The way we shop online is on the cusp of a massive shift. Over the next decade, the familiar routine of searching, browsing, and clicking through websites will evolve into something far more integrated and conversational. Artificial intelligence is moving from a background role in search algorithms to a central player in our purchasing journey. Get ready for a future where commerce may become less about navigating websites alone and increasingly shaped by interactions with intelligent systems.
This post explores the potential future of ecommerce and what your business could encounter over the next ten years. We will look at how AI is reshaping product discovery and checkout, its potential to become the new advertising frontier, and the foundational steps you can take to prepare your business for this new era.
Online shopping has reached a point of diminishing returns for many consumers. What was once efficient can now feel bloated, repetitive and time-consuming. Searching across marketplaces, brand sites and reviews often involves comparing dozens of products that appear different on the surface but offer little meaningful distinction in practice. In this environment, more choice does not necessarily lead to better decisions.
AI shopping assistants appeal because they reduce that cognitive load. Rather than expanding options, they compress them, filtering large product sets into a smaller number of viable choices that meet practical constraints such as price, availability, suitability and delivery timing. For many consumers, the value lies less in discovery and more in elimination.
Market saturation also plays a role. Ecommerce has become increasingly crowded with stores selling highly similar or identical products sourced from large global supplier ecosystems such as Alibaba, Taobao and Temu. These platforms play a legitimate role in global commerce, but their scale means the same products can surface repeatedly across different storefronts, often presented with different branding, positioning and price points. For consumers, this repetition can make it harder to assess what is genuinely differentiated or worth paying a premium for.
In response, some shoppers turn to AI tools as a way to cut through duplication, validate comparisons and reduce the time spent navigating multiple versions of the same offer. Rather than relying on any single brand or listing, they use AI as a comparative layer to narrow options before deciding where to buy.
Over time, repeated usefulness builds familiarity. As AI tools begin to retain context such as preferences, spending limits, past purchases and brand affinities, they become less generic and more aligned with individual decision-making styles. This does not mean consumers outsource decisions entirely, but it does mean AI becomes embedded as a layer in the buying process. While younger demographics may adopt these tools more quickly, the broader shift is less about age and more about relief from complexity and decision fatigue.
As AI-assisted shopping becomes embedded in everyday behaviour, the next shift is not faster comparison, but delegation. By 2036, many consumers no longer use AI tools only to shortlist options. They increasingly allow them to coordinate decisions and transactions across multiple steps.
In this model, an AI agent does not simply recommend products. It interprets intent, evaluates trade-offs, and assembles outcomes. A customer planning a camping trip may specify duration, climate, number of people, budget and delivery deadline. The agent identifies compatible products, checks availability, balances price against quality signals, and presents a consolidated option that can be purchased in one flow.
This is the core of agent-assisted commerce. Decision-making shifts from browsing interfaces to outcome-oriented coordination. Discovery, comparison and checkout collapse into a single conversational or ambient experience. Payment, identity and fulfilment systems increasingly sit behind the interface rather than being navigated explicitly by the user.
For consumers, the appeal is not novelty, but reduced effort across complex or multi-item purchases. For businesses, the implications are structural. Visibility is no longer determined only by search ranking, ad placement or on-site persuasion. It is influenced by how well products can be interpreted, evaluated and selected by automated agents acting on behalf of customers.
This changes the optimisation problem. Being present online is no longer sufficient. Products need to be legible to systems that compare across suppliers, sensitive to constraints such as compatibility and delivery, and informed by signals of reliability over time. As agent-assisted commerce matures, the competitive advantage shifts towards businesses that perform consistently well when evaluated by machines as well as people.
Google Ads has long been a major channel for capturing high-intent demand, particularly at the point of decision. As consumers increasingly turn to AI for recommendations, a new advertising model is beginning to emerge alongside it. AI platforms are already testing advertising within conversational interfaces, signalling that commercial influence is likely to become part of how these systems are funded and scaled.
When a user asks an AI for the “best camping gear”, the system has to decide what to surface. Today, that decision may be based on product information, availability, reddit chatter, reviews and relevance. Over time, it is plausible that paid placements will also play a role, with businesses paying for increased visibility or preferential inclusion within AI-mediated recommendations. By 2036, this could resemble a familiar dynamic, where commercial incentives sit alongside relevance signals rather than replacing them entirely.
This has led some to suggest that AI could replace search engines and traditional channels like Google Ads altogether. That outcome is unlikely. A more plausible shift is that AI-mediated discovery becomes an additional layer rather than a replacement, potentially reducing the dominance of any single channel. Evening the playing field, so to speak. In that sense, AI may contribute to a more balanced digital advertising ecosystem, where visibility is not dictated solely by bidding power, but also by how well a product performs when evaluated across multiple signals.
Search advertising itself already faces trust challenges, with many users sceptical of heavily sponsored results and commercial influence at the top of the page. AI-driven recommendations may encounter similar scrutiny as they mature, particularly if users begin to question whether suggestions reflect genuine suitability or paid prioritisation. In this way, AI does not automatically solve the trust problem in advertising. It may simply relocate it.
Current experiments suggest that commercial influence inside AI systems may not rely solely on traditional advertising formats. Some platforms are testing transaction-based models, where revenue is tied to completed purchases rather than exposure or clicks. In these cases, AI acts not just as a discovery layer, but as an intermediary that facilitates the transaction itself. This approach shifts monetisation away from attention and towards outcomes, but it also introduces new questions around margin pressure, platform dependency and the cost of access to AI-mediated demand.
These experiments do not represent a settled model, nor do they imply that all AI-driven commerce will operate this way. They do, however, signal that the economics of influence inside AI systems are still being explored. As AI increasingly shapes how products are discovered and chosen, platforms will continue to test where value capture sits, whether through advertising, transaction fees, data partnerships or other mechanisms.
As a result, while paid placements inside AI systems may offer reach and exposure, they are unlikely to be sufficient on their own. Just as with search advertising, users will learn to interpret and discount commercial signals over time. What changes is not the existence of advertising, but the environment in which it operates.
This reinforces a broader pattern. Advertising continues to matter, but it works best when supported by strong fundamentals. Clear value, reliable delivery, accurate representation and a genuinely good customer experience remain critical. In a future where AI plays a larger role in discovery, these factors are likely to influence not only human decisions, but also how products are evaluated and surfaced by automated systems.
As AI-mediated discovery and advertising mature, some commentators suggest that businesses may no longer need traditional websites at all, arguing that products and services could simply connect directly into AI systems. This view assumes purchasing decisions are primarily technical and that intermediated interfaces can fully replace brand environments. That assumption overlooks how and why people buy.
In reality, ecommerce behaviour has always reflected different buyer motivations, and those differences are unlikely to disappear by 2036.
A useful parallel can be seen in food delivery marketplaces. Platforms such as Uber Eats have changed how people discover and purchase food by centralising search, reviews, popularity signals, pricing and payment into a single interface. For some buyers and some occasions, this model works well, particularly when speed, convenience and easy comparison matter most.
However, this marketplace model does not serve all buyers equally. Many people still choose restaurants based on brand, reputation, values, story or personal loyalty rather than rankings or proximity alone. For these buyers, a restaurant’s own website and owned channels remain important. As a result, many restaurant strategies deliberately focus on reducing reliance on marketplaces by encouraging direct ordering and repeat visits through owned channels.
Owned channels build loyalty and provide context that a marketplace listing cannot fully capture, including the full menu, seasonal changes, philosophy, tone, community presence and the broader experience behind the food. A similar tension is likely to emerge as AI-mediated commerce grows. If AI platforms begin to concentrate discovery or transactions, some businesses may actively invest in direct channels to retain control over margins, customer relationships and brand expression rather than ceding those entirely to intermediated systems.
This dynamic is instructive for ecommerce in 2036. AI-mediated discovery and agent-assisted purchasing are well suited to buyers who prioritise efficiency, comparison and speed. At the same time, branding continues to matter for buyers influenced by narrative, curation, originality and trust built over time.
Rather than disappearing, websites may become more specialised. Businesses offering distinctive products, strong brand identity or meaningful loyalty programs are likely to continue benefiting from direct engagement through owned channels. Conversely, sites that primarily sell duplicated or commoditised products may face greater pressure as automated comparison becomes more effective.
In this context, the website’s role shifts. It is less about being the only point of sale and more about being a credible, expressive and trustworthy representation of the brand. For businesses competing on originality, curation and customer loyalty, that role may remain commercially important well into 2036.
As AI becomes more involved in product discovery and comparison, a common assumption emerges that brand influence will fade in favour of objective optimisation. If an AI can surface a cheaper, higher-quality product with better reviews, why would brand still matter at all?
In practice, brand and story are unlikely to disappear. Many purchasing decisions are not purely rational. Some buyers are influenced by identity, aspiration, cultural signalling or emotional connection, and those motivations do not vanish simply because better alternatives exist. People are unlikely to stop buying luxury fashion, iconic sneakers or culturally relevant products solely because an AI highlights a more cost-effective option. Visibility, association and narrative continue to shape desire in ways that are not reducible to specifications or ratings.
What does change is the balance between story and substance. In an AI-mediated environment, brand claims are increasingly evaluated alongside accumulated evidence. Signals such as pricing consistency, fulfilment reliability, return behaviour, customer complaints and long-term satisfaction become harder to obscure as comparison becomes more automated. Brands that historically relied on image, promotion or perceived exclusivity without delivering consistently may find it more difficult to sustain visibility as these signals compound over time.
For brands with genuine cultural relevance, strong product quality or loyal customer bases, this shift can reinforce rather than undermine their position. For others, it introduces pressure. Brand still opens the door, but it is less able to carry the entire decision on its own. Story continues to attract attention, but outcomes increasingly determine whether that attention converts and persists.
By 2036, brand is neither obsolete nor all-powerful. It operates alongside measurable performance rather than above it. In a landscape where AI helps buyers compare, validate and repeat decisions, the brands that endure are likely to be those where narrative, product and experience reinforce one another over time.
Once AI-assisted shopping is no longer novel, ecommerce does not simply become quieter or purely transactional. Instead, it becomes more intentional. Consumers move fluidly between AI tools, marketplaces, brand sites and physical retail depending on mood, context, urgency and familiarity. Some journeys are streamlined and directed. Others remain exploratory, social or aspirational.
Browsing does not disappear. It changes shape. For routine or repeat purchases, AI-assisted filtering shortens the journey by removing unnecessary steps. For more expressive or interest-driven categories, people still enjoy looking, discovering and spending time with brands. The difference is that exploration becomes more curated and less cluttered, shaped by relevance rather than volume. This environment favours clarity over noise. Brands that sell largely interchangeable products with limited differentiation face increasing pressure as automated comparison becomes more effective. At the same time, brands that offer something distinctive, whether through design, story, quality or experience, retain strength because they give people a reason to browse, not just to buy. The market does not collapse into uniformity, but it becomes less forgiving of weak or superficial propositions.
Importantly, AI does not replace human judgement. It reshapes it. Consumers still want control over meaningful purchases and still derive enjoyment from discovery, identity and expression. AI increasingly supports comparison, validation and repetition, while brands provide context, reassurance and inspiration. The result is a layered commerce experience where efficiency and enjoyment coexist, and where businesses that understand this balance are better positioned to adapt as expectations evolve.
The expansion of commerce into AI-mediated interfaces can sound daunting, but preparing for it does not require a complete reinvention of your business or website. In practice, it reinforces the fundamentals that already underpin strong ecommerce performance. While discovery and purchasing paths may diversify, the underlying reasons people choose one business over another remain largely the same.
What does change is the importance of clarity, context and accumulated evidence. If AI systems increasingly support discovery, comparison or purchasing on behalf of customers, your digital presence needs to communicate value in a way that is easy to interpret, both for people and for systems acting on their behalf.
No matter how a customer encounters your product, whether through search, social, direct browsing or an AI-mediated interaction, the clarity and quality of your digital presence influences whether a purchase happens, where it happens and whether it repeats. Even when transactions are completed through intermediated interfaces, the information, pricing and experience provided by the business shape the outcome.
Pricing should be simple, transparent and easy to understand. Unexpected fees, unclear shipping costs or confusing bundles create friction and undermine confidence. Clarity reduces hesitation for customers and ambiguity for automated evaluation.
Product pages should clearly explain what the product is, who it is for and when it is most suitable. Beyond basic specifications, context matters. Use cases, sizing guidance, compatibility, care instructions and delivery expectations help reduce uncertainty. The more clearly a product’s purpose and boundaries are defined, the easier it is for AI systems to match it to the right buyer scenario.
Customer reviews provide tangible evidence of performance over time. Reviews attached to individual products are particularly valuable because they reflect real usage rather than general brand sentiment. They help future customers understand trade-offs and help systems distinguish between similar offers.
If your product is genuinely different, that difference should be easy to articulate. Whether it is design, quality, service, ethics, origin or expertise, your unique proposition should be visible without interpretation. AI-assisted comparison makes vague positioning less effective, but it can amplify clear differentiation.
Simple, readable structure
Short sentences, clear headings and straightforward language improve comprehension. Important information such as delivery timelines, returns, guarantees and support should be easy to find and consistently presented. This benefits human readers and reduces the risk of misinterpretation by automated tools.
Reliable checkout and fulfilment
Even if the final transaction happens elsewhere, predictable fulfilment still matters. Accurate delivery information, reliable dispatch and clear post-purchase communication reinforce trust and support repeat behaviour, regardless of where the order is placed.
Optimise for understanding, not just visibility
Preparing for AI-mediated commerce is less about chasing new tactics and more about making your business legible. AI does not replace judgement, but it relies on clear, structured and consistent information to support it. The easier your products, policies and performance are to interpret, the more likely they are to be surfaced accurately and confidently.
This is not just about a single transaction. Long-term success still depends on understanding acquisition costs, conversion behaviour and customer lifetime value. By building a digital presence that communicates value clearly, answers questions upfront and delivers consistently, you position your business to perform well across changing discovery and purchasing environments, not just within one channel.
The next ten years will bring significant changes to the world of ecommerce. AI is likely to open up new avenues for discovery and create more convenient shopping experiences for consumers. While the tools and interfaces involved may evolve, the underlying principles of a successful business are unlikely to change. What does change is what gets rewarded. This is not a race to adopt new technology for its own sake, but a shift in how value is assessed and reinforced. AI does not favour novelty alone. It favours clarity, consistency and follow-through over time. Businesses that communicate clearly, deliver reliably and back up their claims with real outcomes are easier for both people and systems to understand, evaluate and return to.
As commerce becomes more mediated, weak propositions are exposed faster. Vague positioning, inflated promises and superficial differentiation become harder to sustain when comparison is automated and history is visible. At the same time, businesses that do the basics well, and do them consistently, benefit from compounding credibility rather than momentary attention.
In this context, transparency, reliability and customer-centred execution are not abstract values. They are practical signals that shape how products are surfaced, selected and repeated. Brands that prioritise strong fundamentals are better positioned to perform across changing discovery and purchasing environments, whether transactions happen directly, through platforms or via AI-mediated flows.
By 2036, success in ecommerce is unlikely to hinge on mastering a single channel or interface. It will hinge on whether a business is legible, dependable and genuinely worth choosing, regardless of how the customer arrives. In that sense, AI does not replace good commerce. It reinforces it.
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About the Author
Maryanne Ciego is a marketing leader and FinTech/SaaS strategist with over 15 years of experience driving growth in fast-paced B2B environments. Known for blending data-driven strategy with creative storytelling, she builds impactful brand narratives that resonate and perform.