The idea of taking a future-oriented approach to selecting artificial intelligence (AI) technologies for knowledge platforms is an important one. It recognizes that the field of AI is rapidly evolving, and decisions made today can have a significant impact on the scalability, adaptability, and relevance of knowledge platforms in the future.

This paper addresses a critical issue in the ever-evolving landscape of AI and knowledge platforms. It underlines the importance of taking a proactive and future-oriented approach when selecting AI technologies to power knowledge platforms. The authors provide a comprehensive and well-reasoned exploration of the key considerations involved in making such decisions.

The authors outline strategies for choosing AI tools and technologies to get the functionality of a cutting-edge information platform that fosters innovation. The development of online information platforms and learning platforms has, up until now, lagged other software-specific concerns such as the selection difficulty. Using an expert Delphi survey and multicriteria analysis techniques, they connect technology recommendations from group decision support exercises to the platform design goals and limitations.

The authors demonstrate that the relationships between the anticipated benefits of employing AI building tools, AI-related system functionalities, and their continued applicability through 2030, were evaluated and used to improve the learning scenarios and plan the platform’s future development. The chosen technologies gave platform management the ability to put the needed capabilities into place, maximizing the potential of open innovation platforms and providing a template for the creation of a useful class of cutting-edge open-access knowledge provision systems. Furthermore, the technique is a crucial component of the digital sustainability and AI-alignment plan for the systems in the class. The knowledge platform was created as part of a European Union (EU) Horizon 2020 research project and serves as a case study for the technique.

After reading the paper and disseminating the results obtained by the authors, I can state that the work of the authors is a valuable contribution to the literature on AI and knowledge management. It has the potential to guide professionals in making informed decisions that will positively shape the future of their knowledge platforms.