Product discovery becomes a core, integrated component of eCommerce operations
In 2026, product discovery is part of the engine. Retailers that still treat their discovery functions as cosmetic or front-end features are already behind. Discovery is now driven by systems thinking. It’s one unified operation where AI combines product data, shopper behavior, logistics, content, and merchandising decisions into a single, actionable stack.
Kate Massey, General Manager for APAC at Athos Commerce, explains this shift clearly: discovery needs to be the shopper’s next logical step, where every signal points the customer toward action. And action without struggle creates loyalty. If retailers don’t get this right, they risk turning their eCommerce experiences into dead ends.
The key enabler behind this evolution is clean, structured product data. When combined with AI, this data doesn’t just make discovery faster, it makes it smarter, aligning suggestions with customer intent. Performance improves across the board. Marketing gets sharper. Fulfillment becomes predictable. Merchandising adapts in real time. Massey sees AI-powered discovery tools like intelligent search and contextual recommendations becoming standard, not a point of differentiation. And that’s the right mindset. Don’t aim to be fancy. Aim to be functional, with precision.
Retailers that unify their data around this ecosystem won’t just beat the competition, they’ll own the customer relationship. The result? You’re not optimizing for a single transaction. You’re building for predictable, scalable engagement.
AI extends product discovery beyond traditional channels
Discovery isn’t happening in search bars anymore. It’s happening everywhere people scroll, watch, speak, or tap. Retail hasn’t outgrown the infinite shelf, it’s just moved it into everything from Instagram stories to customer service chats. If your systems don’t recognize this, you’re denying your customers the ability to buy from wherever they are, on whatever platform they’re on.
Kate Massey highlights the fast rise of conversational commerce. Think about it, an AI like ChatGPT can now take a single message like “I need a black waterproof jacket for winter biking” and generate precise, shoppable options. That means your product data must be structured well enough to be understood not just by humans, but by algorithms interpreting context, emotion, and need in real time.
This is where most commerce stacks fail. They manage data in silos, inventory here, creatives there, nomenclature somewhere else entirely. Massey makes this problem clear: good AI-assisted discovery depends on cleanly labeled, standardized, and enriched product information across the entire stack. That’s what enables consistency. Without it, you’re running an AI engine on noise, not signal.
In this landscape, success comes from measuring new things. Are customers moving from first glance to purchase without hesitation? Are recommendations coming with confidence and context? AI won’t work with guesswork, it thrives on structure. And the smartest retailers are already adjusting naming conventions, creative assets, and taxonomy rules just to get ahead of this. If your systems speak clearly to machines, your brand will make sense to people, anywhere they choose to shop.
Flexible subscription models are poised to become essential revenue drivers
Subscription commerce isn’t going away. It’s pivoting. Consumers are still willing to subscribe, especially for essentials and lifestyle categories, but their expectations are different. They’re more aware of their monthly spending, tracking how many services they’ve signed up for, and pausing more often. Lock-in models aren’t cutting it anymore.
Simon Wharton, Founder of PushON, points out that by 2026, shoppers will favor brands that make subscriptions adaptive. That means the ability to pause, switch to one-time purchases, apply loyalty discounts, or bundle and reorder on flexible terms. This isn’t nice-to-have functionality. It’s table stakes. Most consumers won’t even consider subscriptions that don’t offer some form of control and transparency.
This evolution in consumer behavior is directly tied to broader financial awareness. Every subscription is now judged against overall disposable income. Brands that make adjustment easy, without friction, are going to earn more long-term value.
For executives, the message is simple: rigid systems will bleed customers. Flexible subscription management, integrated with account-level intelligence, is what sustains trust and revenue. And as Wharton notes, this ties into where commerce is headed, systems that support smarter, user-led journeys with lower friction and fewer dead ends. If your retention strategy isn’t adaptive, it won’t be competitive.
Agentic AI systems will evolve to manage and optimize shopping processes automatically on behalf of consumers
AI isn’t just becoming better at suggestions, it’s starting to act independently to serve customers. By 2026, we’ll see more agentic systems that don’t wait for users to request help. They’ll monitor signals, predict needs, and take action. That includes comparing prices, customizing order baskets based on budgets, avoiding product clashes, and fixing problems before they happen.
Simon Wharton makes this shift clear. He describes a near-term environment where AI isn’t assisting, it’s operating. These agentic systems will plan and optimize transactions on behalf of the user, especially in complex or recurring purchase cycles. The AI will know budget limits. It will understand the product ecosystem. It will restructure carts proactively and flag shipping or service issues before they escalate to the customer.
This changes the entire framework of digital shopping. Brands that adopt these systems will stand out by offering useful automation instead of generic intelligence. The risk here isn’t complexity, it’s delay. Companies that don’t start embedding agentic AI early are going to be slow to unlock its potential. And once shoppers start experiencing these benefits, they’ll expect them elsewhere.
C-suite leaders need to think beyond recommendation engines. Start funding infrastructure that puts AI in a coordinating role, not just an assisting one. The payoff is clear: fewer support tickets, smarter commerce journeys, and a deeper sense of service ownership between brand and customer.
Emotional commerce will transform online retail by tailoring user interfaces to the consumer’s emotional state
eCommerce design is shifting focus, from usability to emotional relevance. User interfaces will soon adapt in real time, responding not just to clicks but to how users feel during the interaction. Frustration, curiosity, urgency, these are signals. And by 2026, leading platforms will use those signals to change what the user sees and how the system behaves.
Junwei Huang, CEO of Genstore, explains this transition clearly: layouts and interactions will shape themselves dynamically based on emotional cues. These cues won’t come from static segmentation or assumptions. They’ll come from real inputs, time spent on certain pages, hesitation, aborted actions, and engagement intensity. AI will interpret these patterns and make interface decisions more personalized and more timely.
This isn’t about optimizing for conversions alone. It’s about reducing effort for users who are emotionally fatigued and expanding discovery when users are highly engaged. That level of personalization, grounded in user psychology, will significantly raise the perceived intelligence of the platform and improve satisfaction.
For executives, the operational direction is straightforward: invest in technology that can read and respond, not just instruct and display. Users won’t settle for one-size-fits-all experiences, especially as expectations increasingly lean toward systems that “understand” human context. Emotional commerce will define a new threshold of personalization, where the experience feels responsive rather than static.
The normalization of AI-native brands will hinge on maintaining core service quality alongside innovative AI integration
The average consumer already assumes most brands are powered by AI. By 2026, that assumption becomes default. The more important question consumers will ask is whether the brand delivers, on time, at a fair price, and with smooth resolution when things go wrong. The tech behind it becomes secondary if the core experience performs well.
Junwei Huang, Chief Executive at Genstore, forecasts that the first AI-operated brand, where AI runs most of the business functions, will be profitable by 2026. But that achievement alone won’t spark shock or skepticism among customers. They’ll accept it as long as the outcomes meet expectations. That disbelief threshold is already dropping in most markets.
For brand leaders, the takeaway is not to overemphasize automation for its own sake. Buyers are focused on outcomes, not process architecture. If AI enables better stock availability, more accurate recommendations, or faster returns, it earns trust. If it introduces new friction, it backfires.
The line between human-led and machine-led commerce is fading. Forward-looking companies will make this a non-issue by aligning performance standards across both. In this environment, AI becomes invisible, but only when it’s effective. Executive focus should stay on reliability, clarity, and precision in customer-facing outcomes. AI can do the work, but it can’t replace expectations.
Main highlights
- Product discovery becomes infrastructure: Leaders should embed discovery into core operations by unifying search, content, logistics, and merchandising as one AI-powered data system. This shift drives loyalty through seamless customer journeys and improves performance across departments.
- Multi-channel AI discovery needs data clarity: To support discovery across social, voice, video, and chat, standardized product data must be managed consistently. Executives should prioritize structuring and enriching data to enable precision across all customer entry points.
- Subscription flexibility will drive retention: Rigid subscription models will underperform in a cost-conscious marketplace. Decision-makers should redesign programs to support pauses, one-time purchases, and loyalty-driven flex options to reduce churn and boost lifetime value.
- Agentic AI will optimize full shopping cycles: AI systems will transition from passive assistants to active agents managing pricing, basket composition, and issue resolution. Leaders should invest in AI infrastructure that anticipates and automates customer decisions.
- Emotional commerce redefines personalization: Interfaces will adapt in real time based on user emotion, reducing friction and expanding discovery. Executives should commit to technologies that make platforms feel emotionally responsive, raising satisfaction and conversion rates.
- AI-native brands will gain share if service holds: Consumers will accept AI-led experiences if they deliver on reliability, price, and support. Leadership must ensure AI performance meets or exceeds existing service benchmarks to maintain brand trust.
