Gen AI May Finally Bridge the Personalization Gap, Experts Say

For decades, personalization has been more of a dream than reality. Businesses promised experiences tailored to the customer, but offers and recommendations still often miss the mark, despite the ability to track which websites consumers click on when they’re online.

With the rise of generative artificial intelligence (AI), will true personalization finally be within reach?

“Personalization has long been a digital promise that’s failed to fully deliver,” Ja-Naé Duane, lecturer at Brown University and research fellow at MIT CISR, told PYMNTS. “We’ve been told for decades that our clicks would unlock tailored experiences, but if you’ve ever scratched your head at a bizarre Netflix recommendation, you know we’re not quite there yet.”

That may be changing. According to Duane, the latest AI systems are moving past rule-based segmentation toward real-time learning and contextual awareness.

“Today’s systems don’t just track what we do; they infer how we feel,” Duane said. “They shift tone mid-conversation, rewrite content on the fly, and evolve as our needs change.” But she also acknowledged AI systems’ limitations. “True personalization is a moving target because human desire is fluid, contextual and often contradictory.”

Past efforts also have come up short because they rely more on segmentation instead of truly understanding the target audience.

“Most so-called ‘personalization’ over the past two decades has been little more than categorization,” Puneet Mehta, CEO of Netomi, told PYMNTS. “Show sci-fi to the sci-fi crowd. Offer 10% off to frequent buyers. That is not personalization. That is pattern matching.” He said what’s changing now is the ability to add a “true human element” to digital interactions, including emotional intelligence and memory of prior exchanges.

According to a PYMNTS Intelligence report, while 83% of consumers are receptive to personalized offers, only 44% find them “very relevant” to their needs and 17% said these were “completely irrelevant.” Moreover, personalization of offers can be a more persuasive selling tool than the discount amount. “The true value of personalizing an offer to a consumer lies in how tightly the merchant tailors it to that consumer’s preferences rather than how much the discount is,” according to the report.

Meanwhile, an SAS survey showed that while 75% of marketers are using generative AI in their daily tasks, only 19% are using it to target audiences. This is “not optimal” because when marketers use gen AI for personalization, 92% said they are seeing higher ROI.

The culprit? Lack of understanding about what gen AI can do at the CMO and senior management level, according to SAS. A separate report by Data Axle cited additional barriers such as organizational silos, legacy systems and regulatory complexity as blocking true personalization. That’s even though 91% of marketers believe that blending a consumer’s personal and professional data will lead to better audience targeting.

Read more: Personalized Offers Are Powerful – But Too Often Off-Base

Moving From Data to Action Is Complex

Jackie Walker, retail experience strategy lead, North America, at Publicis Sapient, told PYMNTS that personalization faltered for several reasons.

“Part of that is about inputs, like the quality of the data that a business has about its customers and how usable it is, and the other part is about outputs, such as what meaningful changes can be made to the interactions that a business has with its customers across the wide range of customer touchpoints,” Walker said. “The complexity inherent in both of these areas has resulted in redefining personalization to just mean better segmentation. … Customers, however, are smarter than that.”

For example, if a diner always orders a burger with no pickles and a restaurant recommends a new Southern-style sandwich with extra pickles, this isn’t meeting the consumer’s needs. For a business, understanding this data for thousands of customers is complex: They have to stitch together the data and perhaps infer some attributes of the customer, then figure out how to act on the data to make an offer.

“AI truly has the power to tackle both sides (consumer and business) of the problem,” Walker said. “With AI, data aggregation and interpretation become far more accessible. You can consider more scenarios than were previously possible. There’s the ability to tie together data assets you couldn’t before.”

But Lei Gao, CTO of SleekFlow, told PYMNTS that while AI has made strides in delivering true personalization, “there is still a disconnect between promise and delivery.”

That’s because artificial intelligence is only as good as the data it ingests. The problem is most businesses still have their data in silos, or lack the infrastructure to make their customer data available for use in real time across systems.

“Without combined, high-quality streams of data, AI recommendations will always fall short,” Gao said.

Second, Gao said true personalization is not just about what a customer likes or dislikes. The context is important as well. “In conversational commerce, for example, a product recommendation will be a function not just of a customer’s purchase history but also of their location in the purchasing process at the time,” Gao said.

Data also should be as fresh as possible to be most effective.

“Personalization is only as effective as the data behind it,” Dean de la Peña, vice president at Resonate, told PYMNTS. “If it’s working with outdated or incomplete information, the insights you receive can be off base.” For personalization to work, he argued, brands need access to real-time signals that reflect not just past behavior but current context — particularly in volatile times when consumer sentiment shifts rapidly.

For example, if a retailer based this year’s marketing campaigns on last year’s back-to-school spending trends, it risks missing the market, de la Peña said. Consumer priorities can shift quickly in response to factors like inflation, fashion trends, shifting cultural attitudes and more.

But not all personalization efforts have failed. Some have succeeded too well.

“This is a two-sided story,” Siamak Freydoonnejad, co-founder at Sprites AI, told PYMNTS. “On one side, AI has already made significant advancements in personalizing content for audiences. One of the most compelling examples would be TikTok or Instagram. The recommendation systems of these platforms curate visual content that feels like it gets you. That’s precisely why it’s so addictive.”

However, “Spotify’s Discover Weekly AI algorithm results are either extremely relevant or horrifically out of touch with reality,” Freydoonnejad said. “These algorithms are highly adaptive and precise and will get it right in most cases. You don’t have to search for what you like; it’s already there in your feed. And that’s the beauty and the curse.”

So will generative AI fix personalization? “While we’re definitely closer today, ‘true personalization’ is still a moving target,” SleekFlow’s Gao said. “It’s less about a magic algorithm and more about building robust, thoughtful systems that combine technology with human-centered design.”

Read more:

Stitch Fix: AI-Powered Personalization Will Overcome Any Macro Challenges

Retailers Embrace the Power of Hyper-Personalization

Retail Personalization Is About More Than Just Sending Offers

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