What: Retailers face a complex balancing act in implementing GenAI, needing to address infrastructure and data integration challenges while building robust monitoring systems, as the technology moves from experimental to essential for competitive advantage.

Why it is important: The gap between successful GenAI implementers and those struggling with infrastructure and data integration challenges threatens to create a growing competitive divide in the retail industry, making strategic implementation crucial for long-term survival.

The implementation of GenAI in retail is reaching a critical phase where success requires mastering multiple technical and operational elements. Companies must carefully select between open-source and closed-source foundation models while building robust enterprise AI foundations - a challenge evidenced by only 10% of companies successfully scaling their GenAI applications. Data integration remains a significant hurdle, with nearly half of retailers struggling to make their data accessible and connected. However, the potential rewards are substantial, as demonstrated by early adopters reporting revenue increases of over 6%. Success requires not just implementation but also robust monitoring systems, with leading companies achieving 30% faster application development and 60% higher user satisfaction rates through structured evaluation approaches.

IADS Notes: The retail industry's approach to GenAI implementation is reaching a critical juncture in 2024. While retailers must navigate the choice between open-source and closed-source foundation models, with 70% planning implementation this year , the technical infrastructure remains a significant challenge, as evidenced by only 10% of companies successfully scaling GenAI applications. Data integration poses a particular challenge, with a recent Salesforce study revealing that nearly half of retailers struggle with data accessibility despite high AI adoption rates. However, success stories are emerging, with 87% of early adopters reporting revenue increases of 6% or more. The emphasis on operational monitoring is growing, with companies implementing structured evaluation approaches that have led to significant improvements, including 30% faster application development and 60% higher user satisfaction rates . Walmart's success in enhancing 850 million product catalog data points demonstrates the potential when these elements are effectively combined.

Laying the tech foundation for Gen AI success