How to Prioritize AI Search Improvements for an Existing Website Without Rebuilding It

“If we want our website to work well with AI search, shouldn’t we rebuild it from scratch?”

This is a question we hear more and more often.

As AI-powered search tools such as ChatGPT, Perplexity, Google AI Overviews, and AI Mode become more widely used, concerns about whether an existing website is “good enough” for AI search are increasingly leading companies to consider a full website redesign.

But here is the conclusion: in many cases, you do not need to rebuild your website.

Instead, the first step should be to identify the improvements that can have an impact at relatively low cost, and prioritize them based on their expected impact and ease of implementation.

In this article, we explain why rebuilding should be considered a last resort, and how to determine which improvements to tackle first, with a practical framework you can apply to your own website.

Rebuilding Should Be a Last Resor

First, let’s clarify one important point: whether a website is likely to be cited by AI search is not determined by how modern its visual design looks. What matters is the structure of the information, the credibility of the content, and whether the site is accessible and understandable to AI systems.

In other words, you can start optimizing your website for AI search without changing its current design or CMS.

For example, you can reorganize headings, restructure content so that the conclusion comes first, remove unintended blocks on AI crawlers, and clearly identify the authors of your content—all while keeping the existing website structure largely intact.

So why is rebuilding so often considered the first option?

One reason is that, from a project management perspective, “redoing everything” can be easier to explain internally and easier to justify when seeking approval. Another is that AI has lowered the baseline cost of content and website production, making it tempting to think, “If it’s cheaper to build now, why not rebuild?”

However, as we have discussed in our series on [Website Renewal in the AI Era (Part 1)] and [Part 2], the first question when considering a website renewal should not be whether to build a website. It should be “What business challenge are we trying to solve?”

Rather than rebuilding everything to perfection, the key is to focus on the areas that need improvement in order to address the business challenge at hand.

Of course, this does not mean that rebuilding is never the right choice. A rebuild may be appropriate in cases such as:

• The CMS or template imposes technical limitations that prevent the content from being output in an HTML-readable format.
• The information architecture itself is fundamentally broken and cannot be fixed through page-level improvements.
• The underlying business challenge involves redefining the target audience or value proposition, meaning that the purpose of the website itself needs to be reconsidered.

If none of these apply—if the basic structure is working, but there is room to improve how information is presented or the technical foundation—consider using the approach described in this article: identify the bottlenecks and prioritize improvements accordingly.

By addressing the areas that are holding the site back, rather than changing everything at once, you can potentially generate meaningful returns with a relatively small investment.

Start by Identifying the Current Issues

The first step in making an existing website more accessible to AI search, or optimizing it for LLMO, is to accurately identify what may be preventing AI systems from understanding the site.

When budgets are limited, making changes based on assumptions is not an efficient approach. Start by auditing the current state of your website and making its issues visible.

There are three key areas to examine.

The first two focus on what information you provide and how you present it. The third looks at whether that information can actually be accessed and understood by AI systems.

• Information Architecture (IA): Is the content structured so that AI systems can easily understand and summarize it? Does it use a logical heading structure (H1–H3), with key conclusions presented upfront?
• Credibility (Clear Identification of Sources and Expertise): Are the author and reviewer’s expertise and track record clearly stated? Does the content include original insights, first-party information, or examples based on the company’s own experience?
• Technical Accessibility (Can AI Systems Actually Read It?): Are AI crawlers being unintentionally blocked by robots.txt or bot protection? Does the content rely on JavaScript rendering rather than being available in the HTML? Is structured data, such as Organization or Article in JSON-LD, implemented to communicate the source and type of information to machines?

Note: Since AI providers do not publicly disclose their content retrieval and citation criteria, these are practical criteria we use in our work. They should not be interpreted as the evaluation criteria used by AI systems themselves.

Looking at these three areas together can reveal specific bottlenecks unique to your website. For example, you may find that the content itself is strong, but a technical issue is preventing AI crawlers from accessing it. Or you may find that the technical foundation is sound, but the content lacks sufficient original information.

How to Prioritize Improvements: Impact × Ease of Implementation

One practical way to prioritize improvements is to assess them using two dimensions: impact and ease of implementation.

• Vertical axis: Impact — How much the initiative is expected to contribute to the desired change in user or business outcomes.
• Horizontal axis: Ease of implementation — How much effort, cost, and internal coordination are required.

Plotting initiatives across these two axes creates four quadrants, making it easier to determine what to tackle first.

Note: Because AI providers do not disclose their criteria for determining impact, these categories represent our current assessment rather than an established ranking.

The initiatives listed in ① below are prerequisites for making content accessible to AI systems, regardless of whether AI providers publicly identify them as ranking or citation factors.

From ② onward, however, the relative size of the impact is based on our current assessment and should not be interpreted as a definitive ranking.

① High Impact × High Ease of Implementation: Highest Priority

These are initiatives that can have a significant impact and can be implemented relatively quickly.

Start by checking whether AI crawlers are being unintentionally blocked by robots.txt or bot protection. Keep in mind that AI-related crawlers may be separated according to their purposes, so permissions should be configured based on the intended use. At the same time, check for any noindex settings that may have been left in place on the production site.

Next, make sure that pages whose main content depends on JavaScript rendering are also readable as HTML. Google’s search crawlers execute JavaScript, but crawlers used by services such as ChatGPT and Perplexity are generally said to rely primarily on the HTML they initially receive.

You should also replace vague headings with wording that clearly communicates what the section is about.

Then, make it possible for AI systems to identify who is publishing the information. Specifically, implement Organization structured data and use sameAs to declare external URLs that point to the company, such as its official social media accounts.

These measures establish the basic conditions for your content to be accessible to AI systems. This is where you should start building your foundation.

② High Impact × Low Ease of Implementation: Plan and Implement Systematically

These initiatives can have a significant impact but require more time, resources, or organizational coordination.

Examples include creating original, first-party content and rewriting key pages so that their structure follows a conclusion-first approach.

These initiatives are important, but rather than trying to complete them all at once, they should be incorporated into a realistic schedule and implemented systematically.

③ Low Impact × High Ease of Implementation: Do When Resources Allow

These are relatively low-cost initiatives that are unlikely to be decisive on their own.

Examples include creating an llms.txt file containing information intended to help AI systems understand a website, and implementing BreadcrumbList structured data.

BreadcrumbList is primarily intended to support the display of breadcrumb navigation in Google Search (currently available only on desktop), so its purpose is different from AI search optimization. While it can have value as an SEO measure, it should not be treated as a direct AI search optimization measure.

Similarly, AI providers have not publicly stated that they use llms.txt as a source for retrieval.

Both can be relatively simple to implement, particularly when incorporated into a website template, so there may be value in implementing them as a precaution. However, they should not take priority over the Organization structured data described in ①, which helps clearly identify the source of information.

④ Low Impact × Low Ease of Implementation: Deprioritize or Skip

These are initiatives that are unlikely to have much impact specifically in the context of AI search, while requiring significant time or effort.

A typical example is producing large numbers of thin pages simply to increase the chances of being picked up by AI systems.

Increasing the number of articles may make the site look more substantial, but pages without original information, concrete data, or other useful evidence give AI systems little reason to cite them. They also require production resources, while potentially reducing the overall information density of the site.

Rather than prioritizing this type of activity, it is more practical to focus first on ① and ②.

The important thing is not to make these decisions based on intuition alone.

Evaluate impact in relation to the framework we defined in [Website Renewal in the AI Era (Part 1)] and [Part 2]: Who are you trying to move, from what state to what state?

Estimate ease of implementation by considering who will actually carry out the work—whether it will be handled internally or by an external partner—and what resources and coordination will be required.

When you can articulate both dimensions, you can also explain to management why a particular initiative should be addressed first.

Build It Into an Ongoing Process

Prioritizing and implementing improvements is not the end of the process.

AI search evaluation systems are not publicly disclosed by the providers and continue to evolve. Rather than treating optimization as a one-time project, it is essential to establish an ongoing cycle of “improve → measure → decide what to do next.”

A practical process could look like this:

1. Diagnosis: Measure your visibility and citation status in AI search, and score the site across the three areas described above.
2. Prioritization: Use the impact × ease-of-implementation framework to determine which initiatives to address next.
3. Implementation: Start with ①, improving crawler accessibility and optimizing content UX and information architecture (IA).
4. Monitoring: Check AI crawler access logs to confirm that your content is actually accessible. Then track changes in AI search visibility (such as citation rates) and branded search volume as a proxy indicator of top-of-mind awareness. Evaluate the results and feed them into the next cycle.

Only when this process becomes an established part of the organization’s regular operations can you build a resilient foundation that can continue to adapt as the AI search landscape changes.

Start with small improvements, measure the results carefully, and incorporate the cycle into your existing website operations.

Conclusion

You do not necessarily need to spend a large budget and several months completely rebuilding your website to prepare it for AI search.

Instead, make use of the existing website as an asset, prioritize improvements based on impact and ease of implementation, and address the areas that matter most first.

This is a practical way to improve cost efficiency while making targeted improvements to the site.

We will cover the technical foundation in more detail—including how to implement structured data—in a separate article.

Note: The statistics and views presented in this article are based on publicly available information confirmed as of July 2026. Since the specifications and search algorithms of generative AI providers may change, please check the latest information before implementing any measures.

Want to Know Where to Start on Your Website?

You may understand the overall approach but still find it difficult to determine where the bottlenecks are on your own website.

Neuromagic offers a free LLMO Website Quick Diagnosis, which assesses websites from an LLMO perspective and identifies the areas that should be prioritized for improvement in the era of AI search.

The diagnosis scores your website out of 100 based on criteria including those discussed in this article, and provides improvement recommendations with priorities.

Why not take the first step by finding out where your website stands today—and whether it is in a state that AI systems can easily understand?