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Generative artificial intelligence (AI) and large language models (LLMs) like ChatGPT, Gemini and Claude haven’t just given marketing teams a new toolbox, they’ve completely re-written the rules of the game.
Today, branding is no longer just about showing up in traditional channels and Google search results. It’s now about understanding how the brand is perceived, processed and recommended by algorithms, and how consumers are using AI to make purchase decisions as they navigate a customer journey that’s constantly changing and ever more automated.
The numbers from various reports (like this one on AI marketing trends from Averi AI) show how AI has become a vital piece of business infrastructure, with roughly of 88% marketers saying they use AI daily for various tasks and purposes. And what we’re starting to see is the AI branding scenario which – a little ahead of the curve, we might add 😉 – this blog was already talking about way back in 2020, when there were still more questions than answers about the technology.
So, where are we now and where are we heading?
In this post, we’ll explore the new approaches to building and managing brands and marketing campaigns that AI is enabling, while also looking at new concepts like algorithmic consumers and AI Darwinism along the way.
How is AI re-shaping the customer journey?
Here, it’s no exaggeration to say that there’s been a revolution.
The traditional marketing funnel, with its three-stage flow from awareness to consideration and then conversion, is becoming more fluid and, in many cases, invisible: AI is acting as both accelerator and intermediary in the customer journey in at least three key ways.
- Shrinking linear search: consumers no longer type a couple of keywords into Google, compare the results and then make a choice. Instead, they’re asking LLMs questions like: “I often travel for work and I’m looking for a trolley suitcase that’s sturdy and high tech. I have a £500 budget. What do you recommend?“
We’ve moved from the zero moment of truth – the phase of the customer journey when the consumer searches for a product or service online – to the agentic moment of truth: when, rather than serving up search results, an AI agent re-frames the need and provides a shortlist of a few (very few even…) options for the user to choose from.
- Compressed customer journeys: as the latest Adobe AI and Digital Trends Report highlights, generative AI models and AI agents are transforming the user experience by shortening interaction times. The passage from discovery to decision often takes place within a single chat session. So if a brand is not in that chat, it effectively doesn’t exist for that user. In the words of Erich Joachimsthaler, world-renowned expert on branding:
The old funnel – awareness → consideration → purchase – is collapsing. AI compresses these steps into a single action.
- Hyper-personalisation and sales ecosystems: we’re moving towards ecosystems in which AI assistants don’t just inform users, but plug directly into complex value chains and direct purchasing systems (like the commerce protocols that OpenAI has developed with Stripe and Google with Shopify).
How consumers have changed: three characteristics of the algorithmic consumer
When brand building today, we first have to understand how our target audience has evolved psychologically and behaviourally. In the AI era, users and consumers display some fundamental and often contradictory characteristics that in the past mattered less – or were even entirely absent.
- Zero tolerance for irrelevance. Now accustomed to instant, tailored answers, people won’t put up with one-to-many communication anymore. A generic newsletter or irrelevant ad are perceived as background noise and immediately ignored. This is where solutions like Dynamic Yield and Salesforce Cloud Einstein come in by offering advanced and dynamic personalisation of content and messaging throughout the customer journey.
- Preference for authenticity. In a world awash with artificial content, consumers are rewarding authenticity. A detailed trend analysis carried out by The Branding Journal shows how people are willingly and actively seeking human imperfections, handmade approaches, true stories and unique view points that can’t be generated by AI.
- Growing trust in AI solutions. Paradoxically, we’re also placing more trust in AI to guide us in our purchase decisions. This has brought with it the new concept of generative engine optimisation (GEO), which involves brands curating their digital presence to ensure they are cited (correctly!) in AI chat answers. But greater openness to AI brings challenges, too: Google users who are shown AI overviews – a feature that employs generative AI to provide direct answers to queries at the top of the SERP – are less likely to click links to other websites than those that aren’t shown them. And Google CEO Sundar Pichai has talked about an end-to-end AI search experience. The direction of travel is clear and has been for some time: for a good decade, far-sighted marketers have been talking about the algorithmic consumer. This describes the phenomenon whereby consumers delegate a large part of their purchasing decisions to algorithms and AI agents. There’s a nice paper from 2016 (!) in which Harvard researchers explore the idea in depth. And quick to grasp the implications, SEO solutions like Semrush (with its AI Visibility Toolkit) and Ahrefs (Custom Prompt tracker) now let marketers monitor how brands are mentioned in conversational search.
Lessons from how top brands are (and aren’t) using AI in their marketing
Not only is AI is fundamentally re-shaping the customer journey, it’s also opening up new opportunities – and threats – for marketing. Here are three insightful case studies about AI use in advertising by global consumer brands.
Nike
After Jannik Sinner’s record-breaking triumph at the Italian Open in Rome in May 2026, one of the tennis star’s main sponsors, Nike, posted a piece of content that quickly went viral for the wrong reasons.
What was all the fuss about? Well, many saw in the copy telltale signs of a non-human creative colleague (the em dash, the “it’s not X, it’s Y” rhetorical construction ). Which brings us to another curious characteristic of the algorithmic consumer: a study published in the July 2024 issue of the Journal of Retailing and Consumer Services showed how brands that use GenAI to create social media content are perceived as less authentic and less credible. And for a brand like Nike whose identity is built around personal passion, human performance and the stories of overcoming that go with them, this is a problem…
Heinz
Who hasn’t heard of the iconic brand of ketchup? Well, quite. It’s exactly this truth – so simple yet powerful – that lies behind the company’s latest ad campaign. In Heinz AI Ketchup, we see generative AI being prompted to create images of ketchup. A generic word for which the AI engine could have produced pictures of all of manner ketchup bottles from of sorts of brands. Yet it did this…
Dove
Using AI well also means staying true to your brand’s identity and purpose. Because purpose-driven marketing is a serious business, and Dove knows it.
About 20 years ago, the brand launched a marketing campaign (which would later become its brand signature) called The Real Beauty, which challenged prevalent female beauty standards and encouraged women to feel confident in their bodies.
The Real Beauty was inspired by an international study that asked more than three thousand women in ten different countries about their perceptions of female beauty and their relationship with their own bodies.
The research produced some striking findings:
- Only 4% of women saw themselves as “beautiful“
- 47% considered themselves overweight
- Over 70% of the women interviewed didn’t attribute the concept of beauty solely to a woman’s physical appearance
The brand saw in this an opportunity, as former Dove CMO Alessandro Manfredi explained:
This allowed us to demonstrate that social impact, when fully embedded into a business, not only does not trade off with profit, but is a phenomenal driver of growth.
In 2024, to mark two decades of the campaign, Dove doubled down on its purpose and message by taking another strong stance: the brand vowed never to use AI instead of real women in its marketing. In so doing, it strengthened its positioning by championing authentic beauty over artificiality.
The future of brands in the age of LLMs: towards AI Darwinism?
Whether it’s for re-shaping customer journeys, finding new ways for developing content or exploring innovative uses in advertising, AI adoption is now a priority for leaders in marketing, communications and branding, no matter the size of their company or industry.
But where does the future lie? Many believe agentic AI – a term that I’ve already mentioned here and there – will cause the next paradigm shift.
‘Traditional’ AI tools, like chatbots, respond to direct prompts from users. But with agentic AI, an agent receives a high-level goal, autonomously plans how to achieve it and then uses external tools to complete the task, all with minimal human intervention.
So how should we prepare for this next phase? A study published in the March/April 2026 issue of the Harvard Business Review tackled this very question. And, based on the results of extensive research carried out with international consumers, the authors offered several takeaways:
As AI agents spread, the traditional relationship between brands and consumers will give way to new types of interaction, some mediated by AI, others entirely guided by it.
Alongside direct human-to-human relationships, three other types of interaction are emerging between people and brands.
1. Brand agent ➔ human consumer
In this model, brands deploy their own AI agents designed to interact directly with humans and guide them along the customer journey. We’re talking about systems like the Capital One digital concierge for car buyers, which checks stocks, books test drives and calculates financing, letting users complete almost all of the purchase process before they even set foot inside a dealership.
2. Consumer agent ➔ brand
Here consumers delegate search, comparison and sometimes even purchase to their own AI assistant. The agent browses the web, fills in forms and evaluates options from different brands on behalf of the user, acting as their virtual representative. In this scenario, a website’s aesthetics and user interface matter less: what matters more is presenting information in a structured, machine-readable format (hence all the chatter about machine-readable brands), otherwise the agent will ignore the offering. BBC technology journalist Thomas Germain talks about the machine web: an internet where websites are built to be read by AI agents as well as humans, and where we get most of our information from machine-generated summaries.
3. Agent2agent commerce
In this scenario – the most complex of all – direct human interaction almost completely disappears from the customer journey. Transactions are instead carried out autonomously between AI agents: the consumer agent (which knows its user’s preferences, budget and intent) deals directly with the brand agent (which manages availability and prices in real time). A simple example would be an AI personal assistant scanning OpenTable or The Fork, picking the ideal place to eat and making a booking by directly interfacing with the restaurant’s AI-driven reservation system.
This last vision, so new that its consequences are still hard to predict, may seem far off, but it is by non means impossible. And should this come to pass, it would pave the way for a concept that innovation expert Brian Solis calls AI Darwinism.
Solis argues that:
AI maturity isn’t adoption. It’s the ability to reinvent how value is created.
Most organisations are still trying to adapt AI to the business that they already know. They’re using machines to work faster, cut costs, summarise meetings, automate tasks and make traditional workflows more efficient.
But according to Solis’s AI Darwinism framework, the real opportunities lie in asking what AI makes possible that wasn’t possible before.
- What processes should be re-designed?
- What customer needs are we missing that are right under our noses?
- What roles can be augmented rather than eliminated?
- What biases and beliefs are holding back the organisation’s growth?
The bar keeps being raised: from generative AI to agents, from agent-based workflows to completely new models of interaction. The brands and businesses that prosper won’t be those who just automate the old model, but those who invent the next.
Now there’s a challenge!