AI shopping agents threaten the traditional eCommerce revenue model
AI agents are reshaping how online shopping happens, fast, efficiently, and with little to no human input. But that’s not good news for platforms like Amazon and others built on sustained engagement with human customers. These platforms make money through a complex ecosystem, ads, upselling, personalized recommendations, loyalty programs. The AI agent shows up, buys one item, and disappears. No browsing. No data trail. No second purchase. That ecosystem doesn’t work without a customer journey.
Take Amazon. In the past year, they pulled in over $600 billion from advertising. That’s not a side hustle, it’s core business. Brands pay them to show up in search results, sponsored listings, and targeted suggestions. The entire experience is built to maximize spend per visit and stimulate repeat purchases via things like past order reminders, “customers who bought this also bought” suggestions, and tailored promotions. AI agents eliminate all of that. When bots handle the transaction, there’s no upsell. No brand visibility. No user retention loop.
This matters for more than one company. Nearly every eRetailer, from large marketplaces to niche online stores, depends on that same logic. Personalized marketing, product discovery, loyalty incentives. These aren’t optional features; they’re pillars of the revenue stack. Remove the customer, and the stack starts to collapse. We’re talking about cutting out repeat value and brand-building opportunities.
The internet economy was designed for human consumers browsing, choosing, and engaging. AI agents strip out that entire interaction layer. If these bots become mainstream, what’s left is a transactional interface, fast, but shallow. For businesses that rely on emotional connection, branded discovery, or customer behavior data, that’s a problem.
Executives need to start asking: how do we build for a future where the buyer might not be human? What’s the monetization path when there’s no one to see an ad and no one to join a loyalty program? When you recognize what’s coming, continuing to invest in current engagement models without adaptation is a bad bet. This is not a temporary shift. It’s structural.
The conflict between amazon and perplexity highlights a broader tension between AI innovation and platform control
Amazon recently issued a legal demand to AI company Perplexity. The request was simple in principle: if an autonomous agent is going to interact with Amazon’s ecosystem, it should announce itself for what it is, an AI, not a human. It’s about control, revenue, and data integrity. Amazon doesn’t want bots quietly pulling value from its system without contributing back to its business.
The reaction from Perplexity was aggressive. They responded publicly, accused Amazon of being anti-innovation, and positioned themselves as defending what users want, automation, simplicity, and freedom from manipulation. According to Perplexity, Amazon’s real motivation isn’t transparency; it’s protecting its advertising engine and pushing users to buy more than they need.
This dispute is more than a branding clash. It shows what happens when AI agents start acting on behalf of users inside ecosystems built to monetize human attention. Amazon’s ecosystem is optimized around visibility, influence, and behavioral feedback. When bots short-circuit that system, Amazon loses the ability to deliver targeted content, measure intent, or monetize the journey. From Amazon’s standpoint, unauthorized AI agents are extracting value without offering value back.
For business leaders, this raises a clear challenge: how do you balance open innovation with economic protection? If you own a large digital platform or retail asset, odds are you’re monetizing user interaction one way or another. AI agents bypass that. Boards and executive teams need to understand that if developers are deploying bots inside your ecosystem, without oversight, you may already be losing key revenue signals without even realizing it.
There’s also a real strategic decision here for AI developers. They can either collaborate with platforms and negotiate standards, or they can continue moving fast and breaking things, until they hit a closed gate or a lawsuit. The companies that work within mutually beneficial frameworks will scale better, earn trust, and avoid regulatory blowback. The ones that don’t are exposed.
This isn’t the last time you’ll hear this argument. It’s the beginning. Expect more demand letters. Expect regulatory questions. The underlying business models sitting under many digital experiences weren’t designed for real-time autonomous agents. Now that those agents are here, business leaders on both sides of the stack will need to rethink the rules of engagement.
eRetailers face potential erosion of customer engagement due to anonymous AI agent transactions
Most online retailers don’t rely on just the initial sale. They build systems that turn one purchase into many. This includes product recommendations based on past behavior, time-sensitive promotions, and memberships that reward recurring business. The strategy is designed around human behavior, discovery, comparison, hesitation, and impulse. None of that applies to AI agents making surgical, task-based purchases.
An AI agent doesn’t browse. It doesn’t respond to discounts or get persuaded by product placement. It isn’t influenced by brand messaging or long-term perks. It comes in with a targeted request, makes a one-time selection, and leaves. That eliminates a wide funnel that many retailers depend on to drive revenue beyond the checkout cart.
Executives should step back from the novelty of AI-driven automation and look closely at what it removes. Most eCommerce platforms are structured to capture and retain user attention. Features like “You might also like,” cart reminders, loyalty points, and email follow-ups all disappear when the customer isn’t a human. And when engagement disappears, the conversion opportunities that come with it vanish too.
For eRetailers, this means customer lifetime value could drop sharply, not because customers are leaving, but because bots are replacing them as transaction agents. That becomes a serious concern for any business optimized around relationship-driven digital commerce.
Many smaller retailers have high hopes that AI agents will become new traffic channels, similar to how search engines currently channel users into storefronts. But the context is different. Google sends people, AI agents make decisions without human sentiment or curiosity. Any expectation that these bots will behave like human customers is misaligned with how the tech works.
To stay competitive, retailers will need to develop hybrid strategies that still account for AI-mediated shopping while protecting the mechanisms that have traditionally created margin growth. This could include authorized agent partnerships, transaction-based access fees, or AI-visible product metadata designed for machine relevance, just to name a few pathways worth exploring. What’s clear is the traditional user-centric commerce model isn’t future-proof by default. The shift is already happening. What matters now is how retailers adapt before the data trails, and the engagement they power, start disappearing.
The future impact of AI agents depends on both developer practices and evolving user trust
Widespread adoption of AI shopping agents isn’t inevitable, it depends on two things: how developers deploy them, and whether users trust them. Right now, both are in early stages. On the developer side, companies like Perplexity are testing boundaries. Their track record includes incidents like bypassing publisher paywalls, which raised questions about compliance and respect for digital property. These aren’t small issues. They influence how platforms and regulators respond.
If AI companies continue pushing unauthorized access or ignoring platform terms, they invite barriers, whether technical, legal, or commercial. Ecosystems like Amazon will either block these agents outright or demand structured agreements. Developers who want to scale will eventually need to offer value back, be it through revenue sharing, traffic attribution, or formal APIs designed for autonomous interactions. Without that, bot-driven transactions won’t be sustainable at scale.
On the user side, trust is the bottleneck. Perplexity claims that this AI-driven approach is what users want. But that remains speculative. Many users still prefer direct control over their transactions, searching, comparing, evaluating. Others hesitate to hand over any financial authority to a system they don’t fully understand, let alone trust with debit card credentials, order history, or address data.
For executive leaders, this introduces a critical variable. If AI agents become the interface between user intent and your business, you no longer operate in a customer-facing model. That changes how loyalty is built, how brand value is conveyed, and how product differentiation is communicated. Your customer becomes the AI’s algorithm, and optimizing for that requires completely different design logic.
To respond effectively, businesses should start investing in interoperability with trusted AI agents, while shaping new standards that ensure cooperative behavior inside commercial platforms. Think in terms of control, compliance, and compatibility. Developers who align their architecture with platform expectations and end-user control will be better positioned to grow. Those clinging to unregulated access approaches will be shut out.
This is a transition point. Whether AI agents become dominant in e-commerce depends on decision-making today, both by the companies building the tech, and those shaping its commercial environment. Early alignment will define who captures value and who gets disrupted. The shift may not happen overnight, but it will happen fast once trust and access are solved.
Key highlights
- AI agents undermine core e-commerce revenue channels: Autonomous shopping bots bypass key monetization paths like advertising, upselling, and loyalty programs. Leaders should reassess strategies that rely on human-driven engagement and invest in models that accommodate non-human buyers.
- Platform control is clashing with AI-driven automation: The Amazon-Perplexity standoff highlights the need for clear standards on how AI interacts with commercial ecosystems. Executives should define platform boundaries early and establish policies for AI agent access that protect data and revenue.
- Customer engagement strategies face structural disruption: AI agents don’t respond to traditional marketing triggers or brand incentives, weakening long-term customer value. Retailers should explore alternate engagement loops or AI-facing frameworks to maintain transaction volume and margin.
- Future impact hinges on developer compliance and consumer trust: Trust in AI agents remains limited, and developer overreach risks being cut off from major platforms. Business leaders should prioritize partnerships with responsible AI vendors and invest in frameworks that give end users secure, transparent control.
