Combining Human and Artificial Intelligence: Hybrid Problem-Solving in Organizations

Organizations increasingly use artificial intelligence (AI) to solve previously unexplored problems. While routine tasks can be automated, the intricate nature of exploratory tasks, such as solving new problems, demands a hybrid approach that integrates human intelligence with AI. We argue that the outcomes of this human–AI collaboration are contingent on the processes employed to combine human intelligence and AI. Our model unpacks three hybrid problem-solving processes and their outcomes: Compared to human problem-solving, autonomous search generates more distant solutions, sequential search enables more local solutions, and interactive search promotes more recombinative ones. Collectively, these hybrid problem-solving processes broaden the range of organizational search outcomes. We enrich the behavioral theory of the firm with a technology-conscious perspective of organizational problem-solving that complements its traditional human-centric perspective. Additionally, we contribute to the literature on AI in management by extending its scope from using predictive AI for routine tasks to generative AI applications for more exploratory tasks.