Retail has always belonged to the brands that understand their customers. 20 years ago, that meant remembering a familiar face, or recommending a product based on a quick conversation at the checkout. Today, it means helping people cut through the noise and find what feels right for them, quickly and confidently. AI is now doing this at a scale no team could match, guiding shoppers with suggestions that feel truly intuitive, as opposed to intrusive.
Consumers are responding. 69% say they’re satisfied with the product recommendations they receive, which shows a clear appetite for guidance that feels useful and natural. For retailers, this is the time to rethink how product discovery works, how intent is interpreted, and you can support your customers at every step of the journey.
The New Age of AI-Driven Discovery
Customers now expect product discovery to feel quick, clear, and relevant. SAP Emarsys research shows that shoppers are already turning to AI throughout the earliest stages of their journey, and AI is shaping these moments behind the scenes to help people find what they want with less effort and more confidence.
What customers are saying about the AI experience
AI is quietly supporting the decisions that customers make every day, and retailers are beginning to feel the impact. This shift is creating a new standard for how product discovery should work across every channel.
Setting the Foundation for What Comes Next
As customers rely more on AI to help them search, compare, and make decisions, retailers need a foundation that can keep pace. AI-powered recommendations only work when the underlying data is connected, consistent, and available in real time. That’s what allows AI to recognize intent, understand behavior patterns, and surface products that truly match what customers are looking for.
You don’t need to overhaul your entire strategy to make this possible. Many retailers start with simple behavioral signals and a unified view of the customer, then let their marketing platform handle the complexity in the background. This is where an AI engine like SAP Emarsys becomes valuable. It interprets intent in real time, adapts recommendations automatically, and supports every channel with the relevance customers now expect.
How AI Transforms Every Step of the Customer Journey
SAP Emarsys research shows that shoppers use AI from the earliest stages of exploration, which gives retailers a powerful opportunity to influence decisions long before a customer reaches the checkout.
By interpreting behaviour in real time, AI builds a clearer picture of what each customer is trying to achieve. Those insights shape recommendations across the entire journey, guiding shoppers with suggestions that feel timely, relevant, and consistent across channels. Instead of treating each touchpoint as a separate moment, AI connects the dots and turns them into a single, fluid experience.
Discovery
The first interaction is often subtle, yet full of intent. A quick search, a product view, or a category click gives AI enough context to begin shaping relevant suggestions.
What AI interprets at this stage:
- Early browsing cues
- Category signals
- Affinity patterns between similar shoppers
- Returning visitors showing renewed interest
How it improves the experience: AI surfaces products that match early behaviour rather than broad assumptions. It helps customers narrow their options and reduces the noise that often slows discovery down. With many shoppers already using AI to explore products and compare choices, this moment has become a natural point of influence.
Consideration
As your customers delve deeper into your product offering, an AI marketing platform sharpens its understanding. If a shopper revisits an item, checks alternatives, or compares specs, AI adapts to those behaviors.
At this stage AI can:
- Recommend closer matches
- Highlight better-fitting alternatives
- Adapt suggestions to budget or preference patterns
- Recognise hesitation and guide customers forward
Why this matters:
More than half of consumers say AI makes shopping easier and faster, which is exactly what the consideration stage requires. Relevance here builds confidence, and confidence drives conversion.
Conversion and beyond
When your shopper is close to buying, AI removes friction. It can offer timely nudges, highlight stock availability, recommend complementary items to increase AOV, or re-engage customers who appear to be drifting away.
AI supports these engagements by:
- Sending reminders at the right time
- Suggesting product alternatives if something is unavailable
- Recommending add-ons that enhance the purchase
- Guiding post-purchase journeys with personalized next steps
Why this matters:
Retailers using AI report stronger engagement and higher loyalty, showing the long-term value of well-timed recommendations that extend beyond a single transaction.
Explore how AI is reshaping retail engagement and loyalty in this new global report
Real Brands Using AI Recommendations to Drive Measurable Results
If you’re exploring how AI recommendations could work in your own strategy, it helps to see how other retailers are already putting them to use. The SAP Emarsys research offers a clear message. Once AI is connected to real behavioural data, it becomes far easier to guide customers with relevance, support each stage of the journey, and unlock results that feel meaningful rather than incremental. These examples can help you picture what this could look like in your own world:
Gibson: Turning insights into personalized journeys
Gibson faced the same challenge many retailers recognise. They wanted to reach customers with meaningful recommendations, not generic messages. By using AI to interpret behaviour and personalize journeys at scale, they now drive 46% of revenue through automated campaigns, freeing their team to focus on creativity rather than manual execution.
City Beach: Re-engaging customers with timely suggestions
If retaining defecting customers is on your radar, City Beach’s approach is a useful reference point. Their team uses AI to spot when customers are drifting and recommends products that match each individual’s lifecycle stage. The outcome was a 48% win-back rate within 90 days, powered by automated journeys that respond in the moment.
Arezzo: Matching “right now” intent with the right product
Arezzo wanted to respond to customers in real time rather than relying on static segments. With AI detecting intent in the moment, they deliver recommendations that reflect what shoppers are doing right now, not what they did weeks ago. This approach helps them deliver more accurate suggestions and build longer-lasting relationships.
Turn AI-Powered Product Recommendations into Real Results
Ready to put these ideas into practice? The SAP Emarsys platform gives you the foundation to deliver personalized recommendations at scale. Connect your data, understand intent, and engage your customers with relevance across every channel.
About the author
Mikkel Tophoj has worked in digital marketing for 11 years with a focus on strategy. As a Services Consultant at SAP Emarsys and previous roles at agencies, he has helped Fortune 1000 brands across all industry verticals connect their data to create dream customer experiences and drive measurable results. Keeping up with innovation, he is focusing on how AI can amplify marketing efforts. He holds a BS in business administration with an emphasis in marketing and a minor in economics from CU Denver.
