How To Build The Collective Intelligence Of Humans And Machines

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Looking to stay on top of the latest news and trends? With MyDeloitte you'll never miss out on the information you need to lead. Simply link your email or social profile and select the newsletters and alerts that matter most to you. In the management world right now, it is common to think about AI in terms of automation vs. augmentation. Whereas automation implies machines taking over tasks previously performed by humans, augmentation refers to the cooperation between humans and machines in executing tasks.

According to the conventional wisdom, augmentation leads to higher performance than automation because it alleviates downsides such as short-term thinking, loss of flexibility due to lock-in, and loss of human intuition and skill. That is, downsides that counter long-term success. We are on the cusp of a new wave of hybrid work where organizations won’t just mix in-person and remote workers—they’ll pair humans and AI agents as co-workers. These AI agents will have the ability to take and act on decisions independently and will not be reliant on detailed user inputs, as today’s mainstream GenAI tools are. For example, they will be capable of interpreting context, adapting dynamically to new information, independently ideating, and even partnering with human colleagues to tackle complex and varied tasks. AI agents are set to go beyond simply augmenting humans to being true co-workers alongside us.

By combining human and AI capabilities, these hybrid teams promise to create new possibilities to deliver competitive advantage far beyond incremental productivity gains. This coming shift also demands thoughtful leadership to balance human workers and AI technologies to ensure the unique strengths of each are maximized. In large global organizations, many workers already find themselves collaborating through Slack or Microsoft Teams with colleagues they have never spoken to, let alone met in-person. Even with close colleagues, these real-time digitalinteractions often outnumber face-to-face meetings. Today, there is another human at the other end of those interactions, providing their expertise or performing a specific task. While many workers have already begun incorporating GenAI tools, like ChatGPT, to help with targeted analyses and tasks, the increasing maturity of AI will take this relationship a crucial step further: rather than being...

This emerging hybrid workforce has been made possible by advances in the natural language processing of large language models (LLMs) that enable humans to communicate with AI agents in the same way they would... The reasoning capabilities of LLMs allow natural language instructions to be translated into action without the need for prescriptive code or detailed instructions, or even well-defined steps. Inputs can be more notional, and the AI coworker can still develop and execute a plan, coming back for feedback as needed. In many ways, the interactions of humans and AI colleagues will be analogous to human passengers in self-driving cars. The cars require a destination, but not specific instructions on when to brake or accelerate. Self-driving cars plot a course, but also receive new data about their surroundings, processing it to plan and execute actions.

AI coworkers will be able to act similarly: interpreting context, interacting with other tools and external systems to develop a plan, and even making certain decisions autonomously. They will also maintain task memory so they can learn and improve on the jobs they do regularly. Correspondence should be sent to Pranav Gupta, Gies College of Business, University of Illinois, Urbana‐Champaign, 6 Wohlers Hall, 1206 S. Sixth St., Champaign, IL 61820, USA. Email: pranavgu@illinois.edu Revised 2023 Jun 12; Received 2022 Jun 30; Accepted 2023 Jun 12; Issue date 2025 Apr.

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. Artificial Intelligence (AI) powered machines are increasingly mediating our work and many of our managerial, economic, and cultural interactions. While technology enhances individual capability in many ways, how do we know that the sociotechnical system as a whole, consisting of a complex web of hundreds of human–machine interactions, is exhibiting collective intelligence? Research on human–machine interactions has been conducted within different disciplinary silos, resulting in social science models that underestimate technology and vice versa. Bringing together these different perspectives and methods at this juncture is critical. To truly advance our understanding of this important and quickly evolving area, we need vehicles to help research connect across disciplinary boundaries.

This paper advocates for establishing an interdisciplinary research domain—Collective Human‐Machine Intelligence (COHUMAIN). It outlines a research agenda for a holistic approach to designing and developing the dynamics of sociotechnical systems. In illustrating the kind of approach, we envision in this domain, we describe recent work on a sociocognitive architecture, the transactive systems model of collective intelligence, that articulates the critical processes underlying the emergence... We connect this with synergistic work on a compatible cognitive architecture, instance‐based learning theory and apply it to the design of AI agents that collaborate with humans. We present this work as a call to researchers working on related questions to not only engage with our proposal but also develop their own sociocognitive architectures and unlock the real potential of human–machine...

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