Workplace AI Returns Depend on Teamwork, Argues IE Insights

Companies may struggle to turn individual AI productivity gains into business results because their most consequential work requires collaboration, Juan Parodi argues in IE Insights. He contrasts tools designed around individual conversations with decisions involving multiple teams, such as pricing, product strategy and regulatory responses. Drawing on external research, Parodi attributes disappointing enterprise deployments to weaknesses in workflow integration, learning and employee trust rather than model capabilities alone. He cites findings from Deloitte and Oxford Economics associating technology-first approaches with a greater likelihood of disappointing returns. These are research claims reported by the article, not independently verified findings here. Parodi proposes automating predictable activities, using AI to assist suitable analytical tasks and retaining human responsibility for sensitive or relationship-dependent decisions. He also describes research in which AI improved consultants’ performance on some tasks but reduced accuracy on another, emphasizing that suitability varies by task. His central recommendation is to assign explicit ownership, funding and measures to the human side of AI adoption. Without accountability for organizational learning and redesigned collaboration, he argues, access to capable models is unlikely to create a durable advantage.