What: A large-scale study of 776 Procter & Gamble professionals reveals that AI-enabled teams outperform traditional work groups, with individuals using AI matching the performance of two-person teams while reducing work time by 12-16%.

Why it is important: These findings provide a concrete blueprint for retailers seeking to optimise their workforce, as the industry grapples with implementing AI solutions that have already shown 15-30% efficiency improvements in customer service operations.

The comprehensive study of P&G professionals demonstrates AI's transformative impact on team dynamics and productivity in knowledge work. The research reveals that individuals working with AI can match the performance of traditional two-person teams, while AI-enabled teams achieve the highest overall performance, particularly in producing exceptional solutions ranking in the top 10% of quality. The study also shows that AI effectively bridges professional silos, enabling both technical and commercial specialists to produce balanced solutions regardless of their background. Notably, less experienced employees working with AI performed comparably to experienced teams, suggesting AI's potential to democratise expertise. The emotional impact was equally significant, with AI users reporting higher levels of positive emotions and lower levels of anxiety and frustration. These findings, based on real-world product development tasks with actual business stakes, provide valuable insights into how AI can enhance both individual and team performance while improving the overall work experience.

IADS Notes: The study's findings align with significant retail industry developments throughout 2024-2025. As reported in February 2025, 87% of retailers implementing AI witnessed revenue increases of 6% or more, while customer service operations showed 15-30% efficiency improvements. The democratisation of expertise was particularly evident in retail applications, with AI-tool-directed traffic increasing 304% year-over-year. However, implementation challenges persist, as only 10% of companies successfully scale their AI applications, though those who do report 50% reduction in administrative tasks and 30% faster development with properly supervised systems.

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