Synchronizing innovation: unveiling the synergy of need-based and curiosity-based experimentation in AI technology adoption for libraries

Abstract

Purpose In the context of libraries adopting artificial intelligence (AI) technology, this paper aims to demonstrate the synergy between two different yet complimentary frameworks, need-based experimentation (NBE) and curiosity-based experimentation (CBE). It looks at how these frameworks interact and operate together to promote technological innovation and innovation in libraries. Design/methodology/approach The authors’ extensive professional experience in the AI adoption and innovation of libraries is drew upon in this paper. The methodology encompasses empirical observations of various libraries engaging in digital innovations through experimentations with AI technology adoption practices. Using the frameworks of NBE and CB), these observations are examined to find patterns, relationships and mutual reinforcement between the two methods. The analysis of this study is built on the authors’ observations and real-world case studies. Findings The research reveals that NBE and CBE work together to provide libraries with all-encompassing adoption methods for AI technology. This study indicates a dynamic interaction between NBE and CBE that boosts libraries’ methods for adopting AI technology. NBE acts as a catalyst for CBE by raising awareness of specific library needs, prompting librarians to explore AI technologies aligned with those needs. This synergy empowers librarians to creatively experiment with technology solutions that directly address pressing library challenges. Conversely, CBE fuels NBE by promoting group learning among diverse team members and fostering individual motivation to tackle library needs collaboratively. As they explore AI technology out of personal curiosity, librarians make important contributions that enhance NBE. Originality/value The novel aspect of this study is the recognition of the complementarity between NBE and CBE frameworks, which suggests that libraries should view them as intertwined rather than two separate approaches. Focusing on both methodologies increases the culture of experimentation and improves the problem-solving abilities of librarians. Innovation is fueled by controlled experimentation and innate curiosity in an atmosphere that is fostered by the mutual influence of NBE and CBE. This synthesis offers libraries a comprehensive strategy for adopting AI technology, empowering them to manage the shifting environment and realize the revolutionary promise of AI technologies.