Why AI Doesn’t “Just Work” for Commerce
And what to do about it
AI is everywhere. But for most commerce brands, it still feels like it’s happening over there—in pitch decks, in generic copilots, in tools that promise intelligence but fall short when it comes to action.
Why? Because AI doesn’t just need data. It needs context.
And most commerce stacks aren’t built to provide it.
The missing link between data and action
Over the past decade, brands have been told that collecting first-party data would unlock smarter decisions, better campaigns, and deeper customer relationships. And they’ve done the work—investing in data warehouses, CDPs, attribution tools, ESPs, and more.
But here’s the catch: simply having data isn’t enough. For AI for commerce to deliver on its promise, it needs to understand what that data means, where it came from, and how it connects across systems.
It needs context. Without it, even the most powerful LLMs are just guessing.
The problem with point solutions (and the AI they're layering on)
The average commerce brand is managing 30–50 martech and data tools. These point solutions may each provide value—but they’re also working with siloed slices of your data.
Now those same vendors are racing to bolt AI onto their existing platforms. But adding intelligence to a fragmented stack doesn’t solve the problem—it compounds it. You end up with multiple “smart” systems, each interpreting partial data through their own lens, none of them aligned.
That’s not intelligence. That’s noise.
AI that works starts with a platform that's built for it
At Chord, we think about AI differently. For us, it’s not about novelty. It’s about utility.
Real commerce AI should:
- Run on governed, enriched, commerce-modeled data
- Understand history, identity, and nuance—not just numbers
- Deliver explainable AI outputs that are auditable and actionable
- Power not just insights—but execution and measurement, too
This is the foundation we’ve spent years building: one unified commerce data platform with an AI copilot embedded, so every question leads to an answer, every answer to an action, and every action to measurable results.
No duct tape. No black boxes. Just AI for commerce that works—because the infrastructure was designed for it from day one.
Commerce needs more than a thought partner
There’s a place for AI that helps you brainstorm subject lines or outline a product brief. But commerce brands need more than a thought partner. They need a growth partner.
That means AI agents that don’t stop at insights—but can hand off tasks across systems, activate campaigns, and measure impact. Full circle. End to end.
That’s the version of explainable AI for commerce we’re building at Chord. And we’re just getting started.
FAQs on AI for Commerce
Why doesn’t AI just work for commerce brands?
Most AI tools run on fragmented or siloed data, which lacks context. Without context, even powerful models can’t deliver accurate or actionable outputs—they just guess.
What makes AI for commerce different from generic AI tools?
Commerce AI needs to understand SKU depth, multi-channel journeys, lifetime value, and attribution models. Generic AI tools aren’t designed for this complexity.
What is explainable AI in commerce?
Explainable AI means every output is traceable back to governed data. Teams can audit, verify, and adjust AI-driven recommendations—no black boxes, no hallucinations.
How is a commerce data platform better than multiple point solutions?
Point solutions only solve narrow problems and fragment data. A unified commerce data platform consolidates, enriches, and governs data, enabling AI to work across systems with accuracy and speed.
What do brands gain from using Chord?
Brands using Chord see faster insights, lower martech costs through consolidation, and campaigns launched 2x faster—all powered by AI that actually works on a trusted foundation.
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