The brand strategy playbook needs rewriting. AI just changed the rules.

AI • JUNE 2026

The rules of consumer decision-making are being upended. An AI assistant now decides whether you make the shortlist of brands and it doesn’t respond to great advertising, emotional storytelling, or media spend. The brands that thrive will be those that treat consistency as a strategy – convincing both AI algorithms and humans, and closing the gaps between claims and evidence, between advertising and experience, and between what you say and what others say about you.

AI is doing to decision-making what Google did to search, and most brands aren’t ready for it

The last time the rules of consumer decision-making changed this fundamentally, it was Google rewriting them. Now, AI is doing to the decision itself what Google did to search.

When Google emerged, brands found themselves competing on entirely different terms – the quality, structure, and credibility of their digital presence suddenly mattered more than their media budget. Those that failed to adapt became invisible. The new language was SEO.

With the emergence of AI, brands find themselves competing on entirely different terms – the coherence, credibility, and consistency of everything ever written about them suddenly matters more than the quality of their latest campaign. The search, the comparison, the shortlist, the recommendation are all being shaped by a layer most brands are not set up to influence. Those that fail to adapt will be left off the list entirely. Brands now need to learn two new languages. AEO (Answer Engine Optimization) focuses on making content highly extractable for direct answers (like featured snippets or AI summaries) and GEO (Generative Engine Optimization) focuses on building broad brand trust so AI models naturally recommend your content in detailed responses.

Perhaps ominously for brands, this is where the parallel with Google ends. When SEO emerged, the rules were at least visible. You could test, iterate, and improve. With AI, the models that decide whether your brand makes the shortlist were trained on data you didn’t curate, using criteria you can’t fully audit, producing outputs you can’t directly control. This creates a governance challenge. The question is no longer whether AI is influencing consumer decisions, it is whether your organisation is set up to be found, trusted, and preferred by the models that are making them. The brands that get this right won’t do it through marketing alone – it will take legal, product, pricing, technology, customer experience and the boardroom all pulling in the same direction.

Brands need to convince AI algorithms before they can convince humans

For brands that have spent years optimising for visibility at different stages of the customer journey, AI demands a fundamental reorientation from being findable to being recommendable. When an AI makes a recommendation, it typically surfaces one to three synthesised options. Brands that don’t appear in that shortlist aren’t ranked lower, they’re absent.

Understanding why requires understanding of how AI assistants generate their recommendations. AI doesn’t read your website in the moment. It works from a synthesised model of your brand, constructed from everything it was trained on including what’s been published about you across the internet, how specific and verifiable your claims are, and how much third-party corroboration exists. Brands with rich, accurate, machine-readable information have a structural advantage they may not yet realise they have.

Working with colleagues at Brave Bison, we’ve outlined the practical steps brands should take to earn a place in AI-generated shortlists:

Implement structured data markup across your site.

Schema.org markup tells AI crawlers what you are, what you do, and what makes you distinct. Without it, AI infers your identity from prose alone, which is imprecise and inconsistent.
Ensure consistent brand signals across every digital property.

AI builds a composite picture from everything it finds – your site, Reddit, Wikipedia, LinkedIn, Crunchbase, press coverage, review platforms. Inconsistency produces a blurred, unreliable representation.
Replace vague positioning with specific, verifiable claims.

AI cannot synthesise “we believe in a better tomorrow.” It can synthesise “used by 4,000 businesses in 30 countries, rated 4.8/5 across 12,000 reviews, reduces onboarding time by 40%.” If a claim can’t be cited, it won’t be used.
Publish comparison content explicitly.

AI frequently responds to queries framed as comparisons. Brands that have published honest, well-structured comparison content give AI exactly the material it needs to recommend them in context.
Build FAQs around real decision queries.

Map the questions consumers actually ask when deciding in your category, mirroring their language. Brands that have already answered the question rank higher in the synthesised response.
Build third-party corroboration systematically.

AI trusts what others say about you more than what you say yourself. Grow your review footprint on credible platforms, whichever are most authoritative in your category.

Marketing can satisfy emotional and social needs that AI algorithms can’t

Consumer adoption of AI assistants is only going to accelerate. In behavioural science terms, AI reduces cognitive load, dissolves choice paralysis, and delivers the authority of an impartial expert more efficiently than search ever could.

But mercifully for brands, AI has one major limitation. AI optimises for the decision. It does not optimise for the relationship.

An AI assistant will not convince a Nike buyer to choose New Balance, or a Heinz buyer to switch to the supermarket own-brand. It does not account for how a brand makes people feel, the values they see reflected in it, or the identity they express through choosing it. It does not empathise, it does not understand personal history, and it has no appreciation for individual taste.

Marketing science has long established that humans are primarily driven by the need to fulfil emotional and social goals. In his book ‘Decoded,’ Phil Barden shows that every purchase decision is the brain’s attempt to close the gap between its current state and a desired one, driven far more by implicit feeling than conscious reasoning. AI is useful at the functional layer – helping people make well-informed decisions – but it cannot help people achieve the outcomes that matter most to them as human beings.

What doesn’t change, then, is the essence of marketing. Brands still need to invest in building mental and physical availability, differentiating value and emotional connection. What does change is that AI will surface inconsistencies between what brands promise and what customers experience.

The brands that thrive will treat consistency between promise and experience as a strategy

Brand and marketing strategy in an AI world is, at its core, a consistency challenge. Think of it like a candidate running for office. Their campaign speeches (brand advertising) might be brilliant – inspiring, values-driven, emotionally resonant. But if their voting record, public statements, and social media history tell a different story, AI will surface the inconsistencies and voters will notice.

The same dynamic applies to brands. A brand that speaks with warmth and purpose in its advertising but presents AI with vague, inconsistent, uncorroborated information will struggle to make the consideration set. The promise made to the human will be undermined by the failure to convince the algorithm.

Equally, a brand that performs well from in front of AI algorithms but fails to create an emotional connection will struggle to make it from consideration set to first choice. The promise made to the algorithm will be undermined by the failure to meaningfully engage the human.

The brands that thrive in the AI era will be those that convince both algorithms and humans and treat consistency as a strategic imperative, closing the gaps between claim and evidence, between advertising and experience, and between what you say and what others say about you.

It is unthinkable now to build a business that isn’t discoverable on Google. Brands that don’t pivot from being findable to being recommendable may pay a heavy price, sooner than they expect. The brands that move now, before the models have fully hardened their view, will find it considerably easier than those who wait until absence from the shortlist shows up commercially.

How MTM can help

AI technology and use case mapping. The AI assistant landscape is moving fast, and its impact is uneven across categories. We map the emerging technologies, extrapolate the use cases most likely to affect consumer decision-making in your category, and make strategic recommendations in your competitive context.

Brand audit and consultancy. We audit how your brand currently appears in AI-generated responses, identify where you are absent or misrepresented, and prioritise the steps that will improve your chances of being recommended.

Understanding consumer attitudes and behaviour. AI adoption is not uniform, and neither is consumer trust in it. We conduct qualitative, quantitative, and tracking research into how consumers in your category are using AI in their purchase journeys.

Brand, customer and user experiences. We use established frameworks to help brands integrate AI with human experiences in ways that preserve meaning, trust, and relationship — the things that will define brand preference in an AI era.

Commercial opportunity. AI changes the economics of your category. We assess where AI creates threat and opportunity, help you build the go-to-market case, and connect AI investment to the outcomes that matter: revenue, retention, cost.