Shopify has officially rolled out a suite of AI powered commerce tools that promise to make online shopping smarter for both direct to consumer brands and wholesale businesses. At the heart of these updates are predictive product recommendation engines that analyse behaviour in real time. The goal is simple: show customers what they are most likely to buy next, increase basket size and boost conversion rates. Early adopters such as Gymshark and a growing number of B2B sellers are already seeing tangible results.
How the new tools work
Shopify’s AI tools draw on browsing history, purchase behaviour and contextual signals to predict what a customer might want next. Instead of static “related items” lists, the platform now generates dynamic suggestions that adapt as the customer interacts with the site. A shopper browsing a product page might see complementary items tailored to their previous orders, their current cart and even seasonal trends.
For B2B sellers, the same logic applies. Buyers placing regular wholesale orders receive recommendations for products that align with their purchasing patterns or new items that similar customers are starting to buy.
Gymshark’s experience with AI recommendations
Gymshark, a leading DTC fitness brand, was quick to adopt Shopify’s AI capabilities. Known for its highly engaged community and fast moving product cycles, the company needed a way to keep shoppers discovering new lines without overwhelming them with options. By enabling AI driven recommendations on its website, Gymshark began to see higher average order values within weeks.
One example came from its accessories category. Shoppers who added a pair of training shorts to their cart were automatically shown compatible items such as gym bags and compression wear. Because these suggestions were based on patterns from thousands of similar buyers, they felt relevant rather than pushy. Gymshark reported a measurable lift in cross category sales and a smoother checkout experience for customers.
B2B sellers using predictive tools
Wholesale businesses are also benefiting from Shopify’s AI approach. A supplier of catering equipment integrated predictive recommendations into its ordering portal. When a buyer added a set of glassware, the system suggested matching serving trays and storage solutions. These were not generic upsells but items informed by data from similar buyer accounts in the same industry.
The supplier noticed that smaller clients who typically ordered the same items each month began exploring new products. Larger clients used the recommendations to build more complete orders without extra calls or emails. This led to increased revenue per account and stronger retention rates.
Why this matters for marketers
For DTC marketers, predictive recommendations are a way to personalise at scale. Instead of creating endless manual product bundles or seasonal campaigns, teams can rely on the AI to surface the right products at the right time. This frees up resources to focus on creative campaigns and customer engagement.
For B2B marketers, the tools help deepen relationships with buyers. A wholesale customer who feels understood and supported is more likely to stay loyal. Predictive engines act as a silent sales assistant, providing helpful suggestions and reducing friction in the ordering process.
Practical tips for getting started
- Optimise your product data
The AI works best with detailed, accurate product descriptions and well organised categories. - Test on a smaller segment first
Activate recommendations on a single category or buyer group to measure impact before expanding. - Monitor behaviour and refine
Track which suggestions drive clicks and purchases. Adjust your product library or content accordingly. - Integrate with marketing campaigns
Use insights from the recommendation engine to inform email promotions, ad targeting and content planning.
A new standard for commerce
Shopify’s AI commerce tools signal a broader shift in how online retail is managed. Rather than relying on static merchandising rules, brands can now use live data to guide customers to products they genuinely want. Gymshark’s early success and the uptake among B2B sellers show that this approach is not limited to a single sector.
As more businesses adopt these tools, shoppers will come to expect personalised recommendations as standard. For marketers, the challenge is to embrace the technology early, build on its insights and ensure that every interaction feels tailored, helpful and on brand.
