AI Overviews & AI Mode

Where to start - and how to build content that actually helps

AI Overviews and AI Mode are changing how people arrive on websites.Less through exploration, more through pre-structured answers. Many discussions focus on how to “appear” in these interfaces. That question matters - but it is ot the most important one.

The more useful question is:

Which content genuinely helps people once they arrive with an answer already in mind?

Because that is the situation we are dealing with now.

Start with questions, not keywords

When AI Overviews appear, it is rarely for vague or inspirational searches. They tend to surface where users need orientation or clarification.

Typical patterns are:

• What does something actually mean?

• What is the difference between two approaches?

• When is a solution suitable - and when is it not?

• What are the prerequisites, risks, or limitations?

  
  

These are not “traffic questions”. They are decision and understanding questions.

Google itself frames generative answers this way: as support where users need clarity, not as a replacement for every search. If a question does not influence understanding or decisions, it is rarely a good place to start.

A very reliable signal: what you keep explaining internally

One of the most practical ways to identify relevant questions has nothing to do with SEO tools.

Look at what:

• sales teams explain again and again

• onboarding processes have to clarify

• support tickets revolve around

• demos and presentations repeatedly address

  
  

These questions are not theoretical.They point to real gaps in understanding.

From an information architecture perspective, this is well understood: content works best when it solves a specific task, not when it tries to be comprehensive. If something constantly needs explanation internally, chances are high that externally there is no clear, focused page addressing it.

Why trying to cover everything does not help

In the context of AI Overviews, the instinct to “cover all questions” is understandable - and counterproductive.

Helpful content is not defined by completeness, but by clear responsibility.

Good content answers:

• this question

• for this context

• with clear boundaries

That also means deciding deliberately which questions you do not answer.

Common examples:

• topics where your solution is not suitable

• very early, theoretical questions without decision relevance

• areas outside your actual expertise

This kind of focus is not a weakness. It is a core principle of content design and is widely used in high-quality public-sector content, such as GOV.UK (https://www.gov.uk/guidance/content-design).

The real lever: the right destination page

Being mentioned somewhere is not enough.What matters is where people are sent.

Many websites have:

• good blog articles

• extensive guides

• broad overview pages

What they often lack are pages that:

• answer one specific question

• without detours

• without mixing multiple topics

Users coming from AI Overviews do not arrive at the beginning of a journey.They arrive already informed.

If they land on a page that:

• starts with long introductions

• slowly builds context

• or tries to cover too much at once

they quickly lose orientation.

Research on reading behaviour and zero-click environments consistently shows that users scan faster and leave sooner when the page does not immediately match their intent (https://www.nngroup.com/articles/how-users-read-on-the-web/ https://sparktoro.com/blog/less-than-half-of-google-searches-now-result-in-a-click/).

Helpful pages in this context are often simple - not impressive

Pages that work well for this moment tend to be:

• explicit about the question they answer

• structured and easy to scan

• clear about prerequisites and limitations

• free from unnecessary narrative

This is not “AI optimisation”.It is respecting the user’s situation.

“I can’t find us when I Google it” is not a reliable signal

This objection comes up frequently, especially internally.

It is understandable - and misleading.

AI Overviews:

• do not appear for every query

• depend on user context, language, and location

• vary across markets and rollout stages

Google documents these differences openly (https://developers.google.com/search/docs/specialty/international https://developers.google.com/search/docs/advanced/crawling/localized-versions).

A single manual check is not a valid measure of relevance or suitability.

A more useful question is:

If this question triggers an AI answer, do we actually have a page that helps afterwards?

If not, visibility is not the real issue.

International context adds another layer

Internationally, the picture becomes more complex:

• different languages

• different legal frameworks

• different user expectations

• different AI behaviour by region

As a result:

• the same question may receive different answers

• different sources may be cited

• visibility may exist in one market and not in another

This is not inconsistency - it reflects localisation and risk considerations

That is why isolated tests in one language or country say very little.

Technical foundations still matter - quietly

None of this works if pages are technically unreliable.

At a minimum:

• pages must be indexable

• canonicals must be clear

• content must be present in the initial HTML

• internal linking must point clearly to the reference page

  
  

These are not new rules. They are long-standing fundamentals (https://developers.google.com/search/docs/crawling-indexing/consolidate-duplicate-urls).

What has changed is how quickly shortcomings become visible when users arrive with a very specific expectation.

What this comes down to

The goal is not:

• maximum visibility

• or appearing everywhere

The goal is:

• the right questions

• the right pages

• the right moment

AI Overviews do not redefine what good content is. They make the difference between helpful and unfocused content more visible.

If your pages:

• answer real questions

• are clearly scoped

• and help users move forward after the answer

you are working in the right direction - regardless of how interfaces evolve.

A simple prioritisation framework for deciding which questions to address first

Once you accept that you cannot - and should not - answer everything, the remaining question is a practical one:

Which questions are actually worth addressing first?

The goal of prioritisation here is not maximum coverage, but maximum usefulness.

A helpful way to approach this is to evaluate questions along three simple dimensions.

1. Impact on understanding or decisions

Start by asking:

• Does this question regularly block understanding?

• Does it influence whether someone can evaluate a solution properly?

• Does misunderstanding it lead to wrong assumptions or poor decisions?

Questions with high impact are typically:

• definitional (“What does this actually mean?”)

• comparative (“What’s the difference between A and B?”)

• suitability-related (“Is this appropriate in this context?”)

If answering a question clearly would prevent confusion or misalignment, it belongs high on the list.

2. Frequency in real conversations

Next, look at how often the question comes up in reality, not in tools.

Good indicators are:

• repeated explanations in sales calls

• recurring onboarding friction

• support tickets that point to the same misunderstanding

• questions that stakeholders ask again and again

A question does not need to be searched thousands of times to matter.If it comes up consistently in real conversations, it is already relevant.

3. Risk of getting it wrong

Finally, consider the consequences of an unclear or incorrect answer.

Some questions are low-risk:

• the user can experiment

• the decision is reversible

• misunderstandings are harmless

Others are not.

High-risk questions often relate to:

• security

• compliance

• legal or regulatory constraints

• technical prerequisites

These are exactly the areas where clarity helps most - and where vague or generic content causes the most damage.

How to use this framework in practice

You do not need complex scoring models.

A simple exercise is often enough:

• list the questions you are considering

• assess each one against impact, frequency, and risk

• start with the questions that score high on at least two of the three

This usually results in a short, manageable list - not dozens of topics.

And that is the point.

A final thought

The purpose of this framework is not optimisation for AI interfaces.

It is a way to make sure that:

• you invest effort where it actually helps

• users arrive on pages that respect their context

• and clarity comes before coverage

AI Overviews and AI Mode simply make this more visible.

If you focus on the questions that matter most - and answer them clearly, honestly, and with proper boundaries - you are doing the right work, regardless of how discovery continues to evolve.