Future of Work
T eamwork is the social interaction – people enjoy connecting with other people. Teams also tend to produce better results than individuals. They can tackle more complex problems, pool their expertise, and distribute the workload to improve efficiency. Credit for their collective endeavours can also be shared in a way that is good for morale. As the old adage goes, “There is no ‘I’ in team.” But what about AI? Many organisations have already adopted AI tools in one form or another. Could the next step be cybernetic teams where humans and AI work hand in hand? After all, most Large Language cornerstone of many modern organisations. This is hardly surprising. It meets a basic need for Models (LLMs) – such as ChatGPT, Claude, and Google Gemini – are trained on human language and often act more like a person than a machine. If businesses were to treat generative AI as a team member, what impact would that have on performance, collaboration, and innovation? I set out to explore these questions in my recent research with colleagues from Harvard, Wharton, ESSEC Business School, and Proctor and Gamble (P&G). We conducted a field experiment with 776 professionals at P&G, which owns global brands such as Gillette, Oral B, and Pampers. The professionals were all new product development experts; some had experience in research and development (engineers and scientists), others were marketing and commercial experts. Each professional was asked to complete a product innovation challenge. Some worked
individually, others in pairs. Some had access to AI, others did not. Our findings have significant implications for how organisations should structure teamwork in the age of AI. 1 . AI boosts performance. Individuals who did not use AI produced the lowest quality proposals. Those working in two-person teams produced higher- quality outputs, but took slightly longer to do so. Crucially, individuals who used AI produced better results than both lone workers and teams who had no access to the technology. They weren’t just more effective. They were also more efficient, finishing the task 8–10 minutes faster on average than those working without AI. Generative AI allowed them to quickly access a wider range of expertise (which they would traditionally seek from human colleagues), interrogate that information, and refine their work. In fact, it did this so effectively that – for some collaborative tasks – it could act as a substitute for their human teammates. This was underlined by the fact that teams which used AI performed only marginally better on average than individuals who did so. However, further analysis did highlight an additional benefit to using AI as part of a team. Many organisations place particular emphasis on what we call ‘exceptional’ outcomes. These are ideas which could generate disproportionately large returns if they were implemented. Therefore, we ranked the quality of the ideas the workers produced. This revealed that teams using AI were three times more likely to produce solutions that were ranked in the top 10 per cent.
This suggests that businesses seeking ‘breakthrough’ ideas would be well-advised to train their workforce to use AI effectively as part of their teamwork, not just as individuals. 2 . AI can break down silos. In many organisations, expertise is confined to silos. This restricts the flow of information between teams and leads to duplicated work, slower decisions, and lower efficiency. Our findings demonstrate that LLMs can remove these barriers by democratising expertise. “As the old adage goes, ʻThere is no ʻIʼ in team.ʼ But what about AI?” When workers had no access to AI, their ideas tended to reflect their background. R&D professionals suggested more technical solutions; commercial staff focused on their own field of expertise. Those with little experience of product development performed poorly. When it came to teams, we found their proposals tended to reflect the professional expertise of the more influential team member. However, when they had access to AI, both individuals and teams produced more balanced proposals that covered technical and commercial considerations. This suggests that AI can help professionals to operate across traditional boundaries and adopt a more holistic approach to solving problems.
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