A lot has been written about AI and ecommerce over the past two years. Most of it circles around the same question: which tools should we buy?
We think that's the wrong place to start.
The more interesting question is what your organization actually needs to look like to compete in a world where shoppers are already using AI before they ever land on your site. That's the question we set out to answer.
The result is Build for What's Next: Commerce Operations and Org Design in the Age of AI Commerce, crafted with the help of Aaron Carpenter at ACV Consulting and Siara Nazir, CEO of Insika. Between the three organizations, we've spent a lot of time inside ecommerce and marketing orgs trying to figure out what's actually working. This paper is our attempt to put that into something useful.
The numbers that got our attention
The data on AI-driven shopping behavior has been moving fast. A few stats that shaped how we thought about this:
AI-driven traffic to US retail sites grew 393% year over year in the first three months of 2026, according to Adobe. BCG estimates that large language models already influence up to 20% of purchase decisions. McKinsey projects that agentic commerce could orchestrate up to $1 trillion in US consumer retail revenue by 2030.
These aren't projections about what might happen. This is happening now.
And here's the part most brands haven't fully internalized yet: AI-referred shoppers are generating 53% more revenue per visit than non-AI traffic. The shoppers coming through AI channels are already further along in their decision. They've done their research. They arrive closer to the buy button. What they find when they get there matters more than it ever has.
Why we wrote it
Our conversations with brand and ecommerce leaders over the last year have had consistent themes. They knew something needs to change. They are feeling pressure around GEO, content at scale, and team capacity. But the guidance available to them is either high level or tool-specific. Nobody is really talking about the organizational and operational changes that have to happen underneath all of it.
The reality is that most ecommerce organizations are running content operations that were designed for a different era. The product page used to be the end of the buying journey. Now it's the landing strip. The teams, tools, and workflows built around the old model aren't going to get brands where they need to go.
There's also a broader shift that doesn't get enough attention. The way shoppers research products has fundamentally changed, and it has raised the bar on product content in a way most brands aren't prepared for.
When someone asks an AI assistant whether your hiking boot is suitable for wide feet, or whether your supplement is safe to take with statins, the AI draws from whatever content exists about that product. If the answer isn't there, the AI will fill the gap, and not always accurately. Every attribute, description, FAQ, use case, care instruction, and piece of editorial content attached to a product is now part of what determines how your brand shows up in that moment.
Multiply that across a catalog of hundreds or thousands of SKUs, each needing that level of depth, and the scale of the content challenge becomes clear. Thin specs and generic copy weren't great before. In an LLM-driven shopping environment, they're a competitive liability.
What the paper actually covers
We organized the paper around four areas that we think matter most right now.
The first is getting knowledge out of people's heads. In most brands, the people who actually know the products are largely disconnected from the content operation. Engineers, merchants, category managers, product developers. Their expertise lives in training decks, spec sheets, Slack threads, and their own memory. An AI-native operation routes that knowledge into the system so it shapes every piece of customer-facing content, not just the ones a single employee happened to touch on launch day.
The second is building content operations that can actually keep up. We walk through what an AI-native content workflow looks like versus the traditional model. Ingest agents, research agents, content generation, independent scoring, automated publishing, continuous optimization. It sounds like a lot but the point is that once it's set up, the catalog improves on its own rather than decaying quietly.
The third is governance. Speed without guardrails creates its own problems. We cover how to build review and publishing frameworks that keep content on-brand and accurate without requiring a human to approve every line of copy.
The fourth is organizational design, which is where most transformations get stuck. Some roles change significantly. Some go away. New ones appear. We include an example of what a $75M omnichannel brand's marketing team looks like before and after this kind of redesign, along with a practical change management framework for leaders who need to bring their teams along.
We also close with a "Where to Start" section for leaders who want to take a first step without committing to a full transformation. It starts with reading your own product pages as a shopper would. Most people find that exercise more clarifying than they expected.
If you're a CEO, CMO, or ecommerce leader trying to figure out what AI transformation actually looks like inside your organization, this paper was written for you. It's practical, it's specific, and it doesn't assume you have an unlimited budget or a team of engineers.
You can download it at https://www.brandfuel.ai/whitepaper.
If it raises questions or you want to talk through what this looks like for your specific situation, reach out. We're happy to have that conversation.