What: GenAI tools and decentralised experimentation are enabling employee-driven innovation in retail, but only a minority of companies have built the systems needed to scale these gains.

Why it is important: The gap between GenAI adoption and successful scaling underscores the need for investment in infrastructure, oversight, and employee engagement.

Retailers are increasingly turning to GenAI tools and decentralized experimentation to unlock employee-driven innovation, yet most organizations remain unable to scale these benefits across their workforce. While advanced AI agents and GenAI platforms are transforming decision-making, customer experience, and talent development, only about 10% of retailers have successfully implemented the robust systems required for widespread impact. Those that do achieve measurable results, such as 4.5% annual productivity growth and revenue increases of 6% or more, by moving beyond isolated pilots to comprehensive transformation and knowledge-sharing frameworks. The most successful companies treat GenAI as an “exoskeleton” that augments human capability, empowering employees to tackle previously unattainable tasks and fostering a culture of continuous adaptation. However, persistent challenges in oversight, transparency, and technical infrastructure prevent the majority from realising GenAI’s full potential. These findings confirm that sustainable innovation depends not just on deploying new technology, but on building the organizational systems, trust, and engagement necessary to amplify and scale employee contributions.

IADS Notes: By September 2025, advanced AI agents had begun transforming retail decision-making and talent development, but only 10% of companies had scaled these solutions due to oversight and transparency challenges. July 2025 research emphasised that scalable value from AI requires reimagining operations, while March 2025 findings showed GenAI’s “exoskeleton” effect on talent and productivity. Leading retailers achieved 4.5% annual productivity growth by integrating AI, and those investing in infrastructure and robust monitoring systems reported faster development and higher user satisfaction.

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