AI Visibility is becoming the next digital benchmark

The way customers are now discovering brands or starting their buying journeys has created a significant new challenge for organisations. Most already know how they perform in traditional search and have been optimising for this experience for years. However, far fewer know what an AI assistant or LLM is saying about them, which competitor it recommends or which sources inform its response.

So, I was excited to watch Contentstack’s announcement and demo of the latest tool in the Contentstack Agentic Experience Platform (AxP): Canoe. It’s interesting because it offers a view into this previously hidden part of the customer journey. And while it’s integrated into Contentstack platform, it’s also available as a standalone product.

More importantly, it points towards a wider market shift where AI visibility is becoming measurable, providing the data for AI based recommendations to improve visibility in Agentic Answer Engines like ChatGPT and similar. Like its contemporaries, Contentstack Canoe reveals how AI search engines describe a brand, allows comparison with up to ten competitors, and produces a ranked set of suggested improvements.

Measurement, however, is still only the beginning. Knowing how AI sees a brand is useful but it’s essential to know whether an organisation’s content and ways of working are ready for customers and AI agents to interact with it in ways that resonates with both and captures attention.

Why AI visibility matters

For most of the digital era, visibility has been relatively straightforward to measure. We've tracked rankings, traffic, engagement and conversion to understand how easily customers can find an organisation and what happens when they arrive. AI-mediated discovery changes that sequence. An AI system may research a market, compare alternatives and describe a company’s proposition before a customer reaches its website. It may draw on the company’s own content, but it may also use third-party publications, reviews, community discussions and competitor material.

This means a brand can no longer assume that its website will provide the first or most influential account of who it is. The initial impression may be constructed elsewhere, from sources the organisation does not own and through an interface it does not control.

AI visibility is the extent to which an organisation, product or service can be found, accurately understood, cited and recommended by AI systems.

While it’s related to search visibility, it’s not the same thing. Search engines have traditionally directed people towards sources. Generative systems, meanwhile, interpret information from multiple sources and use it to construct its answer.

This distinction matters. A business can rank well in conventional search but be poorly represented within an AI-generated response. Its name may appear, but with an inaccurate proposition, products may be misunderstood, expertise may be poorly attributed, or it could be absent from a relevant comparison altogether.

Generative Engine Optimisation (GEO), is the emerging discipline concerned with improving how an organisation and its information are found, interpreted and represented across AI-powered discovery experiences.

What does Contentstack Canoe do?

Contentstack Canoe is an AI visibility tool designed to show organisations how AI search engines describe them, which competitors are gaining visibility and what they need to improve.

According to Contentstack, the product starts with a brand’s website and identifies information about the business, its products, potential buyers and competitors. Users can review that information before the tool generates questions that buyers are likely to ask. The platform then analyses how the brand appears, benchmarks it against competitors and concludes with prioritised recommendations. Contentstack has made the tool available as a standalone product, including through a free plan open to businesses that do not use its wider platform.

This is useful because most organisations currently have very little systematic insight into their AI representation. Teams can test individual prompts manually, but isolated tests are difficult to compare, repeat or turn into a coherent improvement plan. The significance of Contentstack Canoe, therefore, isn’t that it provides another dashboard but that it helps make an invisible layer of customer discovery visible.

That has potential value for marketing, digital, commerce and technology leaders alike. But it is important to be precise about what an AI visibility tool can and cannot do. It can reveal an outcome, highlight a competitive difference and suggest an area for attention. It cannot, by itself, repair all the organisational and technical foundations producing that result.

Two categories are emerging in the agentic era

The market conversation around AI discovery currently brings together several different types of technology. Looking at them more closely, I think two distinct but connected categories are forming.

The first is AI Visibility Platforms. These platforms measure how AI systems represent an organisation. They help establish whether a brand appears in relevant answers, how its proposition is described, which competitors are recommended and what sources appear to influence the response.

Contentstack Canoe belongs in this category. Its role is to help an organisation see how it appears across AI-mediated discovery and identify where that representation needs attention. Its value lies in making visibility more systematic, comparable and actionable.

The second category is AI Commerce and Experience Enablement Platforms. These are concerned less with how AI talks about an organisation and more with whether an authorised AI agent can interact with it. Can the agent understand a product catalogue, retrieve reliable information, use approved commerce services, manage a basket or help a customer move towards checkout?

BigCommerce Storefront MCP illustrates this second category. It gives compatible AI applications defined commerce tools through the Model Context Protocol, including catalogue search, product and variant retrieval, cart management and checkout-link generation. Its primary role is not to measure visibility. It provides controlled access to commerce capabilities. This is of particular value to organisations using Contentstack in combination with an eCommerce solution such as BigCommerce. Contentstack can manage the structured content and experience layer while BigCommerce exposes the transactional capabilities an authorised AI agent needs. Together, they help bridge the gap between being understood by AI and enabling AI-assisted journeys to progress towards a purchase.

Shopify is approaching the same market shift through AI discovery and commerce enablement. Its GEO guidance describes how merchants can improve the discoverability of their brands and products across services including ChatGPT, Google AI Mode, Gemini and Perplexity. It positions that discoverability within a broader movement towards transactions and buying journeys taking place through AI channels.

The distinction is that AI Visibility Platforms measure how AI sees you while AI Commerce and Experience Enablement Platforms help AI interact with you.

Organisations need to understand both. Being visible without being ready to participate creates a gap between discovery and experience while enabling agentic interactions without understanding how the brand is represented makes it harder to know whether customers will discover those capabilities in the first place.

Why GEO is not simply another form of SEO

It’s tempting to treat GEO as the next iteration of search optimisation. And there is some overlap. Clear content structures, technical accessibility, relevance, authority and trusted external references remain important in both disciplines.

But GEO exposes a wider organisational challenge.

An AI system trying to understand a company needs more than a collection of optimised pages. It needs consistent information about the organisation, its products, expertise, evidence and relationships. If those signals are fragmented or contradictory, the resulting representation may be incomplete, inaccurate or less useful than the organisation intends.

This is why I see GEO readiness as an experience and architecture question, as well as a marketing one.

The strength of an organisation’s AI representation will depend on the quality of its content models, information architecture and product information. But it will also be influenced by whether data is connected, terminology is consistent and expertise is clearly attributed. It also depends on whether governance is in place to keep information accurate as products, propositions and markets change.

These are not new disciplines. They’ve always contributed to effective digital experiences. AI-mediated discovery is simply making their strategic value more visible.

Inviqa’s AI Visibility Framework reflects this principle. It examines accessibility, discoverability, authority and awareness, but places beneath them a foundation of content architecture, information architecture, product information, journey design, structured content, connected data and experience platforms.

The central point is that AI visibility is not created through isolated optimisation but is the outcome of how effectively an organisation manages its experience ecosystem.

What can an AI visibility assessment reveal?

A useful AI visibility assessment should begin by establishing how a brand is currently represented. Is the company included when buyers ask relevant category questions? Is its proposition described accurately? Are the right products, capabilities and proof points recognised? Which competitors appear more consistently, and how are they positioned differently?

Source analysis is equally important. If an AI response is influenced primarily by third-party sources, the organisation needs to understand whether those sources are current, authoritative and consistent with its intended position. If owned content is rarely referenced, that could point towards issues with accessibility, structure, authority or content coverage.

Competitor analysis can then move beyond a simple comparison of mentions. The useful question isn’t just whether competitors appear more frequently, but why their propositions are easier for AI systems to interpret and reuse.

A competitor may express its specialist expertise more clearly, provide more complete product information, answer customer questions more directly or possess stronger external authority signals. It may also have a more coherent body of content connecting market needs, services, evidence and outcomes.

Visibility tools can help expose those differences. Human analysis is still required to decide which differences are strategically important and what the organisation should change as a result.

What an AI visibility tool can't tell you

While a visibility score provides a useful baseline, it shouldn’t be mistaken for a complete readiness assessment.

A tool might identify weak product representation, for example, while the underlying cause sits in several places. Product information may be incomplete, content may be trapped in formats that machines struggle to interpret, taxonomies may vary between systems, important claims may lack supporting evidence, and internal teams may use different language to describe the same product or service.

A superficial recommendation to create more content won’t solve those issues. It’s more likely to add more inconsistency to an already fragmented information environment.

Which is why organisations should resist the temptation to chase an individual platform score. The objective isn’t to perform well in one diagnostic tool. It’s to make the organisation easier for customers and authorised machines to find, interpret and trust.

Achieving this requires decisions about content and data ownership, governance, publishing workflows, customer journeys and technology architecture. It will also require new ways of working across marketing, product, commerce, data and technology teams.

From visibility analysis to GEO readiness

To be GEO ready, I would encourage organisations to begin with a clear baseline rather than a large transformation programme.

Use AI visibility analysis to establish what AI systems currently say about your organisation, products and competitors. Then examine the sources and organisational foundations behind those answers.

That investigation should connect external representation with internal readiness. If products are described inaccurately, review the product information supplying the market. If competitors dominate a valuable topic, assess both their external authority and the quality of your own coverage. If brand language varies between answers, examine whether the organisation’s positioning is expressed consistently across owned content and external sources.

From there, prioritise a small number of material gaps. You may need to strengthen your product taxonomy, restructure an important body of content or clarify the relationship between several services. Or you may need to improve machine access, add appropriate structured information or create a clearer evidence base for an area of expertise.

The right intervention will depend on the problem. The principle is to move from observation to diagnosis before moving to optimisation.

This is more useful than trying to predict exactly which AI channel will dominate and models, interfaces and customer behaviours will continue to develop. Strong information, coherent architecture and effective governance will make your organisation better able to adapt as they do.

The Inviqa view

The launch of Contentstack Canoe doesn’t mean AI visibility has been solved, but organisations now have a better opportunity to see the problem, especially if they’re already invested Contentstack’s DXP. Over the coming months, I expect there to be tighter integration between Canoe and websites that are using Contentstack as the primary, underlying CMS.

For much of the last decade, the digital conversation has centred on the quality of the experience once a customer reaches a website or application. That experience remains essential. However, organisations must now also consider what happens before the customer arrives, particularly when an AI system is interpreting the market on the customer’s behalf.

This gives us two connected challenges.

The first is representation: can AI find, understand and accurately describe the organisation?

The second is participation: can authorised AI systems interact safely and usefully with its products, information and services?

AI Visibility Platforms can help measure the first while AI Commerce and Experience Enablement Platforms are beginning to support the second. Under both sit the same essential foundations: structured content, reliable product information, connected data, clear governance and adaptable digital architecture.

The organisations best positioned for the agentic era won’t be those that chase every new platform or optimise for every new prompt. They will be those that make themselves consistently understandable and genuinely useful, wherever the customer journey begins.