Why the Data Layer Comes First
In the last article, “From Visibility to Callability,” we proved that invocation isn’t theory anymore — it’s happening right now.
Platforms like Google, Shopify, Walmart, and OpenAI have already re-engineered around structured, callable data.
The evidence is overwhelming: if your data can’t be called, your brand can’t compete.
But evidence alone doesn’t build capability. Invocation readiness demands an operational shift — one that blends data discipline, technical integration, and cultural change. It’s not a project; it’s an evolution of how digital systems, teams, and incentives work together.
Invocation doesn’t happen on web pages. It occurs in data layers. This isn’t a “nice-to-have.” It’s the core requirement. Invocation assumes your brand is callable — but without structured, machine-readable, context-aware data, there’s nothing to call.
Why Most Companies Aren’t Ready
I’ve seen the data gap firsthand. A few years ago, I attended a workshop featuring multiple brands and a large online retailer. The discussion turned to why online sales and Google rankings for their products in the storefront weren’t better across the board. The retailer didn’t mince words:
“Honestly, your feeds are crap.”
The room went silent. The retailer explained: the feeds were full of acronyms, abbreviations, and vague product names. Critical details — such as size, ingredients, and attributes — were missing. The brands looked stunned.
“We thought you optimized them,” one brand manager said.
“With equal confusion, the retailer replied: ‘How do you expect us to do that for over 10,000 merchants?’
Their team explained that they optimize growth and margin opportunities in their system, but the fundamentals of feed optimization are up to the brand.
That moment landed hard. Brands had assumed platforms would “fix” their data. Platforms had assumed brands would own their fundamentals. The result was a governance gap — and the customer experience suffered.
That exchange captured the heart of the problem. Brands assume platforms will “fix” their data. Platforms assume brands will own the fundamentals. The result is a governance gap — and invocation will only magnify it.
Where Companies Fall Short
- No single source of truth
Product and service data is scattered across CMS, ERP, PIM, spreadsheets, and vendor systems. There’s no unified, callable layer. - Flat content, no attributes
Many product descriptions are written as a single, lengthy paragraph. Invocation requires attributes (color, size, material, and use case) to facilitate comparisons and summaries. - No feed discipline
Companies still treat feeds as an afterthought. They hand-manage them for Google Shopping, if at all, rather than running continuous, API-driven updates. - No invocation verbs mapped to data
It’s not enough to know you sell “air conditioners.” Can your system answer “install,” “repair,” “compare,” “schedule”? These verbs must connect to callable data or services.
Rethinking the Architecture
To be invocation-ready, companies need to rethink their data architecture from the ground up:
- Product feeds as a foundation: SKU-to-URL maps, structured attributes, availability, and pricing exposed in callable formats.
- Attribute-rich taxonomies: From materials and functions to localized descriptions and regulatory notes.
- Headless and API-first mindset: Every service should be callable — not hidden behind a web form.
- Content atomization: Long-form content broken down into modular blocks that can be invoked in context.
- Governance and freshness: Processes to ensure feeds and attributes are accurate, current, and trusted.
What Good Looks Like
Contrast that with companies building modern feed infrastructure. Feedonomics (no connection to them) is one example of how companies need to think. Their entire business is about cleaning, normalizing, and syndicating product data at scale. They don’t just pass through messy catalogs — they transform them, map attributes correctly, and distribute them across hundreds of endpoints.
That’s the mindset every brand needs to adopt:
- Treat product feeds as infrastructure, not marketing afterthoughts.
- Normalize and enrich product data so it’s callable across AI systems, marketplaces, and agent environments.
- Keep feeds fresh and trustworthy so that when an AI system invokes your product data, it reflects reality.
Why This Matters for Invocation
Invocation won’t run on the marketing website. It will run on the data fabric underneath it.
- If Google Shopping already works this way…
- If Amazon’s entire marketplace runs on feeds…
- If Walmart’s product discoverability depends on structured attributes…
…then why are so many companies still dragging their feet on data structure?
Because in the old discoverability model, you could get away with a flat product page. In the invocation model, you can’t.
Closing
Companies often discuss AI and generative search as if it’s solely about creating creative content. But the real choke point — and the real opportunity — is in the data layer.
Without structured, callable, and attribute-rich feeds, your brand will be invisible in the era of invocation.
With them, you’re not just found. You’re called.
