Own the Intelligence, Win the Customer: A Retailer’s Guide to Agentic Commerce

According to recent data from Accenture, 74% of global consumers indicate they would trust a personal AI agent over their best friend to make a purchase on their behalf. I don’t know what that says about the quality of modern friendships, but it certainly indicates that agentic commerce is primed to go mainstream. 

In fact, McKinsey estimates AI agents will conservatively mediate $3-5 trillion in consumer commerce globally by 2030. We’re seeing the distance between intent and purchase collapsing in real time, but most retailers and ecommerce companies aren’t architected for this change. 

For years, the “seamless customer experience” has served as an industry northstar, but agentic commerce changes what that actually entails. What used to begin in the storefront, is now starting in conversations on platforms you don’t own, shaped by signals you have likely been outsourcing to third parties in exchange for incremental lifts. 

Winning the agentic commerce race requires thinking like an AI-native operation, which means owning the intelligence beneath the experience. In other words, you’ve got to own the data that teaches an agent what your customer actually wants — before they’ve even arrived. 

This paradigm shift demands digging deeper into the layers that make agentic AI operations possible and taking action immediately in order to gain the upper hand in acquiring the necessary training data. 

The data you’re giving away is the key to the agentic commerce castle

One of the greatest challenges that keeps retailers from being ready for agentic commerce is they’re approaching AI through the same lens as prior point solutions: a search vendor, a chatbot, a recommendation engine, a generative content tool, a visual search plugin. 

Each solves a narrow problem. And while the conversion bump may show up right away, all the learning is compounding inside somebody else’s product. Meanwhile, the retailer is left with a fragmented stack and even more fragmented data. 

A truly differentiated agentic experience runs on something those vendors can’t (or won’t) hand you: a longitudinal, owned picture of your customer. What they hesitated on. What they came back to. What they asked about across sessions, abandoned in a cart, and eventually bought six weeks later. 

When organized in a way agents can use, this kind of data makes what we call an intelligence layer possible. This is where intent gets interpreted, and decisions get made. These decisions include lower risk calls like ranking, recommendations, personalization, and product understanding; all the way up to bigger ones around logistics, returns, and resolving payment issues.

For most retailers, the edge nobody can copy lives in how they understand customers, products, occasions, brand voice, and service moments. To bring that into the agentic commerce future requires leveraging the data estate you already have, and strategically building on it. 

Don’t let messy data get in the way of paving the agentic road

Most retailers treat messy data as a roadblock to getting started, but a multi-year cleanup to “prepare” for AI is an expensive misunderstanding that will cost even more in lost customers, climbing acquisition spend, and the high-intent signals that go uncaptured. 

That’s the compounding dimension of agentic commerce that didn’t exist before: a month of delay is a month of training data your competitors are generating that you are not.

So rather than tearing out your PIM, OMS, CDP, or other commerce platforms, you can build an agentic context layer that sits on top of your current data lake, runs alongside what’s already there, and dramatically speeds up AI innovation by arranging your data so that agents can actually use it.

A strong context layer covers four areas: product understanding (attributes, fit, compatibility, tradeoffs), customer context (behavior, loyalty, order history, stated intent), session context (what the shopper compared, asked, hesitated over, and left in the cart), and business context (inventory, margin, promotions, and merchandising rules). By building this layer now, the benefits of capturing further data will accumulate. 

Every search, every question, every uploaded image, every moment of hesitation gets converted into structured intent, and that intent sharpens ranking, recommendations, content, and service. Don’t get me wrong – this doesn’t translate to a Get Out of Data Clean-Up Free card. You have to keep pushing data quality up in parallel while the layer stacks intent on top. But the retailers who act first will own the training data everyone else is going to wish they’d captured.

The economics favor owning the experience – so build something that can’t be disrupted

Retailers have traditionally competed on the layer of customer experience, with the best experiences defined as those moments that feel effortless for customers, even a little magical. Of course, the reality behind the magic is retailers using what they know about their customers and the context around them to remove any friction. 

SaaS has had a major role to play in removing this friction, with retailers leveraging third-party platforms because building their own was too expensive. It also meant choosing from the same platforms – making truly differentiated experiences hard to achieve. 

But agentic software engineering is breaking that math. Roadmaps that once took years are now delivered in months, so it’s time to reopen the build vs. buy conversation and consider how custom systems could lead to truly differentiated experiences. 

More importantly, however, strategically building internally can also serve as an insurance policy when there is no predicting what tomorrow looks like. With frontier models (and their prices) constantly changing, adaptability is the best way to ensure your success isn’t tied to any one vendor or provider. This is made possible by building an orchestration layer that works to choose the right model, tool, or workflow for the job. With clean interfaces and strong data contracts, any model can plug in, draw on your portable context, and be proven against your own data and quality bar through a standing evaluation harness before you switch.  

This doesn’t mean build everything. Point solutions will still win stretches of the customer journey. But retailers should also be investing in a wider AI commerce operating model with shared infrastructure, reusable intelligence, interoperable systems, and continuous learning spread across the organization.

Do that, and you’re laying down the foundation agents can use to deliver the kind of personalization that used to be available only to the most premium brands – affordably and at scale. The future of commerce isn’t a better storefront or product page, it’s a learning system, an AI flywheel that understands intent across the whole journey, puts that intelligence everywhere, and gets smarter with every interaction. If you build for it.