Your site has more audiences now: Understanding MX and the machine-readable web

A new chapter

For the last 30 years, the deal was simple enough: let search engines crawl your content, and they will send you the traffic you want. However, the rise of LLMs and the influence of answer-focused experiences is stressing that agreement. That pact is still in place, but the journey is getting messier as search engines, platforms and models start to demand a bigger slice of the pie. More of the discovery, comparison, and summarising now happens before a person lands on your site, and much of it is being done by systems rather than people.

Cloudflare’s recent reporting points in the same direction, stating AI and search crawlers are consuming more web content and increasingly returning less referral traffic. That changes what your website is doing. It’s no longer just there for customers and other stakeholders. It is also being read, interpreted and represented by systems you don’t control, and that will make their own call on what your content means.

In practice, this means designing for two very different readers. A person wants reassurance, clarity and ease. A machine wants structure, consistency and explicit meaning.

Having said that, it’s important not to overstate the challenge or complicate the solution. Google has already said there are no extra technical requirements for appearing in AI Overviews or AI Mode, and no special AI markup you need to rush onto your site. 

The fundamentals of crawlability, indexability, internal linking, useful and unique content, and structured data all remain critical.

Those same fundamentals were always about helping systems understand your site. The difference now is what happens next. It’s no longer just about being indexed or cited. Machines are starting to act on that understanding, searching, filtering, comparing and, in some cases, moving closer to transactions on a user’s behalf.

The move towards MX isn’t a reason to abandon sound SEO, accessibility, or content design. If anything, it’s a reason to take them even more seriously, as the cost of getting them wrong is now even higher.

This is why MX should not be treated as a replacement for UX. Instead, it should be seen as its partner in crime, like UX’s Torvill to MX’s Dean. Human-centred design still matters because people still compare, doubt, trust and decide. But AI is now shaping more of the journey earlier on, reading, filtering and narrowing options before a person ever reaches your website. And, in some cases, they won’t reach it at all. A user's entire experience of your brand may be mediated through a machine. That changes the role your content plays. It’s no longer only about what a customer sees on screen; it’s also about how clearly your content, data and digital estate can be consumed, interpreted and represented by AI.

If an agent has to scrape a page and infer meaning from visual layout alone, you have already made the experience harder than it needs to be. A machine should not have to guess whether a number is a price, a review score, a delivery date or a discount. The same applies to product specs buried in images, vague labels, or key details trapped within components that look fine to humans but are difficult for machines to parse. This is where machine readability becomes commercially relevant. Structured content, clean markup, consistent labelling, schema, feeds and APIs all make it easier for systems to understand what your site is actually saying and where you fit in the digital authority rankings. 

What machine-readable really means

So, what does machine-readable mean in practice? Mainly, it means getting the fundamentals right. There aren't any new AI-specific rules; it’s still about good web standards. Google’s guidance confirms this – SEO basics still matter. MDN, Mozilla’s developer team, and the W3C, the organization that sets web standards, make the same point: semantic HTML and clear, readable data help systems understand a page.

Accessibility teams have long advocated for clearer structure, better semantics, and more reliable labeling. Their goal was to make pages understandable to systems that don’t see or navigate like fully sighted users with a mouse and patience. Now AI reads the same web, giving this discipline new commercial importance.

First impact

This change will affect commerce teams before most others, because product details contain info that must be read precisely. Price, availability, and delivery times are all crucial. Returns info, variants, dimensions, review counts, promo terms, shipping thresholds, and subscriptions also matter. Errors weaken your chances to capitalize and let competitors gain ground.

Humans tolerate messiness. We can scan, compare, cross-check, contextualize, and make assumptions. Machines are more literal. Given a well-made product page, they identify essentials well. But if they must infer info from layout, inconsistent naming, tabs, accordions, or text in images, errors increase.

Multi-step checkouts exemplify this. When buying online, we understand delivery might add later or thresholds apply. Machines often don’t. They take the first price as total, since other info is deeper or conditional.

Google’s product docs clarify: Structured product markup lets systems recognize price, availability, and key details in standard form. Shipping and return policy markups have separate docs too – the goal is clarity and consistency.

This affects search, shopping, AI summaries, and agent workflows. Can systems distinguish sale vs. regular price? Know which sizes are in stock? Correctly get delivery promises, or do they pull outdated terms from old policies you thought removed? These seem minor until they affect clicks, comparisons, or purchases.

There’s a subtler issue. Machines don’t always fail dramatically. Sometimes they err slightly, which can be worse since it’s harder to detect. No alert shows a misread delivery threshold; you just see its effect. Sometimes it’s not missing info but distorted info.

When you lose control

Losing control can greatly impact your brand. When prospects see your brand inside Google’s AI overview or large language model interface, they’re outside your brand-controlled environment. They don’t see your carefully structured page as intended, with your chosen emphasis. Instead, they see a version of your business rebuilt from whatever the model finds, interprets, and prioritizes.

This means key points can be compressed, reordered, or omitted. Supporting details may be elevated or removed, and critical context lost. What returns might be generally correct but differ from how you present your brand.

Here MX moves from a technical to a brand and commercial concern. If systems pull different versions of your business, you lose control over comparison, recommendation, and selection.

Ready for your closeup?

At this point, many SEO teams might try tweaking schema, feeds, or APIs. That’s understandable but not always the best first step. The core issue is often that the content itself isn’t properly structured.

Many sites, especially low-cost ones, are designed visually first, with content models patched on later. I've seen many cases where a single “body copy” editor field in the CMS holds hacked-in HTML for product facts, selling points, disclaimers, links, FAQs, and more. Pages then appear fine visually. This works for humans but can seem like a confusing mess to machines.

The solution is proper separation. Product name, descriptions, specs, features, price and offer terms, shipping, reviews, author, category, and FAQs should each be distinct in the backend. When clear, managing, reusing, and presenting across channels becomes easier.

If you think “we don’t sell sweaters, trips, or gadgets,” you’re wrong. This applies beyond commerce. Service businesses’ proposition pages, case studies, and thought leadership also need structure and consistency.

In fact, these can be worse. Brands often use long-form content burying key points in paragraphs instead of clearly defining them backend. This may read well but makes it harder for machines to discern what’s important and the page’s meaning.

Another risk is inconsistency. If the same claim varies in page copy, metadata, and downloadable PDFs, humans might miss it, but machines often see this negatively and seek more reliable sources. This affects reliability as well as visibility. If a system encounters your brand multiple places, does it get the same understanding each time? Often, the honest answer is no.

Where APIs Fit In

APIs are most important when the information is dynamic or critical. Things like stock levels, delivery estimates, booking availability, or pricing logic can cause more harm if slightly wrong than if missing altogether. Direct, structured access removes the need for interpretation and reduces the risk of incorrect assumptions.

Promoting consistency and structure isn’t controversial. Most systems prefer explicit information when available; it’s just that humans handle ambiguity better, so we get lazy and let things slip, knowing users can usually figure it out. Machines, however, reward clarity and penalize any potential confusion.

Still, most businesses don’t need to overhaul their site into a vast API strategy immediately. A smarter first step is to identify which information truly matters, where those facts reside, how often they’re duplicated, and where meaning is most likely lost. That tends to be more productive than instantly adopting every new AI framework circulating.

Heavyweight UX

None of what I’ve discussed here changes the fact that people still make the final decision in most journeys. The numbers may shift as AI improves at understanding audience goals, but to some extent, people still seek brand trust, reassurance, clarity, speed, and confidence. They respond to good design and messaging, and they’ll abandon poor experiences. Even if AI influences more of the shortlist, humans ultimately decide whether they trust the brand, feel the offer is right, and want to spend their money.

The challenge is making the experience cohesive from both angles. A person should arrive and find something clear, persuasive, and easy to use. A machine should identify the core meaning without guessing. 

IDHL and MTM clients have an advantage here. Not because our designers and developers foresaw the future, but because we’ve always focused on fundamentals: clean structure, sensible content models, consistent data, and good publishing practices. The consequences of neglecting this approach are now clearer than ever.

A Sensible Audit

When reviewing a site from this perspective, we begin with simple questions.

• Can a machine understand this site’s purpose within seconds?
• Are key facts explicit, or implied by layout?
• Is the information consistent across page text, metadata, schema, feeds, assets, and channels?
• Would the page still make sense if all styling were removed?
• Are important details hidden inside images, tabs, accordions, or hard-to-parse components?
• Could an assistant accurately summarize the page without filling in many blanks?

That last question matters because every time a system fills gaps, there’s room for error. Sometimes it’s harmless. Sometimes it alters what’s shown, recommended, or chosen. We’ve all heard about AI hallucinating or making things up, so if info is commercially important, guessing isn’t a reliable strategy.

Why Performance Teams Should Care

If it’s not clear why this matters, this article missed its mark. To recap, MX is already impacting performance marketing, organic search, CRO, content, and measurement across industries, audiences, and regions.

The old model was simple: a person searched, clicked, landed, and hopefully converted. The new model is less linear but still straightforward. Discovery may happen via AI-generated answers, comparisons before visits, or recommendations shaped by external product data. Sometimes the click doesn’t even happen.

‘Crawl-to-refer’ data points in the same direction. AI and search platforms consume vast content volumes, often with a notable gap between content crawled and traffic returned. The exact numbers vary by platform and time, but the overall pattern is clear enough to take seriously. 

For performance teams, the risk is simple and manageable. If you see your site purely as a destination for human visits, you’re missing a growing segment and increasing risk. None of this work is exotic or beyond typical digital team concerns, so don’t be daunted by the task. 

At the end of the day, MX is here, whether people use the term or not. The better question is whether your site makes things easy for machines or leaves them guessing. If it’s the latter, you could face commercial troubles quickly.