Searching together: Human–AI complementarities in innovation search

In the pursuit of innovation, diverse actors engage in frequent interactions. While the current understanding of innovation search rests on human-human interactions, the potential benefits of interactions with non-human actors, i.e., Artificial Intelligence (AI), are emerging in literature. We explore complementarities that arise from these interactions between humans and AI and find that pairs of dialectics that complement each other emerge in the search process. They encompass human-centered and boundary-spanning search, human contextualizing and AI decontextualizing, and human judging and AI envisaging. We posit that these complementarities, produced in interaction, could foster enhanced innovation outcomes as they bring expanded rationality and bias mitigation within the search process.