding risks, cybersecurity and privacy were pointed out by six interviewees as central concerns. Regulation and legal uncertainties were cited by four participants, while algorithmic biases and model errors were mentioned by three. These risks underscore the need for rigorous management and a constant balance between innovation and governance, ensuring that the implementation of AI is safe, ethical, and compliant with legal and social requirements. Continuous organizational adaptation emerges as a critical factor for mitigating these risks and capitalizing on opportunities.

In summary, the research demonstrated that the implementation of artificial intelligence in the Brazilian banking institution is a complex and strategic process, which directly impacts operational efficiency, customer experience, innovation, and decision-making. Despite cultural, systemic, and regulatory challenges, AI is consolidating itself as a strategic resource capable of reconfiguring competitiveness in the sector, with customer experience emerging as the central axis of the strategy driven by this technology, requiring continuous organizational adaptation to balance innovation and governance.

4. Conclusion

The study sought to analyze the impacts of the implementation of artificial intelligence on the competitive strategy of a Brazilian banking institution. It was verified that the adoption of AI occurred transversally, gradually, and strategically, permeating diverse areas of the organization. The main findings indicated that artificial intelligence positively impacted operational efficiency, improved customer experience, strengthened strategic competitiveness, optimized data and risk management, and boosted innovation in products and services. It was observed that AI initiatives emerged both in an institutionalized and decentralized manner, and their prioritization considered adaptive, economic, technical, and strategic dimensions.

Despite the benefits, significant challenges were identified in the implementation process, categorized as cultural and organizational, systemic and regulatory, with employee resistance and the need for cultural adaptation being the most prominent. The main contribution of this study lies in demonstrating that customer experience emerged as the central axis of the AI-driven competitive strategy, reconfiguring the institution’s market positioning. Artificial intelligence acts as a strategic resource that requires continuous organizational adaptation to balance innovation and governance, highlighting the human factor as crucial for the sustainability of digital transformation. As a limitation, the single case study restricts the generalization of the results. For future studies, it is suggested to conduct in-depth research into the mechanisms for mitigating cybersecurity risks and algorithmic biases, as well as analyzing the effectiveness of training programs and organizational culture in accelerating AI adoption in other banking institutions.

Bibliographic References

Bardin, L. (2011). Análise de conteúdo. Lisboa: Edições 70.

Gil, A. C. (2019). Métodos e técnicas de pesquisa social. 7. ed. São Paulo: Atlas.

Mattos, L. C. L. de. (2005). A entrevista não-estruturada como forma de conversação: razões e sugestões para sua análise. Revista Brasileira de Educação, n. 29, p. 93-102.

Yin, R. K. (2010). Estudo de caso: planejamento e métodos. 4. ed. Porto Alegre: Bookman.

Article originating from the Course Conclusion Work of the Specialization in Digital Business Management and Artificial Intelligence of the MBA USP/Esalq

To learn more about the course, click here and access the MBX Academy platform