Artificial intelligence is reshaping how organizations innovate. Digital technologies have challenged established assumptions about innovation processes (Nambisan et al., 2017), while AI increasingly permeates organizational activity (von Krogh, 2018). The innovation process spans idea generation, development and implementation, supported by cross-cutting activities: the evaluation and selection of ideas and projects, intellectual property management, performance assessment and the acquisition of external technological knowledge. AI, including generative AI, is expanding technological support for these activities through pattern recognition, prediction, content generation and decision support (Haefner et al., 2021; Füller et al., 2022; Mariani et al., 2024).

Within this landscape, Technology Intelligence (TI) offers a lens for examining the transformation of innovation work. TI comprises the activities through which firms identify information needs, search for and interpret technological developments, and communicate intelligence to support innovation decisions (Kerr et al., 2006; Kerr and Phaal, 2018). Its configuration reflects different information requirements (Mortara et al., 2009), while its value depends on how it reaches decision makers (Mortara, 2015). Research has examined the reorganization of TI under digitalization (Fang et al., 2023), capability development (Noh et al., 2023), and the integration of expert knowledge and machine learning in patent analysis (Park and Lee, 2026).

Building on these contributions, the track examines AI across the innovation process, with particular attention to TI and the interdependencies among activities. Automated search may change which signals attract attention and which forms of expertise are needed to interpret them. Generative AI raises further questions about how interpretations are produced, validated and communicated. Human–AI complementarity emerges as an outcome whose conditions and limits require investigation. Viewed as an ecology of knowledge, innovation involves human expertise, artificial systems, organizational routines and external knowledge sources jointly shape what organizations notice, understand and act upon. The track invites research on the conditions that sustain diverse sources and interpretations, enable scrutiny of AI outputs, and preserve accountable human judgment.

These questions also concern system design. Interaction patterns, transparency, opportunities to challenge outputs and prompts for deliberate reflection may influence whether AI strengthens judgment or encourages uncritical reliance. Viewing AI through the lens of digital agency (Ågerfalk, 2020) opens questions about how systems can expand individual and organizational capacities to translate knowledge into action.

For managers, these questions concern the design of innovation and intelligence processes, investment in organizational capabilities (Noh et al., 2023), and whether technological support improves the use of knowledge in decisions. The track welcomes theoretical, empirical and methodological contributions on AI in innovation management, particularly on technology intelligence, scouting, foresight and roadmapping practices. Qualitative, quantitative and mixed methods are encouraged, including expert-based and computational approaches, as are studies of universities, intermediaries and providers.