How GenAI Is Rewriting Retail’s Playbook for Transaction Data

Enterprise transformation isn’t easy. But, as technologies like GenAI rearchitect what is considered possible for business operations to achieve, it’s becoming increasingly necessary.

Against today’s operational backdrop, retail data has become more than just a byproduct of business operations; it’s now a strategic asset.

Findings from the PYMNTS Intelligence and Fiserv collaboration, “GenAI Applications in Retail Transaction Analysis: Industry Trends and Insights,” reveal that the emergence of GenAI has amplified the potential of data, especially transaction and payments data, turning it into a lever for innovation, customer engagement and profitability.

Legacy silos and fragmented tech stacks have stifled innovation in retail. Yet GenAI’s arrival has intensified the urgency for cohesive data strategies. Early adopters are moving quickly to unify disparate data sources, from point of sale (POS) and customer relationship management (CRM) to inventory, social and customer sentiment, and layer governance and accessibility frameworks on top.

More than 90% of retailers surveyed by PYMNTS Intelligence, all generating at least $500 million in annual revenue, have already integrated GenAI into their transaction data workflows.

There’s a widening gap between retailers who are AI-enabled and those who are still manually pulling reports from six different systems. It’s not just about speed. It’s about resilience, adaptability and long-term relevance.

Read more: Why 73% of Retailers Are Increasing GenAI Investments

As the second wave of GenAI adoption begins, retailers face a stark choice: invest in the foundational work of data empowerment, or risk being left behind by more agile competitors.

Retailers that rely heavily on data are leading the charge. Among the top applications of GenAI by these first movers are use cases that range from data privacy and anomaly detection to customer segmentation and forecasting. This wide adoption signals a fundamental industry shift: data, once siloed and underutilized, is now being actively transformed into actionable intelligence.

After all, it isn’t just about advanced technology. It’s about alignment. Businesses that empower their teams with data can be more agile, more customer-centric and better equipped to adapt to change. 

By weaving GenAI-driven insights into everything from marketing to inventory management, these retailers are capable of working to create a responsive, data-informed organization that can pivot quickly when needed.

In this emerging new ecosystem, retailers are seeing real results and already doubling down on what works.

See also: Retailers Increase GenAI Investments, but Need Better Data to Make It Run

One of the key benefits of GenAI in retail is its ability to make complex data more accessible and actionable for employees at all levels. Rather than relying solely on data scientists or analysts, GenAI tools can provide real-time insights, generate predictive models and create visualizations that help teams understand trends and take timely action.

Despite the growing enthusiasm, the report highlights a noticeable gap between ambition and execution. Fewer than one in three retailers rate their data analytics capabilities as “high,” and only 22% express strong confidence in cross-functional data sharing and collaboration. This points to a broader issue: while the tools exist, the organizational readiness to wield them effectively often lags behind.

But this gap presents a significant opportunity. Retailers that recognize the need to build up their data infrastructure — investing in systems, skills and culture — are positioning themselves to gain a long-term edge. And the good news is that GenAI can be a catalyst for that transformation. In firms that use GenAI across multiple functions, confidence in analytics, visualization and reporting increases measurably, creating a reinforcing loop of improvement.

Standing up a culture of data fluency can potentially help organizations avoid the common pitfall of AI as a black box. When stakeholders understand both the possibilities and the limitations, GenAI becomes less of a magic trick and more of a strategic collaborator.

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