Retailers face hurdles in deploying AI

INDIANAPOLIS — Retailers and consumer brands are adopting artificial intelligence to enhance customer engagement, yet fragmented data systems are keeping many companies from realizing the technology’s potential, according to new research from Emarsys.

The SAP company’s 2026 Global Engagement Index found that 78% of enterprises consider AI essential, yet 77% are unable to operationalize it effectively. The study surveyed 10,000 consumers and 4,800 enterprise decision-makers across six countries.

The challenge is especially significant as retailers increasingly use AI to personalize communications, anticipate customer needs, and automate interactions across digital channels.

According to Emarsys, 54% of enterprises cannot access and use real-time data. Another 60% have “dark data,” information that is collected but never activated, and 55% say their data is too unstructured to use effectively.

Those limitations can become especially apparent when customer-facing AI systems are disconnected from operational data such as inventory availability, purchase history, order status, fulfillment timelines, and customer service activity.

A shopper, for example, could receive a recommendation for an out-of-stock health or beauty product, a promotion for an item already purchased, or a marketing message that fails to account for a recent customer service interaction.

Emarsys argues that integrating operational data with behavioral and marketing information is necessary for AI-powered engagement to become an enterprise capability rather than merely another campaign tool.

AI-powered customer engagement uses technologies such as machine learning, predictive AI, generative AI, and AI agents to identify patterns and determine which interactions may be most relevant to individual customers.

That differs from traditional marketing automation, which typically follows predetermined rules. AI systems can interpret shifting behavior and adjust recommendations or communications without marketers having to program every possible customer scenario.

But those capabilities depend heavily on the quality and breadth of the information the system has access to.

For retailers operating across pharmacy, health and wellness, beauty, and other high-frequency categories, integrating customer behavior with operational data could help ensure that AI-driven communications reflect not only what customers may want but also what the retailer can actually provide.

Emarsys said that closing that data gap will be critical as companies expand their investments in AI-powered personalization and customer engagement.