Treating AI Agents as a Distinct Customer Segment

The shift toward agentic commerce--where AI does not just research products but completes the purchase--is more than an upgrade to online shopping. It changes the relationship between consumers and merchants. Trust, once built between a person and a brand, must now be embedded into the technical design of the agent itself. For businesses, the consequence is that AI is not a new distribution channel. It is a new customer segment that needs its own marketing and persuasion strategy. Companies that treat AI readability as a core skill rather than an IT afterthought will capture market share before the power dynamics of agent-led shopping settle.

The Hidden Cost of "Just Another Channel"

The biggest risk for modern retailers is assuming AI agents are simply a new version of Google Search. Kartik Hosanagar suggests this view is a strategic trap. When companies treat AI as a channel, they focus on SEO to get clicks. But in an agentic world, the agent is not just reading a page; it is reasoning through data from many sources.

Hosanagar compares this to the rise of Netflix. Studios initially saw streaming as just another way to deliver content. They failed to see that the platform was a system that changed consumer behavior and eventually eroded the power of the studios.

> "I think there are similar analogies here which is to say that if you just think about it as another channel, you are missing two things. You are missing one. The fact that your customer behavior is changing in big ways. And as a result, you could lose market share because if all you are thinking is it is another channel and I am on that channel, your competitor that is thinking smartly about how to inform and persuade this new type of customer AI agents will be ahead of you and will steal market share from you."
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> -- Kartik Hosanagar

Why the Obvious Fix Makes Things Worse

Merchants often think getting an AI to read their content is the solution. This is necessary, but not enough. AI models synthesize information from third-party reviews, Reddit threads, and video content. A single piece of outdated information, such as an old Reddit post claiming a brand is closing, can remove a company from an AI recommendation engine.

The system forces brands to become AI-literate. This requires moving beyond standard marketing. Hosanagar notes that current language models favor information in tables over paragraphs. This is not a technical hack; it is how the system processes and prioritizes data. The advantage goes to those who treat AI agents as a distinct audience with their own logic for persuasion.

The 18-Month Payoff: Building for the Agent

Agentic commerce will arrive in phases, category by category. Low-stakes, frequent purchases like groceries will be the first domains where consumers let an agent handle the entire decision.

The real friction is liability. Consumers will not hand over the reins for high-stakes purchases like insurance or cars until the AI platforms act as guarantors. The winners will be businesses that provide an approval gate for the human while building a backend optimized for the agent.

> "If Muse wants to do that for me, or instinct another app or startup that wants to do this for me, if they want me to go full agent, they have to take on the liability risk and say that if our agent does something that deviates from this intent that you had, then we will reimburse you."
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> -- Kartik Hosanagar

Key Action Items

• Audit your AI-Readability (Immediate): Evaluate how your brand appears when queried through LLMs. If you are missing or misrepresented, investigate the third-party sources like Reddit, YouTube, and review sites that the model likely scrapes.
• Segment your marketing strategy (Next 3-6 months): Stop treating AI as a search channel. Create a team or role focused on AI persuasion to understand how models reason through your product category.
• Experiment with data structuring (Next quarter): Test presenting product comparisons in tables rather than prose. This is a low-cost way to improve how your data is ingested by LLMs.
• Define your Human vs. Agent experience (Next 6-12 months): Decide if your brand is a high-touch experience where you compete on the luxury of human shopping, or an agent-first experience where you optimize for automated procurement.
• Invest in interpretability (12-18 months): As the market matures, consider tools or systems that provide insight into how AI agents reason about your brand, effectively building a data science team for non-human customers.