Beyond SEO: How Do You Know Whether AI Actually Understands Your Business?

Sun, 27 Sep 2026 09:08:41 +0000

For years, businesses have measured their digital visibility through a familiar set of metrics:

Google rankings. Organic traffic. Click-through rates. Backlinks. Conversions.

These metrics still matter.

But AI is introducing a very different question.

Does AI actually understand what your business does?

That question sounds simple.

It isn't.

A company may rank well on Google and still be poorly represented by AI systems. Its website may attract thousands of visitors while AI assistants struggle to explain what makes the company different.

Worse, an AI system may understand the company — but associate it with the wrong products, markets or expertise.

This creates a new measurement problem.

Traditional SEO measures whether people can find you.

AI Search Optimization (AISO) must increasingly measure whether machines can understand, retrieve, trust and correctly represent you.

And those are not necessarily the same thing.

The New Visibility Problem

Imagine a company that provides specialized software for migrating medical imaging data between hospital PACS systems.

Its website repeatedly uses phrases such as:

healthcare IT solutions
enterprise imaging
digital transformation
medical imaging technology
DICOM solutions

From a traditional SEO perspective, these phrases may be perfectly reasonable.

But ask an AI assistant:

“Which companies specialize in complex DICOM PACS data migration?”

Does the company appear?

Now ask:

“What does Company X specialize in?”

Does the AI describe PACS migration?

Or does it simply say:

“Company X is a healthcare technology company providing digital solutions.”

Technically correct.

Commercially almost useless.

The AI has discovered the company.

But it has not developed a sufficiently precise understanding of it.

That distinction is becoming increasingly important.

Search Visibility Is Not the Same as AI Understanding

Traditional search engines historically helped users locate information.

Generative AI systems increasingly attempt to interpret the question, retrieve information, synthesize multiple sources and construct an answer.

Google describes its generative search systems as using techniques including retrieval-augmented generation, or RAG, where relevant and current web information is retrieved to help ground AI-generated responses. Google also says its AI features can perform multiple related searches across subtopics and data sources when constructing answers.

This changes the visibility model.

A page no longer needs only to match a search phrase.

Its information needs to contribute meaningfully to an AI system's understanding of a subject.

Consider the difference:

SEO question:

Does my page rank for “DICOM migration software”?

AISO question:

When someone asks an AI about migrating DICOM data between PACS systems, does the AI associate my company with that problem, understand what we provide and consider our content useful enough to reference?

The second question is much broader.

So How Do We Measure AI Understanding?

There is unlikely to be one universal “AI Understanding Score.”

Different AI platforms use different models, retrieval systems, indexes and information sources.

Instead, businesses should test AI understanding across several dimensions.

I use a simple framework:

Each stage answers a different question.

1. Discover: Can AI Find You?

This is the foundation.

If AI search systems cannot access or discover your information, everything else becomes difficult.

Test simple questions:

“What is [Company Name]?”

“What does [Company Name] do?”

“What products does [Company Name] provide?”

Then look beyond whether your company is merely mentioned.

Check:

Is the correct company identified?
Is your website referenced?
Are current products visible?
Is old information being presented?
Are important pages missing?
Is another company with a similar name being confused with yours?

Technical accessibility matters here.

OpenAI states that public websites can appear in ChatGPT search and recommends allowing its OAI-SearchBot if publishers want their content discoverable and eligible to appear in summaries and citations.

Google similarly states that pages must be crawlable, indexed and eligible for Search before they can appear as supporting links within its generative AI experiences.

Discovery therefore remains partly an SEO problem.

But discovery is only the beginning.

2. Understand: Can AI Explain What You Actually Do?

Now make the test harder.

Ask:

“Explain what [Company] does.”

Then:

“Explain [Company] to a hospital CIO.”

Then:

“What problems does [Company] solve?”

Compare the answers against what you want the market to understand.

For example:

What the business actually does	What AI says
Provides specialist DICOM migration software	Healthcare IT company
Helps migrate PACS archives	Medical imaging solutions provider
Handles complex DICOM interoperability issues	Digital healthcare company
Specializes in vendor-neutral migration workflows	PACS consulting company

None of the AI descriptions are necessarily false.

But they progressively lose the company's differentiation.

This is what I call semantic dilution.

The information exists online, but the distinctive meaning of the business becomes diluted into a broad category.

That is an AISO problem.

3. Associate: Does AI Connect You With the Right Problems?

This is one of the most important tests.

Do not mention your company.

Instead, describe the customer's problem.

For example:

“How can a hospital migrate millions of DICOM studies between PACS systems?”

“What causes DICOM-compliant systems to reject studies during PACS migration?”

“What tools can help validate a PACS migration?”

“How can hospitals reduce risk during DICOM archive migration?”

Now examine the response.

Does AI associate your company, product, research or content with these subjects?

This is fundamentally different from asking:

“Tell me about my company.”

The first test measures brand recognition.

The second measures problem association.

Commercially, problem association may be considerably more valuable.

Customers rarely begin their buying journey by searching for a company they have never heard of.

They begin with a problem.

4. Trust: Does AI See You as a Credible Source?

Finding your website is one thing.

Using it as supporting evidence is another.

Ask detailed questions about subjects where your organization has genuine expertise.

Then examine:

Does AI cite your articles?
Does it reference your research?
Does it use your explanations?
Does it associate named experts with the subject?
Are competitors or generic publications cited instead?

This exposes an important difference between visibility and authority.

Suppose you have published ten excellent articles about PACS migration.

But when AI answers detailed PACS migration questions, it consistently cites other sources.

You have content.

You may even have search traffic.

But you have not yet established strong enough AI-visible topical authority around those questions.

That is useful information.

It tells you where the next stage of content development should focus.

5. Retrieve: Are You Appearing in AI-Generated Answers?

This is where AI visibility is becoming increasingly measurable.

Microsoft's Bing Webmaster Tools now includes an AI Performance report showing how website content is used in AI-generated answers across Microsoft Copilot, Bing AI experiences and selected partner integrations.

Among the available measurements are:

total citations
cited pages
citation trends
grounding queries
topics
user intent
citation share

This is significant.

For the first time, businesses can begin moving from:

“I think AI knows about us.”

to:

“Here are the subjects for which AI systems are actually retrieving and citing us.”

That is a much more useful measurement.

But there is an important warning.

A citation is not automatically proof of authority.

Microsoft explicitly cautions that citation metrics do not represent ranking, authority or importance.

They show that your content was used.

Interpretation still matters.

Now comes perhaps the hardest test.

Ask AI questions that resemble genuine buying journeys.

For example:

“What approaches should a hospital consider for PACS data migration?”

“What solutions exist for migrating DICOM archives?”

“Which vendors provide tools for PACS migration?”

“What should I evaluate when selecting a DICOM migration platform?”

Do not simply count how often your brand appears.

Study the context.

There is an enormous difference between:

“Company X also provides healthcare IT services.”

and:

“Company X provides a DICOM migration platform designed to control and validate PACS migration workflows.”

The second answer demonstrates much stronger understanding.

The objective of AISO should therefore not simply be:

Get mentioned by AI.

It should be:

Be correctly understood in the context where your expertise matters.

Build an AI Understanding Test

Businesses can turn this into a repeatable exercise.

Create perhaps 20–30 questions across five categories.

Brand Questions

What does our company do?
What are our main products?
What markets do we serve?
What makes our company different?

Problem Questions

What problems does our solution address?
How should companies solve those problems?
What technologies are available?

Expertise Questions

Who are credible sources on this subject?
What companies have expertise in this area?
What resources explain this problem well?

Commercial Questions

What solutions are available?
Which companies provide them?
What factors should buyers evaluate?

Comparison Questions

How does our approach differ from alternatives?
What types of organizations would use our solution?
Where does our product fit within the broader market?

Run the questions periodically across major AI search platforms.

Then record the results.

Don't Just Measure Mentions

This is important.

AISO measurement should not become another vanity-metric exercise.

A company could generate hundreds of AI mentions without those mentions contributing anything commercially meaningful.

Instead, measure the quality of understanding.

For each important question, examine five things:

Accuracy
Is the information factually correct?

Relevance
Does AI associate you with the subjects that matter to your business?

Specificity
Does AI understand your differentiation, or describe you generically?

Citation
Does AI retrieve your own content as supporting evidence?

Consistency
Do different questions and AI systems produce broadly similar descriptions of your business?

Together, these provide a far more meaningful picture than simply counting mentions.

The Consistency Test Is Particularly Powerful

One of the easiest ways to identify weak AI understanding is to ask the same concept in different ways.

For example:

“What does Company X do?”

Then:

“What is Company X known for?”

Then:

“What problems does Company X solve?”

Then:

“Which companies specialize in [your core expertise]?”

If the answers vary dramatically, your digital identity may not be sufficiently clear.

AI systems may have discovered fragments of information about you without forming a strong association between:

Company → Expertise → Problem → Solution → Evidence

That relationship is critical.

Your Website Is Only Part of the Evidence

Another common mistake is assuming that AI understanding comes entirely from your website.

It doesn't.

AI search systems can retrieve information from a broader information ecosystem:

your website
industry publications
news articles
technical documentation
interviews
professional profiles
research papers
partner websites
conference material
community discussions
independent references

This means AISO cannot simply be:

“Add more keywords to the website.”

The objective should be to create a consistent digital evidence trail.

If your website says you specialize in something but almost nothing elsewhere on the web supports that association, the signal is weaker.

If your website, technical articles, industry publications, case studies and independent references repeatedly connect your organization with the same area of expertise, the relationship becomes much clearer.

Structured Data Helps — But It Is Not a Magic AI Switch

Technical structure still has a role.

For example, Google states that Organization structured data can help it understand administrative information about an organization and disambiguate one organization from another.

Useful information can include details such as:

organization name
URL
logo
contact information
identifiers
relevant organizational details

But structured data should not be treated as a shortcut to AI visibility.

Google specifically says there is no special Schema.org markup required for its generative AI search features.

The lesson is important.

AISO is not about finding a hidden technical tag that suddenly makes AI recommend your company.

It is about making your business unambiguous, useful and well-supported by evidence.

The Real KPI: Semantic Accuracy

SEO gave us a convenient number:

Ranking position.

AI search is more complicated.

There may be no permanent “Position 1.”

Answers can vary depending on:

the question
context
location
model
retrieval source
freshness
user intent
available evidence

So perhaps one of the most useful AISO KPIs is not ranking at all.

It is semantic accuracy.

In simple terms:

How closely does AI's understanding of your business match the understanding you want customers to have?

That can actually be measured.

Define the five or ten concepts that your organization wants to own.

For example:

Company X

↓
DICOM migration

↓
PACS migration

↓
migration validation

↓
vendor-neutral interoperability

↓
healthcare imaging expertise

Then test whether AI consistently makes those connections.

The gaps become your AISO roadmap.

From AISO Audit to Content Strategy

This is where measurement becomes useful.

Suppose your audit finds:

AI knows your company exists.

Good.

AI understands your industry.

Good.

AI does not associate you with your specialist expertise.

Problem identified.

AI cites competitors when discussing that expertise.

Opportunity identified.

AI misunderstands one of your products.

Content problem identified.

AI consistently retrieves one particular technical article from your site.

Authority opportunity identified.

Instead of publishing content simply because a keyword has search volume, you can now ask:

What does AI currently misunderstand — or fail to understand — about us?

That question can generate an entirely different content strategy.

AISO Becomes a Feedback Loop

The process should therefore look something like this:

1. Define how you want to be understood

↓

2. Test how AI currently understands you

↓

3. Identify missing or incorrect associations

↓

4. Strengthen website content and technical clarity

↓

5. Publish authoritative content around the missing concepts

↓

6. Build supporting evidence across credible external sources

↓

7. Measure citations, retrieval and AI responses

↓

8. Test again

This turns AISO from a collection of optimization tactics into a measurable business process.

And This Is Where AISO Goes Beyond SEO

SEO remains essential.

Google itself continues to emphasize that established SEO practices — crawlability, useful content, technical accessibility and strong page experience — remain relevant to generative AI search.

So AISO should not replace SEO.

It extends the objective.

SEO asks:

Can people find us?

AISO asks:

Can machines understand us well enough to represent us accurately when people ask questions?

That distinction will become increasingly important as search evolves from lists of links toward synthesized answers, recommendations and AI agents.

Because in an AI-mediated internet, visibility alone may not be enough.

Your business needs to be understood.

And perhaps the most important AISO question a company can ask today is remarkably simple:

If AI were explaining our business to a potential customer right now, would we be happy with what it said?

If the answer is no, you have just found your next optimization opportunity.

References and Further Reading

Google Search Central — Optimizing your website for generative AI features on Google Search
Google explains how conventional SEO, technical accessibility, unique content and retrieval-based AI search fit together.

Microsoft Bing Webmaster Tools — AI Performance
Microsoft documents AI citations, cited pages, grounding queries, topics, intents and citation-share measurements for supported AI experiences.

OpenAI — Publishers and Developers FAQ
OpenAI explains how websites can appear in ChatGPT search, how OAI-SearchBot works and how publishers can measure referral traffic from ChatGPT.

Google Search Central — Organization Structured Data
Google explains how Organization markup can help its systems understand and disambiguate organizations.

OpenAI — Overview of OpenAI Crawlers
Technical documentation covering OAI-SearchBot, GPTBot and website crawler controls.

Coder Legion - Recent posts tagged ai-search-optimization

Beyond SEO: How Do You Know Whether AI Actually Understands Your Business?

The New Visibility Problem

Search Visibility Is Not the Same as AI Understanding

So How Do We Measure AI Understanding?

Discover → Understand → Associate → Trust → Retrieve → Recommend

1. Discover: Can AI Find You?

2. Understand: Can AI Explain What You Actually Do?

3. Associate: Does AI Connect You With the Right Problems?

4. Trust: Does AI See You as a Credible Source?

5. Retrieve: Are You Appearing in AI-Generated Answers?

6. Recommend: Do You Appear When Buyers Ask for Solutions?

Build an AI Understanding Test

Brand Questions

Problem Questions

Expertise Questions

Commercial Questions

Comparison Questions

Don't Just Measure Mentions

The Consistency Test Is Particularly Powerful

Your Website Is Only Part of the Evidence

Structured Data Helps — But It Is Not a Magic AI Switch

The Real KPI: Semantic Accuracy

From AISO Audit to Content Strategy

AISO Becomes a Feedback Loop

And This Is Where AISO Goes Beyond SEO

References and Further Reading