Use AI to optimize a retail omnichannel customer experience

Lead with a good first impression

Retailers often judge the success of AI-powered personalization based solely on online conversion metrics, and are disappointed when results fall short. But this narrow view ignores how customers actually shop. Research shows that over 73% of consumers use multiple channels before making a purchase. A shopper might discover a product through an AI-powered recommendation, then buy it in-store.

If only the online interaction is measured, that critical first touchpoint is wrongly deemed a failure. In reality, it played a pivotal role in driving the sale. This attribution blindness hides the true value of the AI touchpoints that helped drive the sale, leading retailers to misjudge what’s really influencing conversion.

Leading retailers are addressing this by linking customer interactions across digital and physical channels, connecting recommendation engines to loyalty accounts, mobile app activity, and in-store purchases. This more complete picture allows retailers to optimize AI’s influence throughout the entire journey.

“Customers expect the same brand experience online and in-store, and that requires AI-fueled consistency across systems,” said Sanjiv Raman, Principal of Technology Modernization Services at Grant Thornton.

The personalization paradox

Many retailers assume that more personalization equals more engagement. But when AI efforts jump the gun, greeting first-time visitors with aggressive pop-ups, chatbots, and personalized offers, the result can feel pushy or even creepy.

According to a Forrester survey, 66% of companies target first-time visitors with personalization, yet only 38% of consumers actually want personalized offers at that stage. Most shoppers prefer to explore on their own before indicating purchase intent.

Timing is everything. Personalization should be adaptive, beginning with subtle improvements to search, navigation, and category relevance, then deepening as customers show interest through behaviors like lingering on products or initiating searches.

High-end retailers especially understand the value of restraint. Over-personalization can cheapen the brand experience. Instead, they lead with thoughtful curation, then invite customers to opt into deeper personalization through accounts or consultations.

“Quality AI experiences are moving toward something highly tailored but only after there’s enough information to personalize meaningfully,” said Raman. Shifting from “personalize everything now” to “personalize when ready” creates a more respectful, effective experience.

Unified data doesn’t equal unified experience

Retailers have spent heavily to build a “single customer view,” consolidating data across systems through cloud migrations and data platforms. But the tech alone doesn’t solve the real challenge: disconnected operations.

It’s common for mobile apps to ignore recent web activity, or for call center agents to lack context from in-store conversations. The issue isn’t the data, it’s how the organization uses it. Legacy workflows, siloed departments, and misaligned goals prevent retailers from turning unified data into unified experiences.

E-commerce and brick-and-mortar teams often evolved independently and still compete for resources. IT considers its job done when a data lake goes live. Marketing moves on after launching campaigns. Without cross-functional coordination, personalization falters, no matter how advanced the AI.

The solution isn’t more technology, it’s an operational shift. True omnichannel success requires not just a single source of data, but a shared understanding of how to use it across every customer touchpoint.

“It’s not just automation, it’s about aligning staff, training, and culture with the new customer experience,” says O’Connell.

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The AI profit test

AI personalization can feel like a bottomless investment with unclear payoff. The tools are expensive, the data complex, and the benefits often buried in broader customer behavior.

Forrester calls this the “AI cost center crisis.” Retailers deploy advanced personalization engines but struggle to prove ROI, especially when attribution is broken or disconnected from revenue reporting.

The fix: treat AI like any other product: with hypotheses, metrics, and accountability. Track “personalization profit and loss” with clear goals tied to conversion lift, average order value, or service cost reduction. Pre-deployment forecasting can guide smarter investment, and ongoing tracking helps identify what’s working and what’s not.

Organizations that build financial discipline around AI efforts unlock a virtuous cycle: high-impact initiatives earn more funding and scale quickly, while underperforming ones are improved or retired.

Addressing privacy concerns

AI personalization thrives on data, but that data is getting harder to collect. With consumers opting out of cookies, using VPNs, and blocking tracking apps, many retailers are flying blind.

When AI lacks high-quality input, it can produce generic or irrelevant experiences that frustrate customers even further. Worse, aggressive or covert tracking tactics can erode trust and damage brand equity.

The path forward isn’t to collect more data, it’s to collect it better. That means asking customers directly, giving them control, and clearly communicating the benefits. Preference centers, quizzes, and style profiles can provide high-signal insights while strengthening trust.

Nearly half of consumers are willing to share personal data when their privacy is respected and the value is clear. Retailers who embrace privacy not just as a compliance issue but as part of the customer experience will unlock deeper loyalty and more robust personalization, even with less data.

Trust becomes the differentiator. And trust is earned.