Online shopping is supposed to be easier than ever, until it isn’t. Despite a wealth of choices at our fingertips, nearly 70% of online shopping carts are abandoned before checkout, costing brands an estimated $18 billion annually. It’s a longstanding challenge in e-commerce: customers browse, hesitate, and ultimately leave.
Retailers have spent years optimising search functions and streamlining checkout flows, but the core issue remains, consumers aren’t searching, they’re disengaging.
The way people shop has changed. Platforms like TikTok and Instagram serve up hyper-personalised content that feels intuitive and frictionless. By contrast, traditional e-commerce search forces users to navigate a maze of filters, keyword inputs, and pages of results that don’t always match their intent. Users are being trained to use interfaces where the only thing you need to do to surface information is swipe up, down, left or right, not fill in endless text searches and drop-down check boxes.
The result? A fragmented, frustrating experience that is increasingly out of sync with how people process information and make decisions.
The digital storefronts of today still operate like relics of the early 2000s, where users must know exactly what they want and how to describe it to find the right product. But what if product discovery evolved beyond keyword-based search to something closer to intuition? What if browsing felt as natural as a conversation or as dynamic as a TikTok feed tailored to individual curiosity?
A New Approach to Product Discovery
We have discovered a fundamentally different way to approach product discovery, one that aligns with how people naturally explore, make decisions, and interact with content. Instead of relying on static filters and rigid keyword searches, AI-driven discovery enables a dynamic, fluid experience that adapts in real time. This approach leverages infinite canvas UX, gesture-based interactions, and multimodal AI, allowing users to search not just with text but also with voice, images, and contextual cues.
Imagine a shopper looking for the perfect home sound system. Instead of sifting through endless product specs and trying to decode technical jargon, they could simply say, “I love watching movies and getting fully immersed in the experience. I’d love a sound system that makes my living room feel like a cinema, and it needs to work with my Google Nest so I can control it hands-free.” The AI would instantly surface a curated selection of top-rated, high-quality options, refining recommendations based on whether they prefer hidden speakers, a wall-mounted setup, or a sleek wireless system that blends seamlessly with their space.
Rather than presenting endless pages of static results, AI-powered discovery dynamically visualises options, mapping product relationships in a way that encourages intuitive exploration. This fluid, personalised approach reduces choice paralysis, surfacing alternative options, complementary accessories, and expert recommendations in an organic, easily digestible format.
Unlike traditional e-commerce platforms that prioritise paid promotions and sponsored content, AI-driven discovery prioritises user intent, analysing past interactions, stated preferences, and engagement signals to present only the most relevant products. It eliminates friction, ensuring a shopping experience that adapts in real time rather than relying on outdated search hierarchies.
AI-Powered Product Discovery: Aligning with Human Behaviour
Human decision-making isn’t linear, our brains don’t process information in rigid categories but through associative thinking. When recalling a memory or making a decision, we connect related ideas dynamically, forming a network of associations rather than following a step-by-step path. Research from Harvard University confirms that memory retrieval is not sequential but instead relies on associative connections between concepts, meaning our brains retrieve information in clusters rather than strict hierarchies.
This non-linear processing is reflected in the natural world. Migratory birds and sea turtles navigate vast distances without a rigid, predetermined path, instead adapting their routes in real time based on environmental cues. Similarly, fluid dynamics shows how water moves seamlessly around obstacles, adjusting its course dynamically rather than following a fixed trajectory.
AI-powered product discovery works in much the same way, it removes the rigid constraints of traditional search models, allowing users to explore products in an adaptive, free-flowing way. Instead of being forced down a structured, step-by-step search path, consumers are guided through a dynamic, responsive system that anticipates their needs and surfaces relevant options before they even articulate them.
By mirroring the way humans naturally connect ideas and navigate choices, AI-powered discovery creates a shopping experience that feels intuitive rather than mechanical, unlocking a more natural, fluid way to find and choose products.
How We Process Information: Psychological Insights
Associative Memory & Contextual Recall
When we think about a topic, our brain retrieves related ideas simultaneously, rather than in isolation. For example, if someone is planning a vacation, they don’t think in a strict sequence: destination → hotel → flights. Instead, they might picture a beach scene, which triggers thoughts about sunscreen, swimwear, and luggage—all relevant, but not sequential.
AI-powered discovery mirrors this recall process, surfacing contextually relevant products before a user explicitly searches for them. Instead of requiring users to type in precise keywords, the AI understands broader intent and guides them toward meaningful recommendations based on a web of related ideas, preferences, and inferred needs.
The Influence of Choice Architecture
Studies in behavioural economics show that how choices are structured significantly impacts decision-making. Traditional e-commerce platforms often overwhelm users with long lists and multiple filters, leading to decision fatigue, a key reason for high cart abandonment rates. When faced with too many options in a static, complex format, consumers often hesitate, delay their purchase, or abandon the process entirely.
AI-powered discovery eliminates this friction by curating and reordering options dynamically, much like a skilled salesperson guiding a shopper in-store.
Instead of bombarding users with an exhaustive catalog, AI surfaces only the most relevant selections, ensuring a decision-making experience that feels effortless rather than overwhelming.
Research from Columbia University confirms that choice architecture, how information is presented, can significantly impact consumer confidence and purchasing decisions. By framing options in a way that aligns with how people naturally assess and compare products, AI-powered discovery helps users make faster, more satisfying choices.
The 'Recognition Over Recall' Effect
Cognitive psychology research indicates that people find it easier to recognise something familiar than to recall it from memory, a principle known as the recognition heuristic. This explains why visual browsing consistently outperforms text-based search, users are far better at spotting relevant items when presented with intuitive visual groupings than they are at constructing the perfect search term.
For example, instead of forcing a user to type in specific product names, AI-powered discovery presents curated collections, dynamic visual clusters, and immersive product displays, allowing consumers to recognise what feels right rather than struggling to recall the correct terminology.
Studies on UI/UX design confirm that visual-first experiences significantly increase engagement and decision confidence, reinforcing why AI-powered discovery should prioritise image-based, interconnected browsing over rigid, text-heavy search interfaces.
Exploration vs. Search: How We Navigate Decisions
Human curiosity is driven by exploration, not just problem-solving. Think about how people browse bookstores, art galleries, or even social media feeds, they often wander, follow visual cues, and make unexpected discoveries.
Traditional e-commerce disrupts this natural process, forcing users into search-driven behaviours that feel mechanical and restrictive. AI-powered discovery reintroduces the joy of exploration, allowing users to move fluidly between related product clusters, much like how they would browse in a well-designed physical store.
Behavioral research suggests that non-linear exploration leads to higher engagement and more confident decision-making, as users feel in control of their journey rather than being forced down a predefined, rigid path.
AI-driven discovery embraces this natural behaviour, creating an experience where users don’t just search for products, they discover them, seamlessly moving between recommendations, categories, and interactive content without the friction of traditional navigation systems.
Why This Approach Feels More Natural
AI-powered product discovery eliminates friction by:
- Aligning with natural decision-making processes, surfacing relevant options dynamically.
- Mirroring real-world shopping behaviours, allowing users to explore intuitively.
- Reducing cognitive effort, shifting from a search-driven model to a recognition-based experience.
By adapting to how people actually think and navigate choices, AI-powered systems turn shopping into an intuitive, effortless process.
How Nike Could Leverage AI-Driven Discovery
Nike’s digital storefront today is fragmented, requiring users to navigate an extensive web of filters and categories. A single category, sneakers, breaks down into performance shoes, retro collections, Jordan releases, trend-driven designs, and more. Meanwhile, consumers vary wildly: some know exactly what they want, while others may only have a vague inspiration from an Instagram post.
In a world powered by AI-driven discovery, that experience completely transforms:
- A sneaker enthusiast could upload a photo of a rare pair they love, and the system would instantly recommend visually similar options, factoring in current stock, similar collaborations, and resale trends.
- A casual buyer could simply say, “I need comfortable, stylish sneakers for everyday wear,” and the AI would refine selections dynamically based on their style preferences, foot type, and past purchases.
- A gift shopper could take a picture of the recipient’s shoe rack, and the AI would analyse their collection, suggesting a pair that fits their style without ruining the surprise.
Instead of being forced through static, category-based navigation, Nike’s AI-powered discovery system would allow customers to explore dynamically, surfacing options that fit their lifestyle, aspirations, and personal style.
From Product Discovery to Career Exploration: How We Uncovered These Insights
At Rehab Agency’s Hack Week, we set out to explore a radical question: How can AI be used to create more intuitive, meaningful digital experiences?
By developing Ikigai, we realised that AI-powered discovery could revolutionise not just shopping but decision-making as a whole. Instead of relying on static filters or rigid search, Ikigai learns from each interaction, dynamically surfacing insights and guiding users in real time.
For example, rather than a person searching for generic job titles, Ikigai allows them to speak naturally about their interests and skills. The AI asks intelligent follow-up questions, refining recommendations dynamically and creating interactive AI persona, virtual professionals who provide real insights about the role, industry, and career path.
A job seeker could ask, “What does a UX designer actually do day-to-day?”, and instead of a list of job postings, they’d receive a dynamic, contextualised response from a persona that simulates an experienced UX designer.
Ikigai takes the same principles driving intuitive product discovery and applies them to career exploration, life decisions, and more, removing friction, enabling better choices, and ensuring decision-making feels as intuitive as a conversation.
--
Callum Gill is Head of Strategy at Rehab Agency. Send him an email at callum.gill@rehabagency.ai if you'd like to schedule a demo.
.webp)
.webp)
