Designing for the machine
For years, Digital Marketing has been built around traditional web journeys which involve someone opening a web browser, typing in a search query, scrolling through the sponsored and organic links, clicking into a website, and interacting with it. The journey went something like: search, click, browse. Entire teams exist to optimise every step of this process.
That model still dominates, but AI-led discovery is beginning to change how people find and evaluate information online. Increasingly, users are turning to AI tools to do the legwork for them, outsourcing research, filtering and comparison before ever visiting a website themselves.
This creates a new challenge for marketers. Brands are no longer competing only to rank highly in search engines or persuade human visitors. They are also beginning to compete for interpretation. Can an AI system easily understand what your business does, how your offering compares, and whether your information is trustworthy enough to surface confidently?
That has implications far beyond SEO rankings. It affects visibility, attribution, lead generation and ultimately pipeline, especially if more research and decision-making starts happening inside AI tools rather than on websites directly.
Why AI readability matters
Most websites today are still primarily designed for humans. They prioritise visual presentation, brand storytelling and user journeys, which remain important, but AI systems do not experience websites in the same way people do.
LLMs don’t “browse” websites like we do. They extract, interpret and compare information. That means websites with unclear structures, buried information or tougher navigation become harder for AI models to understand confidently.
This is where concepts like WebMCP start to become more relevant. Not as a defined industry standard, but as a broader movement toward making websites easier for the machine to interpret and interact with.
In practice, this would involve structuring information more clearly, defining actions explicitly and making important content easier for machines to access and interpret.
Content would be easier to identify instead of buried within long pages. Product or service information would be structured more consistently. APIs and structured CMS fields would help expose information more clearly, instead of forcing AI systems to gather meaning from messy page layouts or vague copy.
What is WebMCP?
WebMCP is an experimental browser API proposal designed to help websites expose structured tools and actions directly to AI agents.
Rather than forcing AI systems to interpret buttons, forms and page elements through guesswork, it creates a machine-readable way for websites to describe actions, expose functionality and accept structured inputs more clearly.
How AI is changing discovery
Traditional search has always been built around abundance. Users are presented with pages of links and explored multiple options before deciding where to click. AI-led discoveries work very differently.
Instead of exploration, AI systems focus on narrowing down options quickly. Rather than presenting 10 links, they’ll summarise, compare and recommend a much smaller pool of choices, or even just a single answer.
That means being visible online may increasingly depend on whether LLMs can confidently crawl through your site and easily interpret and compare your offering.
Clear signals become more important:
Specific capabilities
Transparent pricing
Clear positioning
Structured service information
Demonstrable credibility
Consistent terminology
Without this, they become less likely to surface your content. For a deeper look at how this change is already affecting search behaviour, take a look at our SEO Team Leader Kelsie Tune’s analysis.
Why your website may no longer tell the full story
One of the more uncomfortable implications of AI-led discovery is that website traffic may become a weaker indicator of marketing performance.
Users may increasingly gather information, compare providers and even make decisions without ever visiting a website directly. That does not mean your website did not influence the decision. It simply means the interaction happened through an AI interpretation layer rather than a direct visit.
This creates growing importance around how websites are structured behind the scenes, not just how they look visually.
For marketers, this means thinking beyond traditional UX alone. The question is no longer whether users navigate the site easily. It may also become: can AI systems interpret the site easily? Can they extract useful information confidently? Can they understand what differentiates the business?
What marketers should focus on
The practical implications are less dramatic than they sound. This is not about rebuilding the internet or designing entirely machine-first websites.
It is more about reducing ambiguity.
That could include:
Clearer site structures
Better organised service pages
More consistent terminology
Structured CMS content
Schema markup
Clearly defined product and service information
Reducing vague or overly decorative copy where clarity matters
The goal is not to remove creativity or storytelling from websites. It is to ensure that important commercial information can also be interpreted accurately by AI systems.
What this means for the future of SEO
The future of SEO may become less about simply driving clicks and more about helping AI systems confidently interpret, compare and surface your business.
Teams are already seeing the rise of zero-click journeys and AI-generated search responses. As this behaviour grows, marketers may need to think more carefully about how AI systems experience their websites alongside human users.
Concepts like WebMCP point toward one possible direction of travel: a web that becomes easier for machines to interpret, navigate and act upon.
Whether or not that specific terminology becomes widely adopted, the underlying shift is already starting to happen.
From visibility to execution
Concepts like WebMCP offer a useful way of thinking about how marketers can make their content and services easier for AI systems to interpret, surface and act upon.
In practice, this can include disciplined machine-readable content, schema markup, and exposing real actions through APIs or endpoints.
Brands that define their offerings clearly, explain how they work, and set obvious boundaries are easier for AI systems to trust and use. Preparing for an AI-first web means deliberately building marketing and systems that are ready to be chosen and executed on.
Talk to us about preparing your marketing for an AI-first web
We offer a complimentary consultation to help you understand how AI systems may currently interpret your website and marketing stack.
During the session, we’ll explore where AI may struggle to understand your offering, where important information becomes difficult to extract, and where structure or messaging may create ambiguity.
We’ll also highlight practical opportunities to make your website easier for both humans and AI systems to navigate and interpret.
Get in contact with us to book a session with one of our AI search specialists.