The Rise of Agentic Commerce

Last week, my daughter lost her USB-C power brick—again. Between my son and daughter, this happens at least twice a year per kid, and it's frustrating every single time. So, I took to the internet to see if there were any products that could help, but I had a hard time coming up with the right incantation to type into my search engine of choice. I tried a few different searches:

• "Trackable power adapter"
• "Prevent losing phone charger"
• "Power adapter with integrated tracker"
• "AirTag power adapter"

No matter what I searched for, I couldn't find a product that fit the bill. Searching Amazon, Wal-Mart, and other e-commerce sites didn't surface anything, either. Having spent nearly an hour with no results, I gave up.

I've had similar experiences before, and I am betting that you have too.

(This is now the part of the infomercial when you hear "There's got to be a better way!")

The Better Way

Earlier this week, OpenAI announced Buy it in ChatGPT, an agentic commerce feature within its popular AI assistant. With this release, they've enabled end-users to discover, investigate, and buy products from popular e-Commerce platforms like Etsy, Shopify, and more.

Revisiting my lost power adapter experience, I fired up ChatGPT and typed in the following:

“My kids keep losing their USB-C power adapters. Is there a power adapter out there
that has something like an AirTag integrated to make it easier to find?”

After just moments, ChatGPT had found PlugBug with Find My from Twelve South. It even mentioned that the price of the product was "premium" and provided me with four "workarounds and hacks."

While Twelve South isn't yet in the ChatGPT agentic commerce program, this interaction validates that OpenAI is onto something.

e-Commerce? a-Commerce!

OpenAI's announcement signals a future in which e-Commerce dramatically changes, becoming something that is better named "a-Commerce," or "Agentic Commerce."

For decades, businesses like Amazon, Wal-Mart, and Etsy have aggressively used data to personalize recommendations, boosting their revenue by driving purchases. Recommendation engines range from basic to incredibly powerful, and often represent trade-secret level technology investments.

Product discovery happens in e-Commerce platforms already, and AI is already used to summarize reviews, and accelerate a customer's buying decision. But, as good as recommendations can be, they don't solve all product discovery problems, and they are generally restricted to a single e-Commerce site.

OpenAI generously open-sourced the tech behind this new feature, including a new open standard, the Agentic Commerce Protocol, or ACP for short. Like MCP before it, ACP allows third-parties to extend supporting AI assistants, but in this case, the extensions are focused squarely on Agentic Commerce.

If ACP takes hold, before long, every e-Commerce platform on the internet will become a-Commerce enabled. For many years, search engines have been front and center as the gateways to the web, but as AI assistants continue to expand their reach, that is likely to fundamentally change.

From SEO to AEO

This shift has profound implications for how businesses think about online visibility. For two decades, companies have invested heavily in Search Engine Optimization (SEO)—crafting content, building links, and optimizing metadata to rank higher in Google's results. But as consumers spend more time conversing with AI assistants than browsing search results pages, that playbook is becoming obsolete.

Welcome to the era of Agent Engine Optimization, or AEO. Instead of optimizing for PageRank algorithms, businesses will need to ensure their products and services are discoverable by AI agents. This means structured data that agents can parse, clear product specifications, authentic reviews, and integration with protocols like ACP. The companies that master AEO early will have a significant advantage as this shift accelerates.

The New Gatekeepers

It's no wonder that Google is investing heavily in AI as a layer on top of their traditional search engine. They understand what's at stake: the very nature of how people discover and purchase products is fundamentally changing. The search box is giving way to the conversation, and the ten blue links are being replaced by personalized recommendations from AI agents that understand context, intent, and nuance in ways that keyword matching never could.

For consumers, this shift promises a future where finding the right product—even something as specific as a USB-C charger with integrated tracking—takes seconds instead of hours. For businesses, it's a wake-up call: the rules of the game are changing, and those who adapt to AEO will thrive while those who cling to SEO strategies alone risk becoming invisible to an entire generation of AI-assisted shoppers.

The era of agentic commerce is here. The question isn't whether it will transform how we shop online, but how quickly.

I. On the surface, this may not seem like an important clarification, but it provides a lot of context for the findings from the rest of the report. Remember, only 36% of companies have been able to reach production.

Let's look at the other conclusions from the MIT study:

• 80% of organizations have piloted LLMs, but less than half of those are in production
• 67% of partner-enabled AI initiatives successfully demonstrated ROI, as compared to only 33% of those developed in-house
• Well over half of AI spend has been focused on GTM, but back-office automation has demonstrated significantly greater ROI

Ultimately, the study demonstrated that AI itself isn't the issue. As is often the case, the issue is execution. LLMs are incredibly capable, as any user of ChatGPT, Claude, or Gemini can plainly see. Organizations made poor decisions about where to invest due to a lack of understanding, as internal teams have deep knowledge of their business, but have little to no experience with AI. They were also resistant to working with expert partners, in spite of data that shows that partners can double their success rate. Finally, organizations failed to understand the scope of internal disruption. Major technology shifts require cultural change – a new way of thinking and doing>. All of these stand in the way of success, and all of them point back to our ancient axiom.