Recent technological advancements have made Large Language Models (LLMs) easily accessible through apps such as ChatGPT, Claude.ai, Google Gemini, and HuggingChat, allowing text generation on diverse topics with a simple prompt. Considering this scenario, we propose three machine–machine collaboration models to streamline and accelerate Delphi execution time by leveraging the extensive knowledge of LLMs. We then applied one of these models—the Iconic Minds Delphi—to run Delphi questionnaires focused on the future of work and higher education in Brazil. Therefore, we prompted ChatGPT to assume the role of well-known public figures from various knowledge areas. To validate the effectiveness of this approach, we asked one of the emulated experts to evaluate his responses. Although this individual validation was not sufficient to generalize the approach’s effectiveness, it revealed an 85% agreement rate, suggesting a promising alignment between the emulated persona and the real expert’s opinions. Our work contributes to leveraging Artificial Intelligence (AI) in Futures Research, emphasizing LLMs’ potential as collaborators in shaping future visions while discussing their limitations. In conclusion, our research demonstrates the synergy between Delphi and LLMs, providing a glimpse into a new method for exploring central themes, such as the future of work and higher education.

Knowledge Management (KM) plays a fundamental role in technology-based organizations by enabling them to effectively manage and utilize their knowledge resources in dynamic and fast-evolving environments. While KM has been extensively explored across various industries, its application within technology-driven sectors requires further examination due to the unique challenges these organizations face, such as rapid technological advancements, knowledge silos, information overload, and high employee turnover. Knowledge is a key driver of innovation, decision-making, and competitive advantage, so a deeper understanding of how KM operates in these contexts is essential. This study conducts a rapid literature review to investigate the benefits, challenges, and evaluation metrics associated with Knowledge Management in technology-based organizations. The findings reveal that well-implemented KM strategies enhance innovation capacity, operational efficiency, and more effective decision-making processes. However, despite its advantages, Knowledge Management implementation is often hindered by cultural barriers, reluctance to share knowledge, and difficulties aligning KM initiatives with overarching strategic goals. Overcoming these obstacles requires targeted investments in digital infrastructure, leadership support, and promoting a strong knowledge-sharing culture. We also identify a set of potential metrics for assessing KM effectiveness, including operational efficiency indicators, employee and customer satisfaction levels, patent data, knowledge stock, and structural capital. While these metrics build upon insights from the literature, further research is necessary to validate their applicability and reliability as measures of KM success in rapidly changing technology environments. Despite these challenges, Knowledge Management presents transformative potential for organizations seeking to leverage knowledge as a strategic asset, driving continuous innovation, improving organizational performance, and sustaining a competitive edge in an increasingly knowledge-intensive economy. By providing a structured understanding of KM’s role, challenges, and possible success metrics, this study offers valuable insights for researchers and practitioners, guiding them in refining Knowledge Management strategies to enhance adaptability, collaboration, and long-term resilience in technology-driven industries.

In recent years, the evolution of technology and the increasing globalization of work have significantly transformed the software development industry. Global Software Development (GSD) has become a prevalent practice, enabling companies to reduce costs, leverage time zone differences for continuous development, and access diverse talent pools worldwide. However, knowledge transfer among distributed teams remains a significant challenge, often resulting in inefficiencies, project delays, and decreased software quality. As organizations increasingly rely on geographically dispersed teams, understanding the barriers to effective knowledge transfer in GSD is essential. This study is motivated by the need to reassess knowledge transfer in GSD, especially considering recent advancements in communication tools, collaboration platforms, and the widespread shift to remote work. While previous studies, such as those conducted by Zahedi et al. (2016), have examined knowledge transfer challenges and practices in GSD, the rapid technological and organizational shifts over the past decade necessitate an updated literature review. This work aims to provide a comprehensive overview of distributed teams’ challenges in knowledge transfer and the emerging practices to address these issues. A systematic literature review was conducted, building on the work of Zahedi et al. (2016) by analyzing research published between 2014 and 2024. The study followed a rigorous research protocol, utilizing the Scopus database to identify relevant papers and extract key insights. The findings highlight persistent challenges, including communication barriers, cultural differences, time zone constraints, and difficulties managing knowledge within distributed teams. Social and organizational factors hinder effective knowledge transfer, such as a lack of trust and inadequate coordination. The literature points to several best practices for facilitating knowledge transfer, including on-site visits for remote employees, synchronous and asynchronous communication tools, structured documentation, training programs, and establishing communities of practice. This study contributes to the field by offering an updated perspective on knowledge transfer in GSD, providing valuable insights for organizations aiming to optimize their global collaboration strategies. Future work should compare these findings with previous studies to track the evolution of knowledge transfer practices over time and explore new organizational approaches that have emerged in response to the changing dynamics of distributed software development.

Knowledge Management (KM) processes are essential for organizations, allowing them to effectively capture, store, and use their knowledge to make informed decisions. Modern enterprises use computerized systems and relational databases to manage their operational processes. However, a significant challenge remains in transforming insights found in digital documents into actionable data models without overloading business analysts or necessitating constant updates and modifications. This work introduces a method for modeling dynamic environments using a knowledge base. The approach involves creating a world model within a relational database that can be updated using Structured Query Language (SQL) expressions derived from documents that describe changes in that world. The techniques discussed include using agents and Large Language Models (LLMs) to generate SQL commands to keep the database current. The proposed world model aims to remain sufficiently generic and adaptable to handle a variety of entities and relationships across multiple organizational domains. Representing events, objects, and their interactions in a flexible structure ensures that real-world transformations are accurately mirrored in the database. This versatility allows the model to be implemented in different sectors without significantly modifying the underlying data architecture. Integrating these processes with advanced language models, such as ChatGPT, aims to improve the generation of data models and streamline the KM workflow by automating the interpretation of explicit knowledge. This integration of language models and relational databases is intended to enhance the organization, storage, and retrieval of insights, thereby reducing manual effort and improving the knowledge base’s adaptability to changing needs. Overall, the proposed solution seeks to leverage LLMs to assist in modeling data and managing knowledge from explicit sources, providing a practical framework for organizations looking to stay competitive in evolving environments.

Knowledge Management (KM) and Strategic Foresight (SF) are essential for organizational growth, particularly in today’s environment shaped by Information and Communication Technologies (ICT). This study conducts a literature review to investigate the effective integration of KM and SF practices with ICT. It addresses two key research questions: How can KM practices be incorporated into SF processes? How can ICT enhance the management of future-oriented knowledge through SF? The increasing complexity of organizational environments and the challenges posed by information overload and future scenario forecasting highlight the importance of this research. ICT serves as a crucial enabler, utilizing tools such as Big Data analytics, Artificial Intelligence (AI), and the Internet of Things (IoT) to optimize KM and SF practices. However, the integration of these technologies encounters significant barriers that require further exploration. The primary objective of this study is to map the current integration of ICT within KM and SF, emphasizing opportunities, limitations, and the potential contributions of AI to these processes. Utilizing a Rapid Review methodology, which streamlines the systematic review process to provide timely insights, the research involved searches in the Scopus database and a snowballing technique, resulting in an analysis of 12 selected articles. Findings indicate that ICT can significantly improve the efficiency and effectiveness of KM and SF processes, particularly in scenario-building and strategic decision-making. The study identifies specific tools and methods, such as collective knowledge systems and IoT frameworks, that organizations can adopt to enhance these practices. Future research should expand on this groundwork by including larger samples and practical studies to validate the proposed hypotheses. This study aims to encourage organizations to adopt ICT-based solutions, fostering sustainable competitive advantages in dynamic business environments.