Adapting your business to the new era of AI commerce: B2A (Business To Agents optimization) 

The ground is quietly shifting under e-commerce. For years, we lived in a familiar world: launch Meta campaigns, launch Google Ads, optimize the funnel, tweak the checkout, scale what works.

Now, more and more real buying journeys are starting in a chat window with an AI assistant.

People are already asking things like:

"I need an outdoor walkie-talkie for a family hiking trip. It should have a long range and be easy for 5 and 7-year-old kids to operate. The budget is under 200 euros for 4 units. Delivery within up to 3 days."

That is not a classic search query. That is a conversation with an agent that can both recommend and complete the purchase without ever sending the user to your site.

This is where B2A (Business-to-Agent) comes in.

As a growth marketer, I see that this channel can impact real growth for early adopters. It's a new performance channel that sits on top of your DPA catalog on Meta, your Performance Max campaign on Google, and any of your brand awareness activities. 

This guide is designed for the forward-thinking marketer, the agile entrepreneur, and the business owner who understands that adaptation is the key to survival. We will explore the rise of AI-powered search, the new protocols that govern it, and the practical steps you must take to ensure your e-commerce business not only survives but thrives in this new landscape.

Just to note, to be 100% transparent: we are just starting to develop an implementation map for it, but we haven't implemented it yet. This article is based on a deep understanding of documentation and some already published cases. It is a strategic briefing on the technologies and principles being implemented today that will define the winners and losers of tomorrow.

The Key Acronyms You Need to Know

To succeed in this new era, you must first understand the language the machines speak. While the acronyms may seem daunting, the concepts behind them are straightforward and represent your new marketing channels. This is no longer just an IT problem; it is the core of your future revenue strategy.

Acronym	Full Name	Simple Explanation
ACP	Agentic Commerce Protocol	An open-source "language" that allows AI assistants to talk to your e-commerce store's backend to get product info and process orders 3.
LLMO	Large Language Model Optimization	The practice of structuring your product data so that AI can easily understand and accurately represent it. Think of it as SEO for machines 4.
AIRO	AI Reliability Optimization	The process of ensuring your store's data and API are fast, accurate, and trustworthy, which builds "loyalty" with AI agents 1.
B2A	Business-to-Agent	The new customer journey, where your primary relationship is with the AI agent, who then brings you customers.

From B2C to B2A: what actually changes in the journey

Business-to-Agents optimization is the practice of making your products, data, and infrastructure ready for AI agents that search, compare, and buy on behalf of customers.

Instead of fighting for clicks, you compete to be the default recommendation inside assistants like ChatGPT that now support Instant Checkout and the Agentic Commerce Protocol, an open standard that lets agents talk directly to your backend and payment stack

Traditional e-commerce is built around a simple pattern:

1. Capture demand with SEO, ads, and email.
2. Drive traffic to your site.
3. Push visitors through a multi-step funnel.
4. Measure everything through web analytics and conversion tracking.

In B2A, the visible part of that journey moves away from your website and into the agent’s interface.

If you want to see the mechanics step by step, I break them down in more detail in the guide on how AI agents shop and make decisions.

The New Customer Journey: A B2A Sale in Action

Let's visualize how a sale happens without a single visit to your website.

Imagine a customer who wants to buy a walkie-talkie.

1. NEED (The Customer):
A person describes their need in natural language to an AI assistant. For example, “I need an outdoor walkie-talkie for a family hiking trip. It should have a long range and be easy for 5 and 7-year-old kids to operate. The budget is under 200 euros for 4 units. Delivery within up to 3 days.”

2. SEARCH (ACP):
The assistant queries connected merchants through standardized protocols and APIs, not just via web search.

3. CHOICE (AIRO & LLMO):
It compares structured attributes, price, availability, shipping, trust signals, and sometimes external content.

• Merchant A has a slow, unstable API and half-structured data. The product feed says “great range” and “perfect for kids,” but there are no machine readable fields for maximum range, age suitability, or units per pack. The API response arrives late, sometimes times out, and one of the offers shows “in stock” while the website says “out of stock.” From an AIRO perspective, this merchant looks risky, so the agent downgrades it.

• Your Store, on the other hand, has a clean, fast API with consistent data. Your product information is fully optimized for LLMO, so the assistant receives a precise, machine-readable response like:

{
  "product_id": "WT-4PACK-ULTRA",
  "title": "Family Outdoor Walkie-Talkie",
  "price": "49.00",
  "currency": "EUR",
  "attributes": {
    "range_km": 12,
    "water_resistant": true,
    "battery_life_hours": 18,
    "age_min": 5,
    "units_per_pack": 1,
    "easy_controls": true
  },
  "availability": "in_stock",
  "delivery_window_days": 3,
  "policies": {
    "returns_days": 30,
    "warranty_years": 2
  }
}

4. RECOMMENDATION (AVM):
It picks a small shortlist and presents it as a direct recommendation. The agent filters by attributes, price, availability, policies, and reliability.

5. SALE (Instant Checkout):
One or two products get surfaced with a simple “Would you like to buy this now.” The user confirms and pays inside the chat. Instant checkout.

That is it.

No banner sliders. No exit popups. No cart recovery emails.

Your beautiful homepage might never be loaded. There is no classic session for Google Analytics to log.

From a user point of view, it feels like “zero click commerce”, which I unpack more in my article about the future of zero click commerce.

To design for this, you need to look at your funnel through a different lens.

Old funnel metric	B2A equivalent	Practical implication
Click through rate	Share of Answer	How often the agent mentions you
Add to cart rate	Recommendation selection rate	How often users pick your product from options
Checkout completion	Instant Checkout success rate	How many agent initiated orders succeed

If you continue to optimize only for web sessions, you will misread your performance.

You might see “traffic down” while revenue from agent-originated checkouts grows quietly in the background. I cover the content side of this shift in more depth in my playbook on content and SEO for agents, where I treat agents as readers with their own content preferences.

To win in this new environment, you need three things.

First, structured and consistent product data that agents can understand and quote. Second, fast, reliable APIs and feeds that keep price and availability accurate. Third, a measurement and experimentation loop focused on new metrics like Share of Answer, not just traffic.

In other words, treat AI agents as your most important B2B buyer who never sleeps.

The core layers of B2A readiness: LLMO, ARC, and Product Feeds

The biggest mindset shift in Business To Agents optimization is this: creative copy is optional, structured truth is mandatory. Large Language Model Optimization (LLMO) is the discipline of making your catalog legible to machines.

In OpenAI’s stack, that work flows through two main channels, on site structured data and the product feed that powers search and Instant Checkout.

LLMs do not “read” your poetic product page the way a human does. They parse fields. They match attributes. They check consistency. If your description says “super light,” but you never specify weight in a machine-readable field, you have missed the point.

You can break LLMO for e-commerce into four building blocks.

Pillar	Question	Example action
Crawlable	Can agents see it	Use feeds, APIs, and sitemaps, not only page HTML
Understandable	Can they parse it	Use Schema.org Product and clean taxonomies
Trustworthy	Can they verify	Align data across site, feed, and backend
Composable	Can they reuse it	Provide concise fact blocks and comparisons

A practical way to start is to design Answer Ready Content for your top products. Create a 120 to 180-word factual summary, a short “facts box” with fields like dimensions, materials, compatibility, and a simple decision rule such as “Choose this model if you travel weekly with a 16-inch laptop.”

Then align that with your product feed. OpenAI’s Product Feed Spec supports CSV, TSV, XML, or JSON, with updates as often as every 15 minutes. For a deeper breakdown of attribute design, I recommend my guide on product data and structured information, which walks through real product examples.

This work feels boring. But it is also exactly the sort of discipline that separates brands agents love from brands they ignore.

AI Reliability, Infrastructure, And The Trust KPI

Once your data is structured, the next question is simple. Can the agent rely on you? OpenAI and Stripe describe the Agentic Commerce Protocol as an open standard that defines how agents, buyers, and businesses coordinate product discovery, checkout, and payment in a safe and consistent way.

From a growth perspective, ACP turns your infrastructure into a front-line marketing asset. If your product feed lags behind actual stock, your price changes but the feed does not, or your API times out, the agent assumes the risk with the shopper. It will quietly learn to rank you lower.

I encourage teams to treat “AI Reliability Optimization” as a shared metric across marketing and engineering. At a minimum, track these signals.

Reliability dimension	Example signals
Accuracy	Price and availability mismatches per 1000 agent queries
Speed	Median response time for ACP product and checkout endpoints
Consistency	Percentage of SKUs fully aligned across site, feed, backend
Transparency	Presence of policies and certifications in structured fields

The payments ecosystem is evolving fast as well. Stripe is the reference implementation. PayPal and other payment providers are integrating digital wallets directly into ChatGPT, expanding merchants' choices and reducing friction for buyers.

From your side, this means working with your technical team to build an ACP-compatible stack that is observable and resilient. I go into more implementation detail in my article on infrastructure and technical stack for agentic commerce, where I describe how to instrument APIs so marketing can actually see agent traffic instead of guessing.

A practical roadmap to implement B2A in your store

I have seen teams stall because “this sounds huge.” It does not need to be.

The smartest approach to Business To Agents optimization is a narrow pilot that proves three things. One, you can ship a clean feed for a small category. Two, your infrastructure can handle ACP-style flows. Three, the organization can collaborate across marketing and engineering without drama.

Here is a simple phased roadmap.

Phase 1: Choose and clean a pilot category

• Pick 1 to 10 SKUs with clear attributes and healthy margin.
• Export their data and run a manual audit.
• Rewrite titles to be factual.
• Extract attributes from fluffy copy into structured columns.

Phase 2: Ship your first agent-ready feed

• Map your columns to OpenAI’s product feed fields, including enable_search and enable_checkout flags, and basic seller policy URLs where required. Read a really good article about Product Feed Specification.
• Generate a CSV or JSON feed and validate syntax.
• Align that feed with Schema.org Product markup on the same SKUs.

Phase 3: Implement a minimal ACP integration

• Implement a basic checkout state machine and instrument it with monitoring.
• Use the open source ACP reference implementation as a template.
• Expose a lightweight product lookup endpoint that reads from your single source of truth.

If you want a more detailed checklist, I expand this plan with timelines and roles in my piece on optimization for B2A. The key is to get your hands dirty early, then scale to the rest of your catalog once you have a working pattern.

Measuring B2A when you don't see the whole funnel (New KPIs)

This is one of the most uncomfortable parts for performance marketers.

We know marketing is a behavior-based science, and we will continue to use proven strategies to generate awareness and product demand.

But it is becoming more complicated, because we will not receive data from the final stages from B2A, and we need to understand how to work this channel if we want to grow in e-commerce.

In classic channels, you can see every step of the funnel.

In B2A, much of the interaction happens inside the agent, which means:

• You may see fewer sessions and events in your web analytics.
• You will see orders coming from new referrers and sources.
• You will have to infer some behavior instead of tracking it directly.

OpenAI and ecosystem partners are already highlighting product feed queries, Instant Checkout flows, and ACP event logs as core observability tools. In parallel, vendors are starting to offer “AI optimization” tracking that estimates how often agents reference or recommend your brand.

When designing a B2A dashboard, I recommend grouping metrics into three buckets.

Bucket	Example KPI	Where it lives
Visibility	Share of Answer for priority intents	AIO tools plus manual prompt testing
Reliability	Error rate and latency on ACP endpoints	API monitoring systems
Revenue impact	Assistant attributed conversions and AOV	Analytics with AI specific referrer logic

On the analytics side, you can already do a lot with existing tools.

• Tag agent originated traffic with custom UTM parameters and referrers that include chatgpt.com or similar hostnames.
• Create views that segment by those referrers to track conversion and retention.
• Pull ACP logs into a warehouse so you can calculate funnel metrics such as “agent queries to completed orders” directly from your own data.

Beyond the numbers, keep an eye on market signals. OpenAI continues to expand Instant Checkout partners, with Etsy, Shopify, and large retailers like Walmart already in motion.

I track these shifts, and related adoption curves, in my ongoing notes on market trends in agentic commerce.

The businesses that treat these metrics as seriously as click-through rate once were will adapt faster than those waiting for a “finished” standard.

How does this fit into your broader marketing strategy

I want to repeat this again: I don't believe that the B2A channel can replace traditional marketing. It sits next to it.

We will still:

• Run paid and organic campaigns to create demand.
• Build brands that people recognize and trust.
• Invest in creative, storytelling, and product education.

The difference is that the “moment of choice” for many users will happen with an AI agent in the middle, not on a search results page or a product listing page.

So the question becomes:

• Are you present and understandable when that agent is asked for a recommendation
• Are you trustworthy from the agent’s point of view, not just from a human’s point of view
• Are you one of the first movers in your niche, or will you join once your competitors have already trained the agents with their data

My personal closing note

On one side, we are moving from an internet of pages to an internet of agents. From the other side: B2A never replaces traditional digital marketing.

Business-to-Agent optimization is how you build that partnership on your terms. You treat product data as infrastructure, not decoration. You build an ACP compatible stack that your growth team can understand and monitor. You learn new metrics that reflect conversations instead of clicks.

If you want to explore the content side of this shift, start with my framework for content and SEO for agents. If you are ready to sketch the technical blueprint, pair this article with my notes on Agentic Commerce Protocols, where I unpack the protocol from a strategist’s perspective.

This transition will not happen in a single quarter. But the brands that start treating agents as first class customers today will be the ones that still grow when traditional tactics quietly lose their edge.

Q&A: Quick Clarifications On Business To Agents Optimization

Q: What exactly is Business To Agents optimization in e-commerce

A: It is the process of making your catalog, infrastructure, and analytics ready for AI agents that research and buy on a customer’s behalf. In practice, that means clean product feeds, ACP compatible APIs, and reliability metrics that help agents trust your store for Instant Checkout experiences.

Q: Do I need to replace traditional SEO with B2A

A: No. You still need a strong website for users who browse, compare, and research in a browser. Business To Agents optimization runs in parallel and often reuses the same structured content, but it shifts your focus from ranking pages to making your products the easiest for agents to understand and recommend.

Q: Is this only relevant if I am on Shopify or Etsy

A: Those platforms have a head start because they are early integration partners for ChatGPT commerce. However, ACP is an open standard and any merchant can implement it, whether they use a SaaS platform or a custom stack. The strategic work around product data and reliability applies in every case.