The Global Shopper, Reimagined
When GenAI shapes the customer journey before the customer arrives
February, 2026
Years ago, I wrote a feature for Desktop magazine about the global shopper: a customer who crossed borders, compared at speed and expected digital commerce to keep up.
The channels have changed, but the shopper’s orientation has not. Customers have continued to become better informed, less patient with friction and more willing to compare across brands, markets and platforms.
Now the journey is shifting again.
It may still end on your website, in your app, in your store or with your service team. But part of it may begin inside a generative AI tool, answer engine or AI browser that has already formed a view before the customer arrives.
The customer is no longer only searching. They are asking a tool to interpret the market for them.
That tool may read product pages, reviews, delivery promises, returns policies, support content and third-party commentary before deciding which options appear credible enough to mention.
It is always nice to meet the customer after someone else has explained you first.
The journey now starts earlier
AI-mediated discovery allows customers to begin with a need rather than a brand or category.
They might ask:
I need something reliable for this use case. I care about price, delivery, returns and support. What should I consider?
The tool may compare specifications, summarise reviews, interpret policies and identify trade-offs. It may also decide which brands are worth including and which appear too difficult, expensive or uncertain.
Sometimes the result will be useful. Sometimes it will be incomplete, stale or more confident than the evidence supports.
Either way, it may shape the customer’s first understanding before the organisation’s own digital experience has had a chance to do much at all.
The website is not dead. Most customers will still need an owned channel to explore the detail, check availability, make a purchase or complete the service.
But the website may no longer be where the first meaningful interpretation is formed.
That changes the role of the owned experience. It may need to confirm, correct or expand an understanding formed somewhere else.
Where the mismatch appears
Most customer journeys are still designed around the person who arrives.
Organisations optimise imagery, content, navigation, offers, comparison tools, service prompts and checkout flows. That work remains important.
The difference is that the customer may now arrive carrying an interpretation of the organisation already.
They may believe the product is best suited to a particular use case. They may have a view about whether the price premium is justified, how easy the returns process will be or whether support appears reliable.
The organisation may not have written that interpretation, even if it supplied some of the source material.
A generative AI tool may describe a slightly different promise from the one the organisation believes it made. A condition in the returns policy may disappear from the summary. A premium product may be reduced to “similar, but more expensive.” A service may be recommended for a need it was not designed to support.
The customer then arrives expecting the AI-generated version, and the website, store or service team inherits the gap.
That can produce abandoned journeys, avoidable service contact, complaints, returns and pressure on frontline teams to explain expectations they did not create.
Being visible is useful. Being confidently misrepresented is not.
The organisation needs to be legible
For a long time, organisations could assume they would have an opportunity to explain themselves. The customer would arrive, browse, compare and decide, and the brand could shape at least part of that understanding through the experience it controlled.
Some of those moments are now moving upstream.
The question for leaders is whether the organisation is clear and consistent enough to be represented reasonably when it is not in the room.
Not perfectly. No organisation can control every external summary, review or answer.
But the underlying evidence should be strong enough that customers are not sent into the journey with the wrong expectation.
This makes some previously unglamorous work much more important.
Product data is not just back-office hygiene. Policy content is not fine print that can be revisited eventually. Delivery commitments, support promises and eligibility conditions cannot quietly vary between the product page, help centre and contact-centre script.
In an AI-mediated journey, those inconsistencies become source material.
If the source material is fragmented, the interpretation is likely to be fragmented too.
The organisation may think it is managing a customer journey when the customer is actually arriving with an interpretation formed somewhere else.
That is now part of customer experience.
Start by observing, not optimising
The practical starting point does not need to be a large AI-discovery strategy.
Choose one product category, service journey or customer need and ask several major generative AI tools the questions a real customer would ask.
Not brand-approved questions. Real ones.
Which option is best for my situation? What should I watch out for? Which provider has the clearest returns policy? Why is this brand more expensive? Which service appears most reliable?
Then compare the answers with the organisation’s actual product data, policies, service promises and frontline reality.
I would want to know what the tools consistently get right, what they flatten or misunderstand, which sources they appear to rely on and whether the expectation created can be supported once the customer arrives.
The first step is observation.
I would not rush straight into optimisation, new content production or another technology purchase. Start by understanding the gap between what the organisation believes it is communicating and what customers are being told elsewhere.
That audit is relatively inexpensive.
The harder part may be reading the results without immediately explaining why the tool is wrong.
Some issues will sit in the AI-generated interpretation. Others will reveal that the organisation’s own data, policies or service messages were never as consistent as everyone assumed.
Both findings are useful.
The response may involve clearer product information, stronger policy content, better structured data, improved third-party listings or a more deliberate handover into owned and human channels.
The point is not to control every answer. That is neither realistic nor a particularly good use of an executive team’s time.
The aim is to make the organisation easier to understand and harder to misrepresent.
The global shopper has changed again.
This time, the customer may not simply arrive informed. They may arrive with expectations shaped by a version of the organisation that the organisation did not create.
Executive note for leaders
Ask a practical question:
What is GenAI telling customers before they reach us?
Choose one product category, service journey or customer need. Ask the questions a real customer would ask, then compare the answers with your product data, policies, service promises, support content and frontline reality.
Look for what is being flattened, omitted or misunderstood, and whether the organisation can support the expectations being created.
The aim is not to control every AI-generated answer.
It is to make sure the organisation is clear and coherent enough to be represented fairly before the customer arrives.
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If a piece raises a question, surfaces a pattern, or helps you think more clearly about a decision, I’d value the conversation.
Thanks for reading,
Stuart Gonsal MAICD
With occasional help from Springsteen, my Border Collie, who reminds me that clarity comes from movement 🐾.
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Disclaimer
Everything shared in The Ripple Effect reflects my personal views and does not reflect those of my current or past employers, clients or partners. Any examples are illustrative, drawn from publicly known patterns or anonymised experience.