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September 15, 2026

Artificial Intelligence as a Transformation Agent in the Labor Market

Maria Izabel Cardoso da Costa; Lidiane Mendes Barbosa

DOI: 10.22167/2675-6528-202602270

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 the Pecege Institute for textual synthesis and organization.

Abstract

The rise of the Fourth Industrial Revolution, driven by generative Artificial Intelligence (AI), has profoundly altered the relationship between capital and labor by automating non-routine cognitive tasks. The general objective was to analyze the perceptions of operational professionals from different sectors on the impact of AI in the labor market, identifying trends and challenges. The methodology employed exploratory and descriptive research, with a quantitative approach through a survey. The data collection instrument was a structured questionnaire, administered to 124 respondents, using Likert and multiple-choice scales. The results showed an advanced stage of technological adoption (74.2%), characterizing the sample as “early adopters”. It was identified that the gain in efficiency freed up between one and eight hours per week for most, although critical barriers such as lack of training and ethical concerns persist. Significant resistance was noted towards algorithmic management and the replacement of human judgment in critical decisions, as in the concept of “Agent Hospital”. Despite optimism regarding productivity, the sustainability of the workforce depends on urgent “reskilling” policies and the preservation of human agency. Human supervision remained indispensable in areas requiring empathy and moral responsibility.

Keywords: Automation; Digital Skills; Technological Ethics; Labor Market.

1. Introduction

The history of humanity is marked by great technological advances that, since the Industrial Revolution, raise concerns about the future of employment and the impact of automation. However, these advances have not only driven economic growth but also generated new tasks and job opportunities, as pointed out by Acemoglu and Restrepo (2018) and Brynjolfsson et al. (2018). Each new technological wave intensifies these debates, as observed by Gmyrek et al. (2023).

While 20th-century technologies predominantly affected manual laborers, advances since 2010, especially in Machine Learning, have focused on computers’ ability to perform non-routine cognitive tasks. This profound shift is reshaping the relationship between capital and labor, as discussed by Acemoglu and Restrepo (2018), Agarwal et al. (2018), Acemoglu et al. (2022), and Gmyrek et al. (2023).

The trajectory of industrial revolutions, from the steam engine (First Industrial Revolution) to Fordist work (Second Industrial Revolution) and the digital age (Third Industrial Revolution), culminated in the current Fourth Industrial Revolution, or Industry 4.0 (Marx, 1996; Carvalho and Pinho, 2009; Pereira et al., 2023). This era is characterized by technologies such as Artificial Intelligence, robotics, and the Internet (Schwab, 2016; Pereira et al., 2023), which break down barriers between the physical and virtual worlds, impacting the labor market and presenting risks for repetitive or low-skilled jobs (Quintela, 2018).

At the core of this new era, Artificial Intelligence (AI) emerges as a paradigm-shifting agent of transformation. Driven by machine learning techniques and vast datasets (Acemoglu et al., 2022; Patil et al., 2024), AI establishes itself as a new factor of production that amplifies both labor and capital (Acemoglu et al., 2022). Technically, AI is conceptualized as a system that perceives its environment and acts upon it to achieve specific goals (Russell et al., 2019; Felten et al., 2018; Felten et al., 2019). This scenario generates contrasting views: while some foresee a future with fewer jobs, others see it as a means to increase productivity and enhance the work experience (Agarwal et al., 2018; Acemoglu et al., 2022; Patil et al., 2024).

Although the automation of repetitive tasks already raised concerns about job displacement (Patil et al., 2024; Virgílio e López, 2021), the rise of generative AI expands automation to non-routine cognitive tasks, creating previously unattainable efficiencies (Gmyrek et al., 2023; Patil et al., 2024). This innovation, however, raises serious concerns about the potential for labor displacement, economic stability, and social equity (Russell et al., 2019; Occhipinti et al., 2025). The impacts on employability remain uncertain (Zhang et al., 2021), and the increasing accessibility of technology has generated anxiety regarding AI (Agarwal et al., 2018; Gmyrek et al., 2023; Lund et al., 2024; Patil et al., 2024).

Beyond the direct impacts on the labor market, technological advancement intensifies concerns about the increase in income inequality (Zhang et al., 2021; Lohr, 2022; Patil et al., 2024). It is estimated that by 2030, professional retraining will be necessary for a significant portion of workers to meet new market demands (Manika et al., 2017; Russell et al., 2019; Zhang et al., 2021; Lohr, 2022; Patil et al., 2024). The impacts of AI on employment and wages at the industry or occupation level are still too small to be conclusively detected (Felten et al., 2018; Felten et al., 2019), and socioeconomic consequences, such as the deepening of income inequality and the emergence of a “digital divide” between nations, remain open and critical issues (Schwab, 2016; Lohr, 2022).

Given this scenario of rapid transformations and uncertainties, it becomes fundamental to understand how professionals perceive the impact of Artificial Intelligence in their routines and careers. This research aims to analyze the perceptions of operational professionals from different sectors on the impact of Artificial Intelligence in the job market, identifying the main trends, challenges, and transformations in professional qualification requirements.

2. Material and Methods

The study adopted a quantitative approach, characterized as an exploratory and descriptive survey. The exploratory dimension sought to familiarize itself with the phenomenon under investigation, while the descriptive one aimed to identify patterns, trends, and professionals’ perceptions regarding the use of Artificial Intelligence (AI) in the job market (Sampieri; Collado; Lucio, 2013; Marconi; Lakatos, 2017; Veiga et al., 2021; Gil, 2019; Mattar, 2022; Fink, 2003; Vergara, 2021).

The data collection instrument was a structured questionnaire (Appendix I), composed of 21 questions and developed from the adaptation of scales and indicators validated in previous studies (Malhotra, 2012; Creswell, 2014; Schultz, 1961; Becker, 1965; Bandura, 1977; Frey and Osborne, 2013; Davenport, 2019; WEF, 2020). The questionnaire was divided into three thematic axes: Block 1 (Respondent Profile), with 5 multiple-choice questions for sociodemographic and professional characterization; Block 2 (Perceptions on the Impacts of AI), with 9 questions about transformations in routine and the job market; and Block 3 (Preparation and Adaptation), with 7 questions about individual adjustment and organizational readiness.

The target population comprised operational professionals from strategic sectors, such as health, education, technology, and business. The sampling strategy was non-probabilistic convenience sampling, using the snowball technique (Babbie, 1999; Duarte, 2010; Malhotra, 2012). The questionnaire was disseminated on the social network LinkedIn and supplemented by direct contacts via email with schools and business associations (Freitas et al., 2000). For variable measurement, a five-point Likert Scale, multiple-choice questions, and numerical range questions were used (Likert, 1932; Dalmoro and Andrade, 2013).

Before the final application, the questionnaire was submitted to a pre-test with a small group of 10 professionals to verify clarity, objectivity, coherence, and adequacy of the scales, as well as to estimate response time. The pre-test data were used exclusively for fine-tuning the instrument.

The questionnaire was anonymous and voluntary, ensuring the privacy and integrity of the participants’ information. The research was exempted from submission to the Research Ethics Committee (CEP), according to CNS Resolution No. 510, of April 2016 (Sole Paragraph, Article 1, Item I), as it was classified as public opinion research with unidentified participants.

The data obtained were organized and processed using Google Sheets and Microsoft Excel. The analysis consisted of tabulating the responses for the presentation of results in percentage terms, identifying the distribution of participants’ opinions and perceptions in each block of the questionnaire. Due to the non-probabilistic sampling, the results reflect the responses of the surveyed group, providing a preliminary understanding of the impact of AI in the investigated context.

3. Results and Discussion

124 responses were collected, with 100% adherence of respondents to the survey terms. Data from Block 1 revealed a profile of high education, with 41.13% of participants holding postgraduate degrees (lato sensu or MBA), 33.87% undergraduate degrees, and 13.71% master’s degrees.

Figure 1. Educational level of respondents

Source: Original research results

According to Figure 1, the majority (56.45%) of respondents have postgraduate or higher education, which is a predictor of lower anxiety regarding AI and greater technological reception (Lund et al., 2024; Patil, 2024). These professionals, often “knowledge workers”, see AI as an opportunity for innovation, demanding digital literacy and critical thinking (Pereira et al., 2023; Patil, 2024; WEF, 2025).

Figure 2 shows a heterogeneous distribution of respondents by area of activity, with a predominance of Administration/Management (28.23%), followed by Marketing/Sales (11.29%) and Technology/IT/Data (7.26%), in addition to 33.06% in other categories. This pattern reinforces the cross-cutting nature of Artificial Intelligence as a general-purpose technology.

Figure 2. Main area of activity of the respondents.

Source: Original research results

The advancement of generative AI shifts automation towards non-routine cognitive activities (Occhipinti, 2025), expanding the exposure of areas such as business, finance, and marketing (Gmyrek et al., 2023; Mainardi et al., 2024; Occhipinti, 2025; WEF, 2025). Professionals in knowledge-intensive roles, such as financiers and programmers, show greater susceptibility to AI integration due to the analytical and repetitive nature of their activities (PIE, 2024; WEF, 2025). The main impact of AI, however, lies in the expansion of productivity and operational efficiency, in a complementarity model between humans and machines (Gmyrek et al., 2023; McKinsey, 2023).

In hierarchical terms, the sample focuses on Analysts/Specialists (29.84%) and Owners/Partners/Self-employed (23.39%), with management positions accounting for 30.64%.

Figure 3. Current hierarchical level of respondents.

Source: Original research results

This composition, according to Figure 3, indicates a strategic and technical audience directly involved in automation decisions (Occhipinti, 2025; WEF, 2025). Technological evolution tends to replace repetitive tasks, not entire occupations (Mainardi et al., 2024), requiring professionals to focus on highly specialized activities that demand human competencies such as leadership and critical thinking (Gmyrek et al., 2023). The predominance of leaders and specialists in the sample suggests that AI governance is a strategic priority in the analyzed organizations (Occhipinti, 2025; WEF, 2025).

Regarding organizational size, the sample shows a balance between professionals from Micro/Small companies (40.32%) and Large companies (35.48%).

Figure 4. Size of the organization where respondents work.

Source: Original research results

The data from Figure 4 challenge the paradigm that cutting-edge technologies are exclusive to large corporations (IOE, 2024). Generative AI, with its high accessibility and low cost (Patil, 2024), has become vital for SMEs to optimize processes and innovate (IOE, 2024; Occhipinti, 2025; WEF, 2025), enabling small businesses to adopt it in proportions equivalent to large organizations (Agarwal et al., 2018; Gmyrek et al., 2023; Lund et al., 2024).

The analysis of familiarity with Artificial Intelligence reveals high adoption: 74.19% of respondents already use it in their routines, while 21.77% still have no practical contact with the technology.

Figure 5. Familiarity with AI

Source: Original research results

According to Figure 5, the data corroborate the rapid penetration of language models in corporate activities (Gmyrek et al., 2023; IOE, 2024). However, unequal adoption may widen productivity gaps, making digital literacy indispensable to avoid skill obsolescence (Patil, 2024). Reskilling and upskilling programs are essential for workforce sustainability (Patil, 2024; IOE, 2024; Mainardi et al., 2024; Occhipinti, 2025).

The analysis of activities with the highest automation potential highlights content creation (35.48%) and data processing (32.26%), as detailed in Figure 6.

Figure 6. Function and automation

Source: Original research results

Figure 6 highlights the expansion of generative AI beyond operational tasks, reaching cognitive and creative dimensions of work (IOE, 2024; Occhipinti, 2025). This transformation is associated with an optimistic perception of the professional future, in which 76.6% of respondents believe that AI will act as a catalyst for new roles.

Figure 7. AI as a promoter of new functions

Source: Original research results

The data from Figure 7 reveal a predominantly optimistic audience, aligned with the literature that predicts new demands and emerging occupations, especially in supervision, development, and governance of intelligent systems (Gmyrek et al., 2023; IOE, 2024; McKinsey, 2023). Regarding efficiency gains, 41.13% of respondents report saving 1 to 3 hours per week and 23.39% save between 4 and 8 hours.

Figure 8. Reduction of time spent on routine tasks.

Source: Original research results

The results of Figure 8 evidence the phenomenon of “augmentation” of work, where AI complements human capabilities, automating routine tasks and freeing up time for strategic activities (Davenport and Kirby, 2016; IOE, 2024), consolidating itself as a vector of productivity and economic growth (McKinsey, 2023; Goldman Sachs, 2023).

However, the materialization of these benefits requires overcoming structural barriers, with the main concerns being Security and Ethics Concerns (33.87%) and Lack of Training (28.23%).

Figure 9. Barriers to adapting AI

Source: Original research results

According to Figure 9, the skills gap is the biggest obstacle to business transformation (IOE, 2024), while the corporate use of technology imposes critical dilemmas regarding privacy and algorithmic biases. The transformative potential of AI depends on the organizational capacity to promote “responsible AI”, based on governance and transparency (Gmyrek et al., 2023; Mainardi et al., 2024). The perception of these challenges is reflected in the pursuit of competitiveness, with 29.84% of respondents considering AI proficiency essential for professional relevance.

Figure 10. Knowledge and competitiveness

Source: Original research results

According to Figure 10, the demand for new skills is central, with Critical Thinking (58.1%) and Prompt Engineering (41.1%) standing out as vital abilities.

Figure 11. Critical Skills

Source: Original research results

The data from Figure 11 align with the guidelines of the World Economic Forum (WEF, 2025), which positions analytical thinking as a core competence for overseeing automated decisions. Prompt Engineering reflects the urgency of AI literacy, although the competitive advantage lies in the combination of technical skills with social skills and emotional intelligence (Davenport, 2020; IOE, 2024; WEF, 2025). Regarding future perspectives, the largest concentration of respondents remains neutral (28.23%), while 23.39% believe that AI will bring more opportunities than risks.

Figure 12. Future perspectives

Source: Original research results

Although Figure 12 points to moderate optimism, this perception of technology as an ally aligns with the projection of new positions in governance and data analysis (Acemoglu and Restrepo, 2019). This ambivalence is grounded in the identification of tasks most susceptible to automation, such as report generation and data analysis (33.06%).

Figure 13. Routine Tasks and their automations

Source: Original research results

According to Figure 13, these technical tasks are the immediate focus of automation. However, the perception of risks reveals a relevant concern with the amplification of social inequalities (26.61%).

Figure 14. AI and social inequality

Source: Original research results

The data from Figure 14 dialogue with the classic literature, which argues that technological productivity gains are rarely distributed equitably (Frey and Osborne, 2013; Acemoglu and Restrepo, 2019). To mitigate this “digital divide”, continuous retraining policies for the workforce become indispensable (Gmyrek et al., 2023; Mainardi et al., 2024; Occhipinti, 2025).

In Block 3, focused on institutional support and skills development, a critical gap is observed: 43.5% of respondents did not dedicate time to AI training in the last 12 months, while only 14.5% invested more than three days in training.

Figure 15. Individual investment in formal AI training

Source: Original research results

This low individual engagement directly correlates with a lack in the corporate environment, where 34.68% of professionals feel totally unsupported by their organizations regarding the provision of resources and training.

Figure 16. Resources and training provided by the organization

Source: Original research results

According to Figures 15 and 16, the scenario illustrates the skills gap, pointed out globally as the main barrier to business transformation (Drucker, 1954; IOE, 2024; WEF, 2025). Lack of skills is cited by 63% of employers as the biggest hurdle for the coming years (WEF, 2025), and less than half of global workers receive adequate guidance (IOE, 2024). Thus, the data confirm the pressing need for reskilling and upskilling strategies, at risk of technological obsolescence (Brynjolfsson and McAfee, 2014; Patil, 2024; Mainardi et al., 2024).

Regarding risks, 28.23% of professionals identify failures in change management — such as lack of communication and training — as the main institutional obstacle.

Figure 17. Risks in AI adoption

Source: Original research results

On the other hand, a moderate trend towards innovation is observed: 33.06% of respondents perceive a neutral incentive and 25.81% feel that the organizational culture actively stimulates experimentation.

Figure 18. Incentive for AI tools

Source: Original research results

According to Figures 17 and 18, the identification of change management as a bottleneck reflects a global barrier, where cultural and structural resistance is one of the biggest obstacles, as AI requires reconfiguration of processes and mindsets (Schein, 1992; Kotter, 1996; Patil, 2024). The still-transitioning incentive reinforces the urgency of promoting a “culture of innovation” to ensure competitiveness in the age of artificial intelligence (WEF, 2025).

Regarding the disposition towards new work models, significant resistance to the loss of autonomy is observed: 31.5% of respondents categorically reject full AI supervision, while only 7.3% show full acceptance.

Figure 19. AI supervision acceptance

Source: Original research results

In contrast, the perception of individual preparation for career transformations is more positive: 30.6% are at an intermediate level and 29.8% feel considerably prepared.

Figure 20. Preparation for the future

Source: Original research results

The analysis of Figures 19 and 20 evokes the debate on “algorithmic management”, in which algorithms assume the assignment of human tasks (Davenport and Kirby, 2016; Gmyrek et al., 2023). The evidenced rejection corroborates that productivity gains are not acceptable when they compromise human agency (IOE, 2024; WEF, 2025).

The concept of “Agent Hospital” — AI systems for clinical decisions without human intervention — revealed an ethical barrier: 38.71% of participants totally disagree with this autonomy, while only 7.26% express full confidence.

Figure 21. Level of confidence in AI diagnostics and medical decisions

Source: Original research results

According to Figure 21, although the sample views technology as an opportunity for innovation (Lund et al., 2024), rejection of this model establishes the final boundary of adoption: professionals accept AI for efficiency but reject it when it replaces human judgment in critical decisions. This skepticism is explored by the “black box” phenomenon, which generates distrust due to the opacity of algorithms. For the WEF (2025), human oversight remains indispensable in areas requiring empathy and nuanced judgment, reaffirming that technology should serve as support, not as a substitute for ethical responsibility.

Table 1 summarizes the analytical correlation of the obtained data, revealing a scenario of resilient self-development. While Blocks 1 and 2 demonstrate that professionals are extracting productivity gains and mastering skills, Block 3 exposes the fragility of organizations, where the lack of support and formal training creates a strategic risk. In summary, the advancement of AI in the sample is led by the individual, not the institution, which generates the ethical concern and resistance to fully automated supervision identified in the final questions.

Table 1. Analytical Correlation (N=124)

Dimension of AnalysisPredominant Profile (N=124)Strategic Indicator Related
EducationHigh (56.45% with postgraduate degree or higher)Less technological anxiety / Better AI reception.
Job HierarchyStrategic and Technical (30.64% management; 29.84% Analysts/Specialists)Audience with high decision-making power and direct exposure to automation.
Company SizeBalanced (40.32% SMEs vs. 35.48% Large Companies)Technological democratization: AI is viable and vital for the survival of SMEs.
Familiarity with AIHigh Adoption (74.2% actively use)Rapid corporate penetration. Literacy is essential to avoid obsolescence.
Areas of Greatest Risk of AutomationContent Creation (35.48%) and Data Processing (32.26%)Expansion of AI into cognitive and creative dimensions. Structural change of intellectual work.
Perception of Efficiency GainsEvident (41.13% save 1-3h/week)Phenomenon of work “Augmentation”: AI as a complement to human capabilities.
Vital Skills (Skills Gap)Critical Thinking (58.1%) and Prompt Engineering (41.1%)Urgency of AI literacy. Competitive advantage is the union of technical and human.
Training EngagementLow (43.5% did not train in the last 12 months)It correlates with the feeling of institutional helplessness (skills gap).
Institutional Risk PerceptionFailures in Change Management (28.23% – communication/training)Cultural resistance is the biggest bottleneck. Need for an ‘innovation culture’.
Disposition to the Work ModelRejection of full automated supervision (31.5% disagree)Concern about the loss of human agency and “algorithmic management”.
Ethical BoundaryInsurmountable barrier: Autonomy for critical decisions (Ex: Agent Hospital – 38.71% refuse)Professionals accept AI for efficiency, but reject it as a substitute for human moral judgment.

Source: Original research results

4. Conclusion

The present research aimed to analyze the perceptions of operational professionals from different sectors on the impact of Artificial Intelligence in the labor market, identifying the main trends and challenges. It was found that Artificial Intelligence is already widely integrated into the daily lives of these professionals, with a high adoption rate that characterizes them as “early adopters”. It was observed that automation has advanced significantly in cognitive tasks, such as content creation and data processing, generating productivity gains that freed up between one and eight hours per week for most respondents. However, this transition revealed critical barriers, such as lack of training and ethical concerns, highlighting a mismatch between the speed of technological evolution and the qualification capacity of the workforce. The main contribution of the study lies in the reconfiguration of the professional qualification paradigm, which shifts from technical execution to strategic curation, supervision, and decision-making, requiring the integration of machine efficiency with intrinsically non-automatable human competencies.

Despite optimism about productivity, significant resistance was noted towards algorithmic management and the replacement of human judgment in critical decisions, as exemplified by the concept of the “Agent Hospital,” establishing an ethical boundary for the full adoption of AI. Career sustainability depends on preserving human agency and moral responsibility, with human oversight remaining indispensable in areas requiring empathy and nuanced discernment. As a limitation, this research was restricted to the analysis of a non-probabilistic and cross-sectional sample, which prevents the generalization of results to the entire Brazilian labor market and does not allow for tracking the evolution of perceptions over time. It is suggested that future research explore specific sectoral segments to identify regional or area-specific disparities, as well as conduct longitudinal studies that track how the advancement of AI autonomy will impact the remuneration and stability of operational roles in the long term.

Bibliographic References

Acemoglu D; Restrepo P. 2018. The Race between Man and Machine: Implications of Technology for Growth, Factor Shares, and Employment. American Economic Review, Vol. 108, No. 6.

Acemoglu, D.; Autor, D.; Hazell, J.; Restrepo, P.. 2022. “Artificial Intelligence and Jobs: Evidence from Online Vacancies.” Journal of Labor Economics 40 (S1): S293–340. https://doi.org/10.1086/718327.

Agarwal, A.; Gans, J.S.; Goldfarb, A. 2018. Prediction machines: The simple economics of artificial intelligence. Cambridge, MA: Harvard Business Review Press.

Brynjolfsson, E.; Mitchell, T.; Rock, D. 2018. “What can machines learn, and what does it mean for occupations and the economy?” AEA Papers and Proceedings 108:43–47.

Gmyrek, P., Berg, J., Bescond, D. (2023). Generative Al and jobs: A global analysis of potential effects on job quantity and quality. ILO Working Paper 96. Geneva: International Labour Office.

Marx, K. 1996. Maquinaria e grande indústria. In K. Marx. O Capital: crítica da economia política, Vol. 2, p. 7-133, São

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October 22, 2026

Stakeholder Engagement Factors in International Technology Transfer to a Public Hospital

This study described the critical factors perceived for stakeholder engagement in international technology transfer in a public hospital, with a focus on planning. The objective was to describe these conditioning factors, according to the perception of professionals. A single case study was conducted, applying a questionnaire to 25 professionals and using content analysis, complemented by descriptive tabulations. The main obstacles identified were resistance to change (72%), funding (64%), lack of communication (60%), and bureaucracy (40%). Only 28% of respondents recognized the existence of adequate tools for stakeholder management, while 48% denied it and 24% did not know. Exploratory cross-tabulations indicated variations in the assessment of influence according to time of practice, but did not establish causal relationships. Lack of engagement was associated with delays, low adherence, waste of resources, communication failures, compromised knowledge absorption, and risks to patient safety. The conclusions were limited to the perceptions of the researched group and supported preliminary recommendations for intersectoral communication and coordination.

Keywords: Case study; Project Management; Health innovation; Stakeholders; Public sector.

October 22, 2026

Combating Commercial Electric Energy Losses Using a Time Series Model

Commercial losses of electric energy represented one of the main challenges for distributors, directly impacting economic sustainability and supply quality. The efficiency of time series models, focusing on exponential smoothing techniques, was evaluated in forecasting billed consumption of consumer units, to support the identification of deviations associated with non-technical losses. Historical data of monthly consumption from an electric energy distributor in Brazil were used, segmented by behavior profiles (stationarity, trend, and seasonality). Simple exponential smoothing models, Holt, and Holt-Winters (additive and multiplicative) were applied, compared to a traditional moving average. The Mean Absolute Percentage Error (MAPE) was the evaluation criterion. Additionally, the ETS approach was employed for automatic model selection and residual diagnostic tests, such as Ljung-Box and autocorrelation function analysis. The results demonstrated that more robust models showed a greater capacity for adaptation to the series’ characteristics and lower error, with a highlight on superior performance in scenarios with trend. For seasonal series, the ETS model showed a greater capacity for adaptation to transitions. It was concluded that the use of more structured time series models contributed significantly to the improvement of forecast accuracy and offered relevant analytical support for the identification of commercial losses and the optimization of operational and commercial processes in the electric sector.

Keywords: Energy distribution; MAPE; Optimization; Consumption forecasting; Exponential smoothing.

Economy

October 19, 2026

End of the 6×1 scale: what do the numbers allow us to conclude?

Following the promulgation of the end of the 6×1 shift system, the debate on the shorter workday moves to implementation and requires distinguishing social benefits from economic costs. The end of the 6×1 shift system transitioned from legislative discussion to a constitutional change in Brazil. On October 16, 2026, the National Congress promulgated Constitutional Amendment No. 141/2026, originating from Proposal […]

Artificial Intelligence

October 16, 2026

Why study if AI exists?

When answers become easy, learning comes to mean more than just arriving at them. Why do I need to learn this if I can ask an artificial intelligence? This type of question has accompanied education for decades. Why learn an operation if a calculator exists? Why memorize information if it can be found […]

Tax Management

October 15, 2026

Pink tax and the gender of price

How different marketing treatments for men and women are incorporated by consumption taxation Imagine a shelf on which two razors of the same brand and intended for the same purpose are displayed side by side: one has dark packaging and promises strength, precision, and efficiency; the other comes in shades of pink or […]

Health

October 13, 2026

The missing link in health transformation

Why is innovation not enough to drive changes in organizations and health systems Digital transformation has become a strategic priority for health systems worldwide. Electronic health records, artificial intelligence, telemedicine, remote monitoring, and data analysis are now part of the strategic agenda of governments and health organizations. Paradoxically, […]

Marketing

October 09, 2026

Bitcoin or pets: what Artificial Intelligence would not see

A real-life consumer intelligence case shows what gets left out of the brief A few years ago, an American insurance company hired a specialized consumer intelligence firm to answer three questions: The insurer was also considering launching paid insurance in bitcoin. At the time, the consultancy’s workflow was as follows: the analyst created a query […]

School Management

October 06, 2026

The school is not a casino: when trading cards become currency in the playground

Why, for certain students, does school manage to offer so few experiences of achievement? The World Cup is over. Brazil left the competition, the school holidays came, the final was played and the football calendar moved on. It would be reasonable to imagine that, with the end of the tournament and the return to classes, […] would also begin

Energy Transition

September 30, 2026

Renewable energies: the Brazilian advantage

Brazil needs to invest in the capacity to transform natural resources into innovation, productivity, and development. Talking about energy is talking about development. Behind industry, transportation, services, cities, and increasingly artificial intelligence, there is an energy infrastructure that sustains the economy. Understanding a country’s energy matrix is understanding […]

Data Analysis

September 30, 2026

When the data changes, has reality changed?

An analysis of almost 20 million teaching positions-year between 2020 and 2025 shows how changes in population composition and record production can alter the interpretation of historical series Between 2020 and 2025, the number of formal teaching employment positions identified in RAIS (Annual Relation of Social Information), from the Ministry of Labor, […]

Artificial Intelligence

September 28, 2026

Artificial intelligence and the trends

Why do so many people join in on the fun with ChatGPT? I was never the trend person. I still am not. Some pass through my timeline, I see them, I find them interesting but not to the point of trying them out. This was the case, inclusive, with some recent waves of images made by artificial intelligence, like the 80s trend, the one that […]

Tax Management

September 01, 2026

Tax reform and estate planning in the countryside

Efficiency in managing taxes facilitates governance and allows for the organization of family succession. Rural wealth organization has become a strategic measure in light of the tax reform in Brazil, which came into effect this year. The planning of activities and rural wealth management, through tools such as the transition from an Individual to […]

Renata de Gaspari Valdejão Almeida

Artificial Intelligence

August 05, 2026

Inside the company and off the radar: the risks of Shadow AI

According to Microsoft, 75% of professionals already use artificial intelligence at work without company approval. The use of generative artificial intelligence is already part of the corporate routine. Tools like ChatGPT, Copilot, and other platforms help professionals produce texts, analyze data, create presentations, and accelerate tasks in practically all sectors. But this advance […]

Renata de Gaspari Valdejão Almeida

Strategy

July 02, 2026

The future as a competence

Academic interest reinforces the projection of scenarios as an essential tool for decision-making. While some companies are still trying to get the next step right, others are already working with multiple possible futures — and preparing for all of them. But after all, what does it mean to “think about the future”? Is it a trend, a strategy, or a new competence that […]

Renata de Gaspari Valdejão Almeida

Innovation

Technology

May 21, 2026

Agro SP Corridor: innovation, institutional articulation and future

Pecege commands the executive operation of the 400 km ecosystem activities that became known as the Brazilian agro Silicon Valley In 2025, in addition to taking over the management of the Piracicaba Technology Park, the Pecege Institute was also designated to command the executive operation of the State Agribusiness Innovation Corridor activities, the so-called […]

Education

April 27, 2026

How to turn a TCC into a business

New management of the Piracicaba Technology Park offers consulting to MBA graduates The new project director of the Piracicaba Technology Park (PTP), Alexandre Barreto, often says that one of the greatest rewards he has ever had in life was seeing his MBA USP/Esalq students structure companies from their final projects […]

Technology

March 20, 2026

The human repertoire in the age of AI

Critical thinking and creativity are irreplaceable in the work of a marketing professional Artificial Intelligence (AI) is capable of incredible feats, but it does not yet possess the judgment, empathy, intuition, and strategic creativity necessary to do the job of a good marketing professional. In this area, it is important to know how to use the tool to automate […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

February 23, 2026

ESG as a growth strategy in a cooperative in RS

Santa Clara balances social and environmental responsibility with innovation Founded in 1911 by Italian immigrants in Carlos Barbosa, in the Serra Gaúcha region, Cooperativa Santa Clara is an example that it is possible to balance economic growth with social and environmental responsibility. From a small cheese production company, the organization has evolved into a […]

Renata de Gaspari Valdejão Almeida

Agribusiness

Innovation

January 22, 2026

Brazil free of Foot-and-Mouth Disease: what changes for Brazilian beef?

With a herd of over 200 million head of cattle, the country is currently the leader in exports. Brazil recently received, for the first time, the certification of country free of foot -and-mouth disease without vaccination, conferred by the World Organisation for Animal Health (WOAH). The seal strengthens the country’s position as a reliable supplier of meat for the […]

Renata de Gaspari Valdejão Almeida

Innovation

December 09, 2025

Innovation: the new cycle of the Piracicaba Technology Park

Instituto Pecege assumes management, betting on the creation of new businesses Starting in 2026, Instituto Pecege will assume the management of the Piracicaba Technology Park (PTP). Among its responsibilities will be the administration, operation, maintenance, and expansion of the park, in addition to fostering activities in innovation, entrepreneurship, and science and technology. The choice was made […]

Renata de Gaspari Valdejão Almeida

July 06, 2026

Large-Scale Architecture: DSR-SCRUM Change Management

Mônica Alessandra Guerios; Maria do Carmo Assis Todorov Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. Architecture and urbanism are configured as essential bases in an increasingly complex and challenging urban space, where the intrinsic connection between social, environmental, and technological issues […]

Executive Mba In Leadership And Management

June 29, 2026

Supplier Qualification: Standardization and Risk

Isis Penelope Sumarelli Albuquerque Huang; Vítor Melão Cassânego Abstract generated by the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. The growing complexity of agri-food supply chains, driven by globalization, has generated an intensified demand for transparency, security, and traceability in food systems. The integrity and authenticity […]

Executive Mba In Leadership And Management

June 26, 2026

Legal: from cost center to strategic partner

Adriana Ehiar Ribeiro Petry; Ana Carla Fernandes Gasques Abstract prepared by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. The expansion scenario in the Brazilian market, although promising for consumer goods companies, presents significant challenges that require a strategic and adaptable approach. With a population […]

Executive Mba In Leadership And Management

June 26, 2026

Culture: Professionalization and growth in family businesses

Yashmin Meirelles Oliveira Araujo; Gilmar Martinelli Junior Abstract prepared by the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. The Brazilian economic scenario is markedly influenced by the expressive representativeness of family businesses, which constitute approximately 90% of the country’s enterprises, according to PwC (2023) data. Within this vast universe, […]

Executive Mba In Leadership And Management

June 26, 2026

Semi-Fixed Teams: Engagement and Cohesion in the Field

Wesley Araújo Martins; Odair Silva Soares Abstract prepared by the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. The operational sector of a company specialized in technical field services, operating in the maintenance of power transformers in industrial environments and energy utilities, faces significant challenges related to […]

Executive Mba In Leadership And Management

June 26, 2026

AI in pre-sales: Automated proposal generation

Vitor Luis Genaro; Felipe Carvalhal Abstract generated by the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. The technology consulting market demands increasing operational efficiency from organizations, combined with analytical capacity and speed of customer response. In this dynamic scenario, the pre-sales area assumes […]

Executive Mba In Leadership And Management

June 26, 2026

PMaaS: Leveraging projects in SMEs without a PMO

Vitor Conceição Faria; Ewerton Mauro Visotto Faria Abstract elaborated by the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. The contemporary business scenario, marked by high competitiveness and accelerated digital transformation, imposes complex challenges on companies, especially small and medium-sized ones. Many of these organizations, although engaged […]

Executive Mba In Leadership And Management

June 26, 2026

Agile Lifecycle Management in Public Funding

Vinícius Nascimento Garcia; Ana Carla Gasques Abstract generated by the ResumeAI tool, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. The state public development agency faced a scenario of reactive and fragmented development of its credit lines, characterized by low data usage and the absence of governance […]

Executive Mba In Leadership And Management

June 26, 2026

Mentoring and Onboarding: Developing Talent in GC

Vinícius Andrade de Carvalho; Ewerton Mauro Visotto Faria Abstract generated by ResumeAI, an artificial intelligence solution developed by Instituto Pecege focused on synthesis and writing. The demand for outsourcing services has experienced remarkable growth both in Brazil and globally, driving companies to expand their outsourced operations. Reports such as the “Global Outsourcing Survey […]