Retailers face data hurdles as just 11% ready for full-scale AI

A new report from Amperity reveals that although nearly half of all retailers are engaging with artificial intelligence tools on a weekly basis, only a small minority are fully prepared to scale these initiatives due to persistent data challenges.

The 2025 State of AI in Retail report surveyed 1,000 retail professionals from sectors ranging from marketing and IT to executive leadership. According to the findings, 45% of retailers currently use AI daily or several times per week, and 97% plan to maintain or increase their AI investment within the next year. However, just 11% report they are ready to implement AI tools at full scale across their organisations.

Tony Owens, Chief Executive Officer of Amperity, emphasised the importance of unified, actionable data in realising AI's benefits.

"Retailers believe in AI's potential to drive loyalty and lifetime value - but belief alone won't close the gap between ambition and execution," said Tony Owens, CEO of Amperity. "What's needed is unified, actionable customer data - regardless of where it resides. With Amperity's patented identity resolution, retailers can unify data without moving it, transforming fragmented records into complete customer profiles that fuel AI and measurable business results."

Customer-facing uses lagging

The report highlights a gap between retailers' expectations and actual deployment of AI in customer engagement. While 63% expressed confidence that AI will foster greater customer loyalty and 65% expected it to increase customer lifetime value, only 43% currently deploy AI in customer-facing roles such as personalisation, chatbots, or bespoke marketing experiences. Moreover, a mere 23% use AI systems in production to resolve customer identities or ready their data for marketing use, indicating a sizeable unrealised opportunity.

Role of customer data platforms

The adoption of customer data platforms (CDPs) appears to be a significant factor separating those retailers making regular and effective use of AI from those lagging behind. The survey revealed that 60% of retailers equipped with a customer data cloud use AI at least weekly, compared to only 29% of retailers without this infrastructure. Additionally, 35% of CDP users employ AI for production-level data preparation for marketing or analytics, suggesting a tangible advantage over the 9% without such platforms. Furthermore, 22% report full-scale AI adoption across multiple departments, while only 10% of those without a CDP do the same.

This alignment between high-quality, unified data and accelerated AI maturity is also reflected in retailers' confidence in understanding and deploying customer insights. The findings suggest that the presence of a CDP is becoming an essential element in modern enterprise AI strategies.

Tapan Patel, Research Director at IDC, commented on these shifts within the retail industry.

"This survey highlights a pivotal shift in retail: AI is moving decisively from experimentation to scaled execution. Retailers are leveraging AI to deliver more adaptive, personalised customer experiences powered by high-quality, unified customer data," said Tapan Patel, research director at IDC. "Retailers who integrate AI into core workflows - across supply chain, merchandising, and engagement - are setting the new standards for customer experience in an increasingly dynamic market."

Technical and data limitations

The upbeat sentiment about AI's prospects is tempered by ongoing challenges, particularly related to data fragmentation and technical resources. According to the report, 58% of respondents described their customer data as fragmented or incomplete, presenting significant hurdles to coherent AI implementation. High costs associated with AI tools were cited by 46%, and limited technical expertise was a concern for 35% of those surveyed. Only 21% of participants expressed strong confidence in their current abilities to act on customer data effectively.

The report's findings underscore a pressing need for strategies that allow retailers to bring together diverse, siloed data sources without compromising on compliance or operational efficiency. As the results indicate, achieving measurable business outcomes with AI hinges largely on solving these foundational data issues.