Retail has no shortage of AI use cases; the greater challenge is operationalizing them consistently across the enterprise. As organizations look beyond experimentation, the conversation is shifting from what AI can do to what the enterprise must do to support it.
To understand what distinguishes organizations that are prepared to expand AI from those that continue to struggle, MathCo partnered with HFS Research to survey senior retail leaders across Fortune 1000 organizations. The study explored how organizations are approaching enterprise AI, where they see the greatest barriers to adoption, and which capabilities they believe will define long-term success.
The findings challenge some of the most common assumptions about AI adoption in retail. What leaders flagged as the biggest obstacles wasn’t what many would expect, and it says less about the sophistication of the models being deployed than about the readiness of the enterprise behind them.
What the Research Reveals
When asked to identify the biggest barriers to scaling AI, retail leaders ranked enterprise data and skilled talent as their top concerns, each cited by 38% of respondents. Governance, integration complexity, and technology infrastructure also emerged as significant challenges. By comparison, factors such as tribal knowledge ranked considerably lower, suggesting that organizational capabilities outweigh institutional know-how when it comes to enterprise AI adoption.
Rather than pointing to shortcomings in AI technology itself, the responses highlight the foundational capabilities required to deploy AI consistently across the enterprise. Retail leaders recognize that achieving enterprise-scale AI requires more than selecting the right models. It depends on the strength of the data, technology, and organizational capabilities that support them.
Why Data Has Become Retail’s AI Bottleneck
Enterprise data has become retail’s biggest AI bottleneck for a simple reason: AI is only as effective as the enterprise context behind it. Unlike earlier generations of analytics, enterprise AI depends on data that is consistent, connected, and accessible across the business. For retailers, achieving that level of readiness has become increasingly difficult as years of digital transformation have expanded both the volume and complexity of enterprise data.
This challenge extends beyond technology adoption. Recent research found that despite strong interest in generative AI, only 4% of Fortune 500 retail executives surveyed reported successfully implementing GenAI at scale. While AI capabilities continue to advance, retailers are still navigating fragmented enterprise environments shaped by ERP modernization, ecommerce expansion, POS upgrades, loyalty platforms, acquisitions, merchandising systems, and supply chain transformation. These investments have strengthened individual business functions but have also introduced disconnected data models and inconsistent information across the enterprise.
Inconsistent product hierarchies, disconnected customer identities, poor data lineage, integration complexity, and migration debt compound the problem, further reducing the enterprise context AI depends on. Without trusted and connected data, AI struggles to deliver consistent outcomes across functions, limiting both performance and business confidence.
Broader industry research reinforces this trend, with 61% of organizations evolving their data and analytics operating model because of AI, underscoring the need for trusted, connected, and governed enterprise data. AI does not eliminate fragmented data. It exposes it, making trusted, connected, and governed data the foundation for enterprise-scale AI.
Enterprise Readiness Is What Separates AI Pilots from AI at Scale
Enterprise readiness is not defined by data or talent alone. The two are complementary capabilities that enable AI to deliver consistent business outcomes. Skilled AI teams can build effective models, but without enterprise data that’s properly governed and easy to access, those models struggle to generate reliable insights at scale. Likewise, trusted data delivers little value without the talent needed to translate it into decisions and operational impact.
More fundamentally, enterprise readiness is about more than technology investments. It reflects an organization’s ability to bring together people, data, governance, and technology within a common operating model that enables AI to scale across functions rather than remain confined to isolated use cases. Organizations that strengthen these foundational capabilities will be better positioned to move beyond experimentation and embed AI across the enterprise.
For retail leaders, the priority is not simply adopting the next generation of AI capabilities. It is strengthening the enterprise foundations that allow AI to operate with consistency, reliability, and business context. Those foundations will ultimately determine whether AI remains a collection of successful pilots or evolves into an enterprise capability that delivers sustained business value.
The Take 5 Report, developed in partnership with HFS Research, explores these themes in greater depth, examining how retail leaders are approaching enterprise AI, the barriers shaping adoption, and the capabilities they believe will define long-term success. Explore the full report for additional research findings and insights into the future of enterprise AI in retail.
To read the report, click here.
