Naming things matters
Traditional SEO is gone
Direct answers on Google results pages (AIO), cause over half of all searches to end without a click. Google has shifted its model and if you aren't in the LLM, you are invisible.
AI Procurement is Coming
In addition to LLMs, AI Agents are now moving to procurement and if they can’t understand or misconstrue your data, you will not show up.
"Marketeers want to appeal to emotions, to needs, to provide solutions, they need to continue to do that, they also need to ensure that what they are selling, to whom and for how much is also delivered unambiguously. Otherwise LLMs in search or procurement could miss or muddle your message."
"Of the CSCOs and COOs surveyed, 64% say gen AI is already transforming their supply chain operations workflows."
— IBM
"Developing a universal semantic layer is now a must‑do for D&A leaders either leading or supporting AI."
— Gartner
From Rich Results to Machine Maps
Traditional to AI-Readable
Google Schema used to be about ranking top in the list of blue links (SERPS). Today, it’s a layer of translation between human-focussed web content and AI data engines.
Structured Entities over Strings
Generative search engines (Various LLMs, AI Overviews) don't just index text strings, they synthesise structured entities and measure relationships.
Humans like storytelling and that's what marketing copy does, it's nuanced and human. LLMs do not like nuance and storytelling, they like entities and relationships. So if we force LLMs to guess what our software or organisation is by making them read human-focussed marketing content can confuse them leading to poor or no Google results. With LLMs inference is much less reliable than instruction.
"The core mission remains unchanged: to connect your solution with the people looking for it. However, the mechanism has shifted from “keywords and links” to “entities and trust.” By structuring your data for agents, building radical authority through experience, and owning the “messy middle” of comparison, you can ensure that your brand isn’t just found—it is chosen. "
Confusion Causes Chaos
Our Biggest Blind Spot
Internal inconsistency is our biggest blind spot in the generative age. Relying on contradictory or misaligned labels weakens entity clarity.
Mixed Signals = Noise
When marketing labels a tool a ‘Product’, documentation calls it an ‘App’ sales copy uses metaphors, and pages lack consistent structured data, machine crawlers get mixed signals. For example until August 2026 Google Schema for Mosaic on the Kolekti site was ‘Product’, on Atlassian’s MPAC Mosaic it was listed as ‘Software App’ and on Docs there was schema no at all.
The Reality: Degrading Entity Confidence
By confusing the description of any entity we force search engines and LLMs to try to decode contradictory descriptions, read on page marketing copy and Docs all of which confuse the AI and erode their confidence.
"Structured data is defined as a standardised format for providing explicit information about a page's content to search engines and AI platforms."
Beyond App Schema: Build a Coherent Knowledge Graph
Beyond App Markup
Some initial studies suggest basic schema doesn't directly move traditional rankings, and that's fine, because we aren't stopping at standalone app markup.
AI Alignment
Structuring explicit entity relationships ensures that whether an LLM encounters our product via sales copy, docs, or third-party mentions, all nodes map back to a single source of truth. So we use Schema as the bedrock and then translate that into all other messaging.
Start with Schema to define your reality, then build all messaging upon it.
We use explicit Schema to anchor every dimension of our business for machines:
- What it is: Defining the product as a
SoftwareApplicationwith explicitfeatureListproperties. - Who owns it: Attaching verified
Organizationand author credentials. - What it costs: Hardcoding clear pricing tiers and trial parameters (
Offer). - Where else it lives: Using
sameAsto connect external channels—social profiles, YouTube demos, and marketplace listings.
Once this machine-readable source of truth is locked, we translate it directly into our marketing copy, documentation, and external touchpoints. Agree on the Schema first, and let the content flow from there.
"Developing a universal semantic layer is now a must‑do for D&A leaders either leading or supporting AI. It is the only way to improve accuracy, manage costs, substantially cut AI debt, align multiagent systems, and stop costly inconsistencies before they spread. D&A leaders must budget for semantic capabilities as a nonnegotiable foundation."
Schema as an Operational Framework
Team Alignment
Schema isn't just a technical SEO trick for developers; it’s an operational standard for team content alignment.
Upfront Guardrails
Agreeing on naming, taxonomy, and entity labeling upfront sets clear guardrails across ALL content, product, and engineering teams. As Schema is the most stripped down, it makes sense to start there.
Strategic Takeaway: Operational Harmony
Standardised schema ensures page content, metadata, and site architecture work in harmony, preventing future content drift and intent overlap before they ever hit production.
"Understanding how Google links entities in the Knowledge Graph can help to ensure that the entities and topics you want are connected to your brand. It can also uncover connections between entities you don’t want associated with your brand."
The Way Forward: Clarity, Organisation, Discovery
Standardise Vocabulary
Establish agreed naming conventions for core products and software across all team silos.
Implement Cross-Entity Schema
Deploy agreed schema for organisations, apps, authors, and any entity touchpoints. (The Sanity CMS has a ‘deploy once’ feature, if we don’t use that use a sameAs pointer in the main object's Schema.)
Enforce Single Canonical Paths
Protect entity authority and stop internal confusion before publishing.
"We are no longer just putting up signposts for search crawlers, we are building the map that AI uses to navigate the space."
Email me →