Your AI Strategy Is Only as Good as Your Data
Most eCommerce leaders will tell you they have an AI strategy. Ask them whether their product data is clean, complete, and structured so an AI model can actually use it, and the room goes quiet.
That’s the real problem. And it’s more fixable than you think.
The Shopping Journey Just Changed. Again.
Your customers stopped browsing. They’re asking. “What’s the best running shoe for flat feet under $150?” “Which air fryer is right for a small kitchen?” They type the question into ChatGPT, Gemini, or Claude and get a curated answer back in seconds.
If your product data can’t speak clearly to an AI model, you’re invisible at the exact moment someone is ready to buy. Klaviyo’s Global AI Shopping Index found that 78% of consumers have already used AI to shop or research products in the last three months, and 75% have walked away from a purchase because they couldn’t get an instant answer.
The brands that show up are the ones whose data is structured, schema-marked, and surfaced through feeds and APIs that AI systems can actually crawl and reason over. The rest are betting their visibility on a search box that fewer people are using every quarter.
The Gap Between Strategy and Results
Having a strategy isn’t the same as getting value from one. You’ve done the workshops, built the decks, and gotten the executive buy-in. Forrester found that 66% of organizations say they have an AI strategy. Far fewer are seeing what they were promised.
The gap is almost always the data.
Siloed catalogs. Customer records scattered across platforms that have never talked to each other. Attribute fields written for a merchandiser, not for a model that needs context and intent. You can’t bolt AI onto a broken data foundation and expect it to perform. It doesn’t work that way.
None of that is a failure on your part. It’s the accumulated weight of legacy systems that were never built for the way customers shop now. But it is yours to solve, and the payoff for solving it is immediate. Getting unstuck starts with being honest about where the foundation actually is.
One more thing, and it’s the part most teams get backwards. Don’t build for what AI can do today. Build for where it’s going. The tools are changing so fast that nobody, including the people building them, has a firm grasp on what’s next. That uncertainty is the reason to prepare now, not the reason to wait. You can’t predict the exact capability you’ll need eighteen months out. You can make sure that when it shows up, your data and your foundation are ready to use it instead of being the reason you can’t.
Where Maze Comes In: Getting You Unstuck
Maze closes the gap between AI strategy and actual business results. It starts with your data, and it moves fast.
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Get your data house in order, and make it AI-readable.
We consolidate and clean your product and customer data so it’s consistent, accurate, and structured to power AI-driven experiences. Good product data isn’t just organized. It’s descriptive, contextual, and written to speak to both the customer and the model they’re asking for a recommendation. We handle both layers: structuring the data, then building the technical foundation underneath it, including schema, feeds, and agent-accessible catalog architecture. The bar isn’t “is your data organized?” It’s “can an AI model read it, reason over it, and act on it right now?”
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Activate what you already have.
You probably don’t need a full platform overhaul. We audit your existing stack, including your eCommerce platform, CRM, PIM, and ESP, and find where AI can be unlocked or layered into what’s already there. You’re closer to ready than you think. Your providers, Shopify, Salesforce, Klaviyo, and the rest, are pouring millions into AI and innovation inside the tools you already pay for. You don’t need to build your own. Take advantage of what they’ve already built. The job is to switch it on and point it at the right problems, not to reinvent it from scratch.
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Build a roadmap that connects to revenue.
We prioritize the use cases that change real outcomes, including conversion, retention, and average order value, then build a phased plan your team can execute without getting lost in the weeds.
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Get your team ready to use it.
The tools don’t work if your team doesn’t trust the outputs or know how to use them. We provide enablement alongside the technical work, so adoption actually sticks. Name an AI evangelist to lead the way, someone who owns the use cases, brings the rest of the organization along, and educates as they go. Start with the simple stuff: the repetitive, low-judgment work that eats your team’s day. Automate that first, and you free up your best people to do what you actually hired them for, which is using their brains to innovate and move the business forward.
What AI-Ready Actually Looks Like
It’s not complicated, but it takes intention. Clean, complete product data across every channel. A single view of your customer. AI investments tied to specific outcomes, with a roadmap to get there. And a team that knows how to use the tools and iterate on what comes back.
So what does that mean for you? Does your data support it? Does your catalog? Does your team?
Your customers are already asking AI what to buy. The only question is whether your business shows up when they do. That starts with your data, and it’s where we start too.
Find Out Before Your Competitors Do
Not sure your data is AI-ready? Maze will run a complimentary audit of your product and customer data. No commitment, just a clear read on where you stand and what to do next. Reach out and we’ll get started.
Your customers aren't waiting for you to get your house in order. Neither are we. #Getunstuck
Maze is a full-service enterprise commerce and digital transformation partner. If you’re ready to take the first step on AI readiness, reach out.
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Sources:
https://www.klaviyo.com/newsroom/ai-shopping-index
https://www.forrester.com/blogs/strategic-ai-readiness-how-to-move-from-hype-to-scalable-impact/
https://www.gartner.com/en/articles/ai-strategy-for-business