Telecom & Technology Consulting
AI shopping assistants are becoming more and more integral to online retail success. So much so, in fact, that according to Salesforce, 20% of 2025’s holiday ecommerce involved the use of these digital assistants. That percentage will only increase.
But retailers shouldn’t rush into trend. Deploying before they’re fully prepared can be dangerous.
Company leaders are torn. They justifiably feel a sense of urgency to implement AI shopping assistants into their retail landscape. Customers enjoy the instant, personalized recommendations, which lead to more seamless experiences and, in turn, increased loyalty. From the brand’s perspective, the longer they wait to deploy, the more they may fall behind competitors who got into the AI shopping assistant game earlier.
Yet brands first need to create a prudent, unified data strategy. Without question, that’s the most crucial determinant of AI shopping assistant success.
Unfortunately, many brands still have siloed data, with product data in one system, inventory data in another, and customer information in yet another. But everything must be stored and easily accessible from a single source. As the saying goes, your outcomes are only good as the inputs you create. Without data alignment, you will likely lack the personalization that AI shopping assistants are expected to provide.
Data that’s not unified can present other, more concerning problems. For example, the AI assistant may suggest out-of-stock items, provide out-of-date return policies, mistakenly claim that a certain product is compatible with another, and deliver other false information. Customers not only end up being annoyed and inconvenienced, they lose trust in the brand. And that means you’ve likely lost a customer – perhaps forever. That’s what happens when you deploy before your data is airtight.
Beyond unifying your data, it’s also critical to carefully assess where you’ll source your AI shopping assistants. Do you develop in-house or partner with a reputable vendor? Building them on your own involves significant time, resources, and expertise. But even if you possess those assets among your team, developing quality AI shopping assistants can still be a challenge.
Besides, even the best developers may not have the requisite security know-how, which is paramount for protecting the enterprise. Which is why most experts in the AI shopping assistant category recommend working with a vendor instead of developing in-house.
CIOs and CTOs should evaluate vendors based on a range of attributes. Ask to see how the technology can boost sales. Request a demonstration that show how the AI shopping assistant will be part of the entire customer lifecycle, and be imbedded into the overall shopping experience (for best results, it shouldn’t be an isolated tool). Assess if you can use the technology to view all the steps in shopping interactions, which can then be used for future marketing campaigns and updates to the user experience.
Whatever course you take, you must be prepared to regularly update and adjust the technology. The right vendor can help to refresh knowledge bases and add new features on an ongoing basis. With the appropriate technology, you’ll be able to edit product catalogs, rotate marketing promotions and deals, change rules, and shift other content that increases the number of transactions. You can even experiment with new methods to increase engagement.
It’s important, however, to not blindly fall in love with the AI shopping assistant trend. Don’t entirely dismiss the fact that many customers still prefer traditional search. As such, avoid forcing them to use AI shopping assistants. Taking shoppers out of their comfort zone can cause confusion and frustration, which may lead them to go to a competitor’s site. Instead, design the UI in a manner where the assistant is visually present so customers have the option to use it. Accommodate all search preferences.
AI shopping assistants are still relatively new in the ecommerce space. Instead of completely diving in, put a toe or two in the water. Get your data in order. Do your research. Interview multiple vendors. Perform some beta testing. When you’re fully prepared, you’re more likely to achieve long-term success.
