Why marketers need to learn the new dance steps

The way people search is evolving, and marketers need to rethink what visibility really means.

For years, search marketing was judged by rankings, traffic and clicks. Success was measured by how many visitors arrived on a website and what they did once they got there.

Today, Google has changed the music.

Search has always evolved. Marketers have adapted to Panda, Penguin, mobile-first indexing, RankBrain, Core Updates, featured snippets and countless changes to the search results page. The businesses that succeed are rarely the biggest or the loudest. They are usually the most adaptable.

It is survival of the flexible.

The latest shift, however, is not simply another ranking update. Increasingly, search engines and AI assistants are providing answers before a click ever happens. According to SparkToro research, around six in ten Google searches now end without a click. The search results page itself has become the answer, accelerated by Google’s AI Overviews and the growing use of tools such as ChatGPT, Perplexity, Gemini and Claude.

Search used to return a list of options and the race was for rank. Now AI increasingly returns a summarised answer, and the race is for citation, accuracy and visibility within that answer.

In a zero-click world, visibility is no longer just about being found. It is about being understood.

What has not changed

Despite all the discussion about AI, the fundamentals of good marketing remain remarkably familiar. Customers still want useful information. They still seek trusted brands. They still value expertise, credibility and authority. AI may be changing how information is discovered, but it has not changed what makes information valuable. In fact, as AI-generated content becomes more widespread, genuine expertise may become an even stronger competitive advantage. The future is unlikely to be won by whoever generates the most words. It will be won by those who create the most value.

The new visibility stack

Most marketers are familiar with content, keywords and rankings. AI introduces another layer. Organisations now need to help machines understand who they are, what they do and why they matter. One way of thinking about this is as a visibility stack.

Content still sits at the foundation. Above that sits structured data. Above that sits entity understanding. Above that sits an AI interpretation. And finally comes recommendation.

The further up the stack you move, the closer you get to becoming the answer rather than simply appearing in the results.

Most organisations focus heavily on creating content but spend far less time helping machines understand what that content actually means. That gap is becoming increasingly important.

In my work on digital strategy, search visibility and go-to-market planning, I increasingly see the same pattern. The issue is not always that AI cannot find an organisation. The issue is that AI does not fully understand it.
In several recent AI visibility audits, I have seen organisations with excellent products, strong brands and high-quality content described incorrectly by AI systems. The issue was not visibility. The issue was understanding.

That is where marketers need to think more systematically. AI visibility should not be approached as a collection of disconnected tactics. It should be approached as a system. Every content asset, entity signal, schema implementation and authority indicator contributes to how machines understand your organisation.

The challenge for marketers is no longer simply creating content. It is designing a digital presence that helps humans and machines reach the same conclusion about who you are, what you do and why you matter.

Three technical concepts worth understanding

One of the objectives of the recent webinar I delivered for CIM was not to turn marketers into developers, it was to help them ask better questions.

Three technical concepts are becoming increasingly important.

Structured data

Structured data is the practice of organising information so that machines understand what each piece of information means. Imagine you own a bakery. To a customer, your address, opening hours and telephone number are obvious. To a machine, they are simply words on a page. Structured data labels those facts so that search engines and AI systems can interpret them correctly.

Schema

Schema is the shared dictionary used to create those labels. Created jointly by Google, Microsoft, Yahoo and Yandex, Schema.org provides a common vocabulary that allows search engines, AI systems and applications to interpret information consistently.

JSON-LD

JSON-LD is the language those labels are commonly written in. Most marketers will never need to write JSON-LD themselves. However, understanding what it is allows them to brief agencies more effectively, challenge developers intelligently and identify opportunities that competitors may be missing. 

The important point is that structured data is not just about AI. For more than a decade it has powered Google’s enhanced search experiences, including review stars, product cards, FAQ results, event listings and knowledge panels.

The real objective: entity clarity

The biggest takeaway from the webinar was not schema itself. It was entity clarity.

An entity is something that search engines and AI systems recognise and understand. Your organisation is an entity. Your products are entities. Your services are entities. Your people are entities. Your expertise is an entity.

Much of modern search and AI retrieval relies on knowledge graphs and entity relationships to determine confidence, relevance and trust. When AI is confused about who you are, it fills in the gaps. Sometimes incorrectly. When AI clearly understands who you are, it becomes significantly more likely to reference, summarise and cite you accurately. 

Schema is the tool. Entity clarity is the outcome.

The future of search is not ranking. It is understanding. Organisations that help AI understand them accurately are more likely to appear in recommendations, summaries and answers. In practice, that creates a new form of competitive advantage at the point where customers are making decisions.

The C.L.E.A.R. framework

To help marketers think about AI visibility in practical terms, I introduced a framework called C.L.E.A.R.

C – Crawlable

AI systems cannot reference content they cannot access.

Marketers should understand whether major AI crawlers such as GPTBot, ClaudeBot, PerplexityBot and Google-Extended are permitted to access their content. If a crawler is blocked, visibility becomes significantly harder.

Practical action: Check whether your robots.txt file allows major AI crawlers to access your key content and service pages.

L – Labelled

Once AI can access your content, it needs help understanding it. For many organisations, four schema types provide strong foundations: Organisation, Product or Service, Person and FAQPage.

One practical improvement many businesses can make immediately is replacing generic author names such as “Admin” or “Web Team” with genuine subject matter experts supported by proper author profiles.

Practical action: Replace generic author names with real experts and ensure those experts have clear profile pages.

E – Entity linked

One of the most important schema properties is called “sameAs”. This allows organisations to connect their website to authoritative external profiles such as LinkedIn, Companies House, Wikidata and industry directories. Think of it as joining the dots. It helps machines understand that the organisation described on your website is the same organisation described elsewhere online.

Practical action: Link your organisation to authoritative external profiles wherever possible.

A – Authoritative

Many marketers assume the largest brands automatically win in AI search. That is not always true.

Authority is often topic-bound rather than domain-bound. A specialist organisation can outperform a larger generalist competitor when answering specific questions if its expertise, authorship and entity signals are stronger. AI visibility is not solely determined by budget. Expertise still matters.

Practical action: Publish content that demonstrates genuine expertise and first-hand experience rather than generic commentary.

R – Reviewed

Test rather than assume.

Use tools such as Schema Validator and Google’s Rich Results Test to validate implementation. More importantly, ask the AI assistants themselves. What does ChatGPT say your organisation does? Who does Perplexity think runs the company? Which sources does Gemini cite? The answers often reveal opportunities and weaknesses far more quickly than traditional reporting tools.

Practical action: Regularly compare how different AI platforms describe your organisation and identify inconsistencies.

Measuring success in a zero-click world

Historically, marketers focused on rankings, sessions and clicks. Those metrics still matter, but they are no longer the whole story. The new questions are:

• Am I being mentioned?
• Am I being described accurately?
• Am I being cited ahead of competitors?

The organisations I work with are increasingly seeing a pattern where impressions rise while clicks fall. In a world where AI systems answer questions directly, that can actually be a sign of growing visibility rather than declining performance. 

The measurement hierarchy I recommend is simple: visibility, accuracy and preference.
First, establish whether you are being mentioned. Then determine whether you are being described correctly. Only then should you focus on becoming the preferred answer.

Actionable insight

One of the biggest risks facing organisations today is becoming paralysed by the pace of change. Every week seems to bring a new AI tool, platform or prediction. It is easy to become distracted by the technology itself. The organisations making the greatest progress are not necessarily those with the largest budgets or the most advanced AI tools. They are the organisations learning the fastest.

This is where a Growth Hacking mindset becomes valuable. Observe what is changing. Form a hypothesis. Run a small experiment. Measure the outcome. Learn. Then repeat. The technology will continue to evolve. The ability to learn faster than competitors may become one of the most valuable marketing capabilities of all.

The challenge for marketers is turning information into action. That requires curiosity, experimentation, measurement and continuous learning. It requires actionable insight.

The opportunity ahead

AI visibility is not a replacement for search marketing. It is the next evolution of it. Google has changed the music once again. The marketers who thrive will be the ones willing to learn the new dance steps. They will be the organisations that create useful content, demonstrate genuine expertise, build authority over time and make themselves easy for both humans and machines to understand.

Because in an AI-driven world, being understood may become just as important as being found.

In the past, competitive advantage came from being found. Increasingly, it may come from being understood.

In an AI-driven search world, the brands that win will not just be visible.

They will be clearly understood.

About the author

Roger Jones is Founder and Managing Director of Actionable Insight Digital Marketing Ltd. He is a digital strategist who thinks like an engineer. He helps organisations improve digital marketing performance through data-led strategy, go-to-market planning, Growth Hacking, search visibility, AI adoption and business transformation. He works with organisations ranging from scale-up businesses to global enterprises across the UK and Europe.

You can connect with him on LinkedIn here.