By Ian Colvin, Chief Strategy Officer & Partner, Greenpark
For years, brands have invested heavily in omnichannel strategies designed to help customers move seamlessly between websites, marketplaces, social platforms and physical stores. But that strategy was built around one assumption: that customers wanted to navigate those channels themselves.
Increasingly, they don’t.
As large language models (LLMs) become part of everyday life, consumers are asking AI to do that work for them. Rather than moving between search engines, retailer websites, reviews and social media, they’re asking a single AI assistant to research, compare and recommend products on their behalf.
The customer journey hasn’t disappeared, but much of the effort involved in navigating it has. Success is no longer just about being present across multiple channels. It’s about whether AI understands your brand well enough to recommend it. AI isn’t replacing omnichannel – it’s redefining it.
Fewer touchpoints over better-connected ones
As AI increasingly becomes the interface between brands and customers, consistency has never mattered more…
A brand whose tone, claims and positioning shift between its website, social channels and press coverage makes it harder for the model to build a coherent picture of what that brand stands for.
An LLM can’t recommend what it doesn’t understand. Delivering that kind of consistency is harder than it sounds because websites, social and PR often sit with separate teams and sometimes even separate agencies who rarely compare notes with one another.
Having an omnichannel strategy was always meant to solve exactly this problem. AI has simply made it more important than ever.
The narrative risk
An important note of caution here is that as AI platforms become the main way people discover and choose brands, brands inevitably lose some control over their own story.
In a pre-AI world, a brand could push its message straight to its audience and be reasonably confident it would land as intended.
But now there’s an intermediary sitting in between, weighing one brand against another in ways that nobody outside the AI labs fully understands so brands really need to consider how they shape their narrative across all channels.
Greenpark has seen what that looks like in practice in the gap between Tony’s Chocolonely and Nestlé on chocolate sustainability. Tony’s is a tiny Dutch brand in comparison to Nestlé, yet it has been winning share of voice in AI search results. Nestlé, by contrast, runs a genuinely large-scale, long-standing fair sourcing programme for its cocoa, at a scale Tony’s simply can’t match.
The issue is that Nestlé’s programme wasn’t structured clearly enough for AI models to understand and surface it consistently. Being good at something and being findable for it have become two different jobs.
An unexpected kind of influencer
AI is also changing what influence looks like. Traditionally, influence has been built by people with audiences. Increasingly, it’s also being shaped by AI.
AI platforms are rapidly becoming a new, text-based source of influence. When someone asks their LLM of choice to compare two products or recommend a brand, what comes back isn’t a list of options to sift through; it’s a recommendation, delivered with the same authority that a trusted expert would carry.
AI isn’t just mining answers – it’s helping people decide what to buy, which brands to consider and ultimately, what to think about them.
Good AI-enabled customer experience
Friction is anything that forces customers to work harder than they should to make a decision.
It’s easiest to see in physical retail.
A VIP personal shopping trip at a store like Selfridges works precisely because someone has already done that work on the customer’s behalf, briefed in advance on their size, their colouring, their taste, so the shopper arrives to a curated shortlist rather than two floors to search through alone.
AI search delivers that same experience to everyone, by default, rather than reserving it for a handful of VIP customers.
Buying spectacles is a good example of where that friction still exists almost everywhere online. Trying to find the right style and fit for your face is tricky as an online task even though the technology to solve it already exists.
Most retailers still ask customers to click through dozens of product pages, guessing at what might suit their face shape, rather than letting them upload a photo and get matched to frames that actually work for them.
An LLM doesn’t need that guesswork because once it knows a customer’s face shape and style preferences, it can draw on that directly and return a considered shortlist from a single prompt.
Brands need to spot where their own customers are working hardest and hand that effort over to AI to set the standard everyone else gets compared against.
So, what should brands do?
None of this means brands should abandon existing channels. Websites, social and stores still matter. What’s changing is how customers navigate them. Increasingly, they’ll ask AI to do that on their behalf.
That means the next steps are clear and, reassuringly, they’re achievable.
Audit how AI already describes your business. Make sure your website, PR and social channels tell the same story. Structure your expertise in a way AI can understand, not just people.
Most importantly, identify where customers are working hardest and ask how AI can remove that friction.
The brands that succeed won’t simply be present across every channel—they’ll be the ones AI understands well enough to recommend.
