Hardly any other term is currently being promoted as aggressively in digital marketing as ‘AI readiness’. Websites are to be optimised for artificial intelligence, content prepared for large language models, and brands made visible to generative search systems. It sounds like an entirely new discipline. With new rules. And, not infrequently, as a good reason to completely rebuild the existing website.
A glance at current offers shows just how much this issue is being exaggerated. Under headlines such as ‘Your website is invisible to ChatGPT’ or ‘84 per cent of all businesses are invisible to AI’, agencies and tool providers are advertising free quick audits. Even websites with a clean ranking and a solid technical foundation are portrayed as “optimised for the past”. The sense of urgency is therefore not the result of the audit, but a prerequisite for it.
This pattern is nothing new. It follows the fear-mongering rhetoric of previous SEO cycles: in 2015, Google announced that it would make mobile-friendliness a ranking factor. The press declared it ‘Mobilegeddon’ and predicted dramatic losses in visibility. In the end, the update was real and significant, but its impact was considerably more moderate than announced: anyone who implemented a responsive layout was fine.
The plain truth is: In many cases, that isn't necessary.
After all, a website that has been developed to a high technical standard, is high-performing, accessible, well-structured and search-engine-friendly already possesses many of the characteristics that AI systems also require. Artificial intelligence does not favour any special ‘magic’ formats. To begin with, it copes just as well with the same fundamentals as traditional search engines and users: clear content, a logical structure, fast loading times and a reliable technical foundation.
AI readiness therefore largely means: a high-quality website plus a few new areas for optimisation. It does not mean: starting from scratch.
What is marketed today as “AI readiness” is often already standard practice
Many recommendations relating to AEO (Answer Engine Optimisation), GEO (Generative Engine Optimisation), LLMO (Large Language Model Optimisation) or ‘AI Search Optimisation’ are by no means new. They have been an integral part of a professional technical SEO and web development strategy for years.
These include, amongst others:
None of this is a response to ChatGPT and the like. These are tried-and-tested fundamentals of good websites.
Even traditional search engines must be able to understand, categorise and reliably process content. This is precisely why the requirements of search engine crawlers and AI-based systems overlap in many areas: both benefit from machine-readable, unambiguous, structured and up-to-date information.
So anyone who has already invested in a technically advanced website isn’t starting from scratch. On the contrary: much of the necessary groundwork is often already in place.
What has actually been added
That does not mean that nothing has changed. With the spread of generative search systems, new variables have come into play. They expand the requirements for websites, but do not automatically replace the existing standards.
1. New crawlers and access rules
In addition to traditional search engine bots, there are now crawlers from various AI and search providers. Whether and how these are permitted to access content can be controlled via technical settings such as the robots.txt file.
This is a new issue because the relevant bots simply did not exist in the past. At the same time, it is usually a specific configuration task – not a reason to completely rebuild the website.
2. Citation impact rather than rankings alone
Traditional SEO often aims to appear as high as possible in search results and thereby attract clicks. With AI-generated answers, there is an additional layer of visibility: content can be referenced, summarised or cited as a source without users having to click directly on the website.
This changes the requirements for content to some extent. Answers should be clear, precise and understandable on their own. Key points must be easy to grasp. Long, well-written texts remain valuable – but they should not consist solely of continuous text from which key information is difficult to extract.
3. The zero-click reality
If an AI answers a question directly within the response, this does not necessarily result in a visit to the website. Visibility and traffic are therefore no longer automatically one and the same. This is, above all, a strategic and economic shift. Businesses need to distinguish more carefully: are they looking for reach, brand awareness, trust, concrete leads or direct website visits?
This also raises the question of how success is actually measured. Clicks and rankings do not tell the whole story, as they only reflect the part of visibility that leads to a page view.
It is useful to include additional key figures:
- Brand mentions in AI responses
- Enquiries with no discernible click path
- Trends in direct traffic and brand searches
Anyone who focuses solely on traditional traffic may underestimate just how visible their own brand actually is. This means they risk scrapping effective measures simply because their impact is no longer reflected in the existing measurement framework. Technical implementation alone does not answer this question. It simply creates the conditions necessary for a brand to be correctly understood and taken into account in the first place.
4. Content for different types of access
In addition to search engines and AI crawlers, internal search systems, knowledge databases and retrieval or RAG applications are also becoming increasingly important. For this to work, content must be discoverable, unambiguous and well-structured.
Here, too, the same applies: a centralised, well-maintained and systematically organised content base is not a new idea. What is new, above all, is the number of systems that will access and process this content in future.
Why so many fundamentals fall by the wayside during a relaunch
If many websites today do not appear sufficiently ‘AI-ready’, this is often not because companies have missed the boat when it comes to the AI era. In many cases, key quality features were not consistently implemented during the last relaunch due to time, budget or expertise constraints.
Typical examples include:
- a generic template with little semantic depth,
- client-side content that is difficult for crawlers to access,
- an unaltered content migration without editorial revision,
- missing or incorrectly structured data,
- Performance optimisation, which was postponed until after the launch,
- unclear responsibilities for content, metadata and data quality,
- an information architecture that has evolved over time and is difficult to understand.
These are real problems. But they did not arise with the advent of generative AI. The current debate on AI merely highlights the technical and structural deficits that have built up over the years.
What is referred to today as “AI readiness” is therefore often a retrospective technical SEO and quality audit of the existing website.
The tendency to build new structures is not always justified from a technical standpoint
A complete website relaunch may be advisable. For example, if the CMS is out of date, important content is not being displayed correctly, the architecture does not allow for further development, or the website has fundamental technical issues regardless of the AI aspect.
However, many of the measures currently offered under the heading of ‘AI readiness’ can be implemented retrospectively in a targeted manner:
To achieve this, we first need a thorough assessment of the current situation – not a knee-jerk ‘start from scratch’ approach.
Our conclusion: AI readiness is about continuous development, not a fresh start
A reputable consultancy should therefore distinguish between three questions:
- What foundations are already in place?
- What technical or editorial shortcomings exist, regardless of AI?
- What additional measures do generative search systems actually make sense to implement?
Only then can a decision be made as to whether selective improvements will suffice, whether a partial overhaul is necessary, or whether a relaunch can be justified on technical grounds. This is no less ambitious. It is simply more honest and makes more economic sense.
Artificial intelligence is changing the way people find information and the way content is selected, summarised and presented. Businesses should take this development seriously. However, this does not necessarily mean they have to completely rebuild their entire website.
The most important foundation remains a website that is technically sound, fast, easy to understand, well-structured and up to date. Added to this are new requirements regarding bot access, citability, content structure and visibility in generative response systems.
AI readiness is therefore not a radical departure from good web development. It is the logical evolution of proven standards—supplemented by a few new requirements.
Anyone who communicates this honestly to their customers does not sell any less. They build trust. And they ensure that investment is directed where action is actually needed.
