Artificial Intelligence (AI) applications streamline workflows, automate tasks, and require adaptive strategies for effective integration into business processes. This research explores the transformative influence of AI on various industries, such as software engineering, automation, education, accounting, mining, legal services, and media. We investigate the relationship between technological advancements and the job market to identify relevant skills for individuals and organizations for implementing and managing AI systems and human–machine interactions necessary for actual and future jobs. We focus on the essential adaptations for individuals and organizations to flourish in this era. To bridge the gap between AI-driven demands and the existing capabilities of the workforce, we employ the Rapid Review methodology to explore the integration of AI in businesses, identify crucial skill sets, analyze challenges, and propose solutions in this dynamic age. We searched the Scopus database, screening a total of 39 articles, of which we selected 20 articles for this systematic review. The inclusion criteria focused on conference papers and journal articles from 2020 or later and written in English. The selected articles offer valuable insights into the impact of AI on education, business, healthcare, robotics, manufacturing, and automation across diverse sectors, as well as providing perspectives on the evolving landscape of expertise. The findings underscore the importance of crucial skill sets, such as technical proficiency and adaptability, to successfully adopt AI. Businesses respond strategically by implementing continuous skill adaptation and ethical technology to address challenges. The paper concludes by emphasizing the imperative of balanced skill development, proactive education, and strategic integration to navigate the profound impact of AI on the workforce effectively.

The paradigm of the Internet of everything (IoE) is advancing toward enriching people’s lives by adding value to the Internet of things (IoT), with connections among people, processes, data, and things. This paper provides a survey of the literature on IoE research, highlighting concerns in terms of intelligence services and knowledge creation. The significant contributions of this study are as follows: (1) a systematic literature review of IoE taxonomies (including IoT); (2) development of a taxonomy to guide the identification of critical knowledge in IoE applications, an in-depth classification of IoE enablers (sensors and actuators); (3) validation of the defined taxonomy with 50 IoE applications; and (4) identification of issues and challenges in existing IoE applications (using the defined taxonomy) with regard to insights about knowledge processes. To the best of our knowledge, and taking into consideration the 76 other taxonomies compared, this present work represents the most comprehensive taxonomy that provides the orchestration of intelligence in network connections concerning knowledge processes, type of IoE enablers, observation characteristics, and technological capabilities in IoE applications.

The fast growth of the urban population increases the demand for energy, water, and transportation, amongst other needs. This study explores the current state and future scenarios of Smart Cities and the environmental, economic, and social challenges that must be overcome. We used the Rapid Review method to understand the challenges of implementing Smart Cities in different urban contexts and the potential impact of research on Smart City planning in future Smart Cities. The study offers insights into the potential for Smart City growth while identifying obstacles that must be addressed to ensure sustainability. Results serve as a foundation for planning and decision-making, highlighting aspects such as the adoption of alternative energies, reduction in car use, preservation of ecosystems, waste reduction, citizen participation, infrastructure, and citizen data privacy, among others. These aspects are essential to overcome obstacles and promoting Smart Cities’ development.

Many studies have focused on estimating the impact of automation on work around the world with results ranging widely. Despite the disagreement about the level of impact that automation will have, experts agree that new technologies tend to be applied to every economic sector, thus impacting work regardless of substituting or complementing it. The purpose of this study is to move on from the discussion about the size of the impact of automation to understanding the main social impacts that automation will cause and what actions should be taken to deal with them. For this purpose, we reviewed literature about technological unemployment found in Scopus and Web of Science published since 2000, presenting an academic view of the actions necessary to deal with the social impact of automation. Our results summarize causes, consequences, and solutions for the technological unemployment found in the literature. We also found that the literature is mainly concentrated on the areas of economy, sociology, and philosophy, with the authors situated in developed economies such as the USA, Europe, and New Zealand. Finally, we present the research agenda proposed by the reviewed papers that could motivate new research on the subject.

Work has been continuously changing throughout history. The most severe changes to work occurred because of the industrial revolutions, and we are living in one of these moments. To allow us to address these changes as early as possible, mitigating important problems before they occur, we need to explore the future of work. As such, our purpose in this paper is to discuss the main global trends and provide a likely scenario for work in 2050 that takes into consideration the recent changes caused by the COVID-19 pandemic. The study was performed by thirteen researchers with different backgrounds divided into five topics that were analyzed individually using four future studies methods: Bibliometrics, Brainstorming, Futures Wheel, and Scenarios. As the study was done before COVID-19, seven researchers of the original group later updated the most likely scenario with new Bibliometrics and Brainstorming. Our findings include that computerization advances will further reduce the demand for low-skill and low-wage jobs; non-standard employment tends to be better regulated; new technologies will allow a transition to a personalized education process; workers will receive knowledge-intensive training, making them more adaptable to new types of jobs; self-employment and entrepreneurship will grow in the global labor market; and universal basic income would not reach its full potential, but income transfer programs will be implemented for the most vulnerable population. Finally, we highlight that this study explores the future of work in 2050 while considering the impact of the COVID-19 pandemic.

Purpose: Brazil is struggling as the unemployment rate is 12.4% and nearly 13m people are unemployed. The fourth Industrial Revolution is advancing, and the country needs to consider how it will impact the labor market. This work explores the impact of automation on the Brazilian workforce to supply decision-makers with information about the subject.Design/methodology/approach: The authors converted the probability of computerization from the seminal work of Frey and Osborne to each of the more than 2,500 occupations in Brazil. They then crossed the automation probability with socioeconomic information about workers and companies available in the Brazilian Ministry of Labor Database.Findings: In total, 60% of employment in Brazil is expected to be highly impacted by automation in the coming decades, with eight out of the ten occupations with the biggest workforce being highly automatable. Automation probability decreases as workers' education level increases, with the most significant difference between workers with higher education and those without it. The results show other inequalities in the impact of automation: the higher the wage, the lower the automation probability of occupations; the bigger the company, the lower the automation index; and workers from 16 to 24 years old have considerably higher chances of being automated.Originality/value: This work is the first to study, in the context of the fourth Industrial Revolution, the impact of automation in Brazil with a socioeconomic analysis.