In the first article of this series, we explored how invocation marks the next evolution of digital discoverability — the shift from being found to being called. Invocation is about readiness: when a system or user asks for something to be done, your brand is what gets pulled into that action.
But here’s the unglamorous truth: invocation doesn’t run on web pages. It runs on data layers.
No matter how strong your brand is or how beautiful your content looks, you can’t be called if your data can’t be read, synthesized, or trusted by machines.
Invocation Is Only as Good as the Data It Can Call
Generative engines, shopping agents, and enterprise copilots don’t “read” pages; they orchestrate data.
They evaluate context, match attributes, and surface structured responses.
For that, they rely on organized, labeled, and callable information — not free-form copy.
Think of it as three evolutionary stages:
| Era | Dominant Mechanic | Optimization Focus | Fuel |
|---|---|---|---|
| Discoverability | Search & keywords | Visibility | Keywords & links |
| Model Eligibility | Generative answers | Authority | Content & schema |
| Invocation | Agents & orchestration | Callability | Structured data & APIs |
Invocation isn’t a content problem. It’s a data infrastructure problem.
The Evidence Is Already in Play
This isn’t theory. The market has already moved. Every major digital platform is quietly building around structured, callable data.
A. Retail and Commerce: Feeds as Infrastructure
- Google Shopping Graph relies entirely on structured product feeds; incomplete data means invisibility.
- Amazon Marketplace runs on product attributes — size, color, material, ratings — not on your product descriptions.
- Walmart Super Agents are designed to call live inventory, pricing, and logistics data, positioning Walmart’s agentic commerce as a potential “Google killer.”
B. Agent Ecosystems and Protocols
- Shopify x OpenAI Integration lets merchants build agents that respond to prompts, surface products, and stage checkout flows — possible only with structured catalog data.
- Model Context Protocol (MCP) is emerging as a universal standard for connecting LLMs to APIs, databases, and enterprise systems.
- OpenAI Instant Checkout with Shopify and Etsy depends on callable product and transaction data, not page scraping.
Then came DevDay 2025 — the real tipping point.
At OpenAI DevDay 2025, Sam Altman described intelligence that works across applications and data sources.
OpenAI introduced AgentKit and an Apps SDK that let third-party apps operate inside ChatGPT — not as links, but as native, in-model experiences.
These embedded apps render UIs, trigger actions, and connect directly to back-end systems.
One demo featured Zillow’s housing search running entirely within ChatGPT. Users could filter listings, adjust price ranges, and view details — all without ever leaving the conversation.
Altman’s line summed it up:
“The intelligence will span applications and data.”
That vision is invocation, realized. It confirms that the future will be multi-dimensional — agents orchestrating and synthesizing data from multiple systems simultaneously.
If your product, pricing, or service data isn’t structured and connected, you won’t be part of that orchestration.
C. Enterprise Agents and Data Unification
- Amazon Q unifies structured and unstructured enterprise data, enabling agents to operate within real-world workflows.
- Alation and other data-governance platforms emphasize that schema discipline and metadata are prerequisites for trustworthy AI agents.
D. Industry Warnings and Analyst Signals
- TechRadar captured it perfectly: “Garbage in, Agentic out.”
- Forrester, Gartner, and IDC all highlight data fabric, data mesh, and semantic layers as foundational to the adoption of agentic AI.
Without these, agents cannot ground or act accurately.
The Reality Check: Where Companies Actually Are
Despite all this progress, most organizations are still optimized for the discoverability era.
- Their content is flat.
- Their data is siloed.
- Their measurement models still rely on sessions and page views.
Few have anything resembling an invocation layer — the APIs, agents, and structured datasets that make their brand callable.
I would see this daily during my Hreflang Builder days. Out of more than 1,000 Hreflang Builder clients, only two could provide a clean SKU-to-URL feeds that we could use to map between markets.
A few years ago, in a joint workshop with several brands and a major online retailer, frustration boiled over when results lagged. The retailer finally said:
“Honestly, your product feeds are crap.”
The room froze.
The retailer went on to complain that the feeds were full of acronyms, abbreviations, and missing attributes. The brand managers incorrectly assumed the retailer would clean them up and optimize them. The retailer replied:
“We optimize for growth and margin. The fundamentals of feed quality are your responsibility.”
That moment exposed the governance gap:
brands expect platforms to fix their data;
platforms expect brands to own it.
In the invocation era, that disconnect will be fatal.
Why the Data Layer Is the New Marketing Layer
If invocation is the future of discoverability, then structured data is its oxygen.
You can’t be invoked without:
- Consistent, attribute-rich product and service data.
- Callable APIs that expose availability, pricing, or specs.
- Semantic alignment across ERP, PIM, and CMS systems.
- Trust mechanisms that validate freshness and accuracy.
In the invocation era, the feed is the strategy.
What Comes Next
Invocation may sound futuristic, but the infrastructure is already here: Google’s feeds, Shopify’s MCP, Walmart’s Super Agents, Amazon Q, and now OpenAI’s cross-app AgentKit.
They all prove the same point: AI agents can only call what your data makes callable.
The uncomfortable truth?
Most corporate data layers aren’t even close. They’re inconsistent, incomplete, and locked inside systems never designed for machine consumption.
In the next article, From Visibility to Callability — Building Invocation Readiness, we’ll move from evidence to execution — showing how to fix the data layer, design callable systems, and build a brand that’s truly ready to be invoked.
