Most Companies Just Haven’t Noticed Yet
By James O’Connor, Managing Partner, AmplifyXM | Director, OC&CO
For years, companies assumed they largely controlled how their customer experience was explained.
Customers might browse a website, read support articles, ask a chatbot or contact a service team directly. Sometimes they’d watch YouTube videos, check Reddit threads, ask in Facebook groups. Across those channels, the organisation still largely understood and could interject into how its products, services and policies were explained.
That assumption is being eroded – and fast.
Increasingly, customers are not asking companies how their products and services work. They are asking AI assistants.
- “Can I cancel this subscription?”
- “How long does a refund take?”
- “Why is this onboarding taking so long”
- “Why was my bill higher this month?”
- “Is there a warranty for this product?”
The answers are now delivered instantly by tools like ChatGPT, Gemini, Copilot, or embedded AI assistants inside search engines and software platforms.
In many cases, those answers are helpful. But sometimes they are incomplete, inferred, or simply wrong.
The issue is not malicious AI. The real issue is that websites, help centres and support documentation were designed for humans to browse, not for AI systems to be able to interpret.
Generative systems read the web differently. They look for structured explanations, sequences of steps, and clear logic about what happens next. When that structure is missing, they infer answers from other fragments of information.
This creates a new and largely invisible CX challenge: Generative Engine Customer Experience.
A New CX Opportunity To Act On: AI-Mediated Customer Understanding
For years, customer experience leaders have focused on improving journeys inside their own channels: websites, apps, contact centres, and stores.
But today an increasing share of post-sale customer understanding happens before the customer ever interacts with the company. In many cases customers never reach the company at all.
Their questions are answered entirely by AI assistants, and the company doesn’t know it. They also don’t know if the question was resolved, or which customer asked.
And when customers do contact support, they often arrive convinced they already understand the answer, regardless of whether that explanation is correct or not.
These systems retrieve fragments of information from across the web and synthesise an answer. When explanations are clear and consistent, the results can be helpful. When they are not, the systems infer meaning from incomplete sources.
The result is a growing gap between how a company thinks its experience works and how customers are being told it works. And that gap matters.
Incorrect explanations at key moments of truth can quickly escalate frustration, drive avoidable support contacts, or cause customers to abandon a product or service altogether.
And that interpretation increasingly shapes how customers understand your business — and how easy they believe it is to do business with you.
In effect, AI assistants are now shaping customer expectations before the organisation even knows the customer needs help.
From SEO to GEO to GECX
For more than two decades, Search Engine Optimisation (SEO) has been an established discipline. Companies invest heavily in ensuring their products, services and content appear clearly in search results when customers are researching solutions.
As generative AI becomes embedded in search, a related concept has emerged: Generative Engine Optimisation (GEO). GEO focuses on how AI systems interpret information about products and companies, helping customers understand what products do and how to choose between them.
But most of this SEO and GEO work focuses on pre-purchase discovery and marketing.
Very little attention has been paid to what happens after the sale. Yet this is where some of the most important customer questions arise. These are customer experience and customer service questions, not marketing questions. Getting these questions wrong impacts brand reputation and customer lifetime value – in the same way poor CX does.
And today many of these key customer questions, when they are in their time of need, are increasingly answered by AI assistants. This creates a problem.
But very few organisations are yet thinking about how generative engines accurately interpret and explain the customer experience after someone becomes a customer.
That is the emerging focus of Generative Engine Customer Experience (GECX).
- SEO helped companies influence what customers discovered.
- GEO is helping shape how AI explains products before purchase.
- GECXO focuses on something different — ensuring AI systems correctly explain the experience after customers become customers.
Why Traditional CX Approaches Don’t Solve This
Most CX teams already have tools designed to improve customer journeys.
Journey mapping helps organisations identify where friction occurs.
Customer analytics highlight contact drivers and pain points.
Knowledge bases and FAQs provide information to customers.
These are all useful. But they were not designed for a world where AI systems are now interpreting experience explanations.
Journey maps identify where problems occur, but don’t define the clear, authoritative explanations customers need at those moments.
Traditional FAQs were written for human browsing, not machine interpretation. AI assistants surface and synthesise content, exposing gaps and inconsistencies in those explanations.
In most organisations, responsibility for explaining how a journey works is fragmented across teams: operations, service, marketing, compliance, and product.
When explanations are fragmented, AI systems begin inferring answers. They may also look for confirmation from external sources — forums, blogs, community discussions — which means the explanation customers receive may come from places the brand neither controls nor updates.
How AI Systems Actually Answer These Questions
In practice, most generative AI systems combine large language models with retrieval mechanisms.
When a customer asks a question, the system searches across available sources — websites, help articles, forums, and documentation — and retrieves relevant passages, and then synthesises a response.
The quality of the answer to your customer depends heavily on how clearly the underlying explanations are structured and whether the sequence of the experience is easy to interpret.
When explanations are fragmented or ambiguous, the system fills the gaps by inferring the most likely answer.
In other words, AI systems are now interpreting your experience using whatever explanations they can find.
Making Customer Journeys Legible to AI
Customer journeys are naturally sequential.
- A customer signs up for a service.
- They receive confirmation.
- The first bill arrives.
- Something unexpected changes.
- A support interaction may occur.
Customers experience these events as a sequence of moments. In many organisations, the explanation of those moments is scattered across dozens of disconnected pieces of content: help articles, policy documents, marketing pages, support scripts, and internal guidance.
AI systems retrieve fragments of those explanations and attempt to assemble them into a coherent answer. When the underlying logic is unclear, the result can be misleading. AI systems infer answers, lose confidence in the brand’s own explanation, and look elsewhere to fill the gaps.
This is where sequence becomes important.
Large language models are far more reliable when they can interpret structured explanations of what happens first, what happens next, and what exceptions might apply.
When the sequence and logic of a journey are explicit, AI systems can interpret and communicate it far more accurately.
Customer journey mapping and moments-of-truth analysis reveal which questions matter most — and which explanations should be prioritised and kept consistently up to date.
Content design and structured publishing then make those answers legible.
Generative Engine Customer Experience Optimisation (GECXO) is the discipline of deliberately shaping how AI systems interpret and explain your customer experience.
Where to Start
Fortunately, organisations do not need large transformation programmes to begin addressing this. The most effective starting point is surprisingly small.
Start with one journey.
- Identify the moments of truth where confusion most often occurs — where customers ask questions, contact support, or abandon a process.
- CX teams already understand many of these moments. Journey mapping programmes, contact centre analytics, and customer journey management platforms already highlight where customers struggle most.
- These insights make it possible to prioritise where clearer explanations will have the greatest impact.
- Then examine how those moments are currently explained across your customer-facing content.
- Are the steps clear?
- Is the sequence explicit?
- Are exceptions explained?
- Would an AI assistant easily understand what happens next?
- Finally, test how AI assistants currently answer those questions.
The gap between what the experience actually does and how AI explains it can be surprisingly revealing.
Improving a handful of high-impact explanations can significantly improve how AI systems guide customers.
A New Discipline for the AI Era of CX
Customer experience leaders have always focused on improving the design of journeys.
But as AI becomes an increasingly common interpreter of those journeys, a new capability is emerging. And it’s not just designing experiences, but ensuring those experiences are explained clearly enough for both humans and machines to understand.
Generative Engine Customer Experience Optimisation is simply the discipline of doing this deliberately.
Because if organisations do not explain how their experience works, AI assistants will, using whatever fragments of information they can find.
About James O’Connor
James works at the intersection of customer experience, technology, and operational transformation. He is Managing Partner at AmplifyXM and Director of OC&CO, advising both enterprise organisations and emerging CX technology companies on turning ambitious customer strategies into improved commercial outcomes.
His background spans strategy and transformation at Bain & Company and Forrester, where he also led global product and operations teams and co-founded a Bain-backed CX technology venture. His recent work focuses on how AI tools are increasingly interpreting company experiences for customers, reshaping expectations before organisations even realise those customers need help.
