Why the Affiliate Model Is the Best Way to Monetize Agentic Commerce and AI‑Powered Shopping

As AI becomes embedded in shopping, whether through agentic commerce or AI-assisted product searching, one question remains: how will AI agents be monetized? The answer may not be ads or paywalls, but rather a diversified revenue model powered largely by affiliate commissions.

AI-powered shopping is going mainstream

We’ve seen AI shopping go from edge-case to mainstream in a matter of just a few years. Research from Capgemini illustrates this seismic shift: 58 % of consumers have replaced traditional search with GenAI tools for product recommendations – a huge jump from just 25% in 2023.

Meanwhile, nearly 70% of consumers want GenAI integrated into their shopping experience. Salesforce research revealed that 39% of consumers (and over half of Gen Z) use AI for product discovery.

So, what’s next? We’re at the very beginning of the agentic commerce era, with AI agents stepping into decision-making and buying roles, such as product research, price comparison, deal-seeking, and even making the final purchase on behalf of a human user. What the industry needs now are viable business models for sustaining the next era of commerce.

Built for transparent monetization

In the affiliate marketing model, referring sites (publishers) are rewarded with a percentage (commission) of completed sales. Among many options, including subscriptions, ads, and sponsored results, it stands apart when AI is heavily influencing purchasing decisions and agents are executing actual transactions. In the affiliate marketing model, referring sites (publishers) are rewarded with a percentage (commission) of completed sales.

While discussing the use of the affiliate model for AI-referred commerce, OpenAI CEO Sam Altman recently said, “If you buy something through Deep Research that you found, we’re going to charge like a 2% affiliate fee or something.”  Altman’s mention serves as a soft testimonial: monetization via affiliate commission on actual sales, not ads, is a viable option, and could be a standard for AI shopping agents.

His comment also hints at affiliate fees becoming a natural complement to subscription revenue for AI platforms like OpenAI, Anthropic, and others – with affiliate programs rapidly becoming a recommended best practice in monetization.

Aligned incentives

Affiliate monetization aligns incentives because payment to the publisher (in this case the AI tool or agent) is based on completed sales, unlike advertising models such as paid search, display ads, or sponsored content, where the advertiser pays for impressions or clicks. With payment (commissions) tied directly to actual sales, advertisers pay for performance and measurable value delivered.

But thinking about the user experience, the affiliate model alone doesn’t automatically guarantee unbiased product recommendations. As affiliate monetization is introduced to AI systems, it’s going to be imperative to maintain user trust and alignment. One way to do that is by sharing the value an AI platform captures for its referred purchases; for example, offering more value to subscribers by enabling those paid users to receive a portion of earned affiliate commissions as cashback to offset monthly subscription fees.

This transparency helps ensure the AI agent continues acting in the user’s best interest, and doesn’t prioritize its own revenue opportunity over end-user value.

Research shows this matters: 62% of consumers are more inclined to buy from sources that transparently disclose affiliate compensation models. Trust is earned when users both understand the incentive model and can directly benefit from it.

The affiliate monetization model truly benefits all participants:

• AI platform owners unlock growth-based revenue based on referred sales – the more customers using their platform to conduct agentic shopping, the more commission revenue they earn
• Consumers enjoy the convenience of AI-assisted or agentic shopping without intrusive ads or sponsored listings, and may even share in the affiliate commission revenue
• Brands and merchants enjoy a performance-based return on their marketing budget, in which they set a commission rate and pay only on completed sales.

Built-in tracking and attribution infrastructure

Existing affiliate tracking is set up for the tracking and attribution of third party-referred sales for a given merchant when a user clicks an affiliate link and places an order. But, with AI shopping agents doing the purchasing, there’s no human transacting on the merchant’s site, and that also means there’s no traditional “click” upon which to start the typical affiliate tracking process through to completed purchase and attribution back to the referring site.

But with an agent assigned to be in charge of a shopping journey, even without a human clicking around, there are still links for the agent to follow based on products’ “place” on the internet. That agent’s selection of a product might come from a product data feed, but it still lives somewhere for the agent to choose it and follow the path to completing a purchase.

Affiliate marketing is already built for this scenario, too. While much of today’s affiliate tracking still relies heavily on browser cookies and JavaScript to track clicks and attribute referrer sales, large affiliate networks including CJ, Rakuten, and Awin do support machine-to-machine tracking via APIs and server-side calls. They can identify referring sources, pass affiliate IDs, and verify the completed sale, all without humans actually clicking links.

This secure machine-to-machine method ensures affiliate networks credit agents reliably and transparently.

Other attributes that lend affiliate well to the coming age of agentic commerce

In addition to its pricing model and the tracking infrastructure, affiliate also offers other features that make it uniquely suited for AI-driven commerce:

Product feeds and APIs are machine ready. CJ, Awin, and Rakuten offer the ability for retailers to provide product data feeds in formats including CSV, XML, JSON. These feeds are packed with rich metadata such as pricing, inventory, descriptions, product categories, even coupon codes – and are the structured input LLMs and shopping agents need to evaluate product options and make informed recommendations or purchases for users.

Notably, as agentic commerce matures, companies are developing Model Context Protocol (MCP) servers to let AI agents programmatically access product catalogs, attributes, and pricing. Rather than creating brand new data sources, developers can tap into existing feeds to provide agents with product data.

Globally scalable. Numerous retailers work across multiple countries, so most affiliate networks handle cross-border payments, currency conversion, and omnichannel tracking for desktop, mobile, and app environments. They also support massive scale: tens of thousands of merchants and millions of SKUs—without requiring custom integration.

Regulation-ready architecture. Most major affiliate platforms prioritize user data privacy: CJ Affiliate has implemented GDPR readiness with server-side consent tools and clear user data management practices, while Rakuten mandates publishers maintain CCPA compliance.

The Path for Monetizing AI Agents

As agentic commerce matures, merchants, networks, and agent builders must build for machine trust, seamless tracking, and transparent affiliate relationships. The affiliate marketing ecosystem already provides the monetization model, tracking infrastructure, and the essential architecture (structured product data, scale, and regulatory compliance) to make it ideally suited for the next generation of agentic commerce.  If more AI platforms adopt the affiliate marketing model, Sam Altman’s 2% affiliate commission mention may turn out to be more than just a snappy soundbite. It could be a prescient roadmap for the future of AI agent monetization.