Branding for Two Audiences: People and AI

How Brands Can Remain Meaningful to People…

…While Becoming Clear, Credible, and Recommendable to Machines

For most modern branding, we have been trying to earn a place in people’s minds. We have worried about awareness, meaning, distinctiveness, memory, preference and trust, occasionally producing a new pyramid or concentric circle to make the whole affair look reassuringly scientific.

A new participant has now joined the decision-making process: artificial intelligence -AI-.

People increasingly ask AI assistants which hotel might suit a particular trip, which software could help their company, which running shoe fits a certain need, which bank offers a particular service, which university might suit their children or which wine would work with dinner. The answer may include several brands, comparisons, explanations and, increasingly, recommendations.

The machine has quietly joined the buying committee. Rather awkwardly, nobody sent it an agenda.

This creates an interesting branding challenge because people and artificial intelligence interpret brands in very different ways. People bring memory, experience, emotions, cultural context, habits and prejudice to a decision. AI systems retrieve, compare, synthesise and infer from large quantities of available information. People may fall in love with a brand. Algorithms remain considerably harder to seduce over candlelight.

Brands therefore need to perform in two related environments. They must remain meaningful, distinctive and desirable to people while becoming sufficiently clear, credible and well evidenced for machines to understand what they are, when they are relevant and why they deserve consideration.

That is a bigger question than SEO. It is becoming a branding question.

A New Layer Between People and Brands

For years, search provided a relatively visible route between curiosity and choice. Someone typed a question, received a collection of links and decided where to go next. Brands could work on visibility, content, ranking and conversion while retaining considerable influence over what people eventually encountered.

Generative AI changes part of that journey. An AI assistant can interpret a question, consult multiple sources, compare alternatives and present a synthesis before somebody visits a corporate website, reads an advertisement or speaks to a salesperson. In some situations, the customer reaches the brand after an algorithm has already framed what that brand means.

That changes the order of the conversation. A person asking for “a laptop brand known for privacy and an integrated ecosystem”, “an outdoor clothing company with strong environmental credentials” or “affordable furniture with circular services” gives an AI system a collection of signals to interpret.

The system searches for brands that have developed recognisable associations around those subjects and for evidence supporting them. For example:

• Apple has invested for years in making privacy a visible part of its proposition, products and corporate discourse.
• Patagonia has connected its environmental position with highly tangible behaviours such as product repair, resale and extending the useful life of clothing through Worn Wear.
• IKEA connects its sustainability ambitions with circular services involving repair, resale, reuse and recycling.
• LEGO links its long-established idea of learning through play with commitments involving children, materials, packaging and environmental impact.

These companies differ enormously in category, history and business model. What they share is something increasingly important: their intended meaning leaves traces.

People can experience those traces. Machines can retrieve them.

Machines Need Clarity, People Need Meaning

This difference deserves attention. People rarely evaluate brands as databases. A holiday memory, the feel of opening a package, something a parent always bought, the way an employee dealt with a problem or a comment from a trusted friend may influence preference far more than a perfectly organised corporate website.

Artificial intelligence approaches the matter differently. It works with accessible signals: descriptions, associations, structured information, products, reviews, media coverage, expert opinions, documentation, customer conversations and evidence distributed across the digital environment.

For a machine, ambiguity makes interpretation harder. For a person, ambiguity can occasionally make a brand rather interesting. The challenge is therefore one of dual readability.

A brand needs enough strategic clarity for machines to understand its category, relevance, difference and proof. It simultaneously needs enough richness for people to find meaning, personality, familiarity and reasons to care.

An organisation can describe every feature of its offer with machine-like precision and still inspire approximately the emotional attachment of an airport parking ticket. Conversely, a brand can create beautiful stories that leave both customers and algorithms slightly unsure what the company actually does.

Strong brands increasingly need both qualities.

Being Visible Is Only the First Hurdle

Much of the emerging conversation around AI discovery naturally focuses on visibility: whether a brand appears when people ask ChatGPT, Gemini, Perplexity, Copilot or other systems about a category or problem.

Visibility matters. Yet it represents only one stage in a longer process.

A useful sequence might be: visibility, understanding, credibility, consideration, and preference -in this sequence-.

A machine may find a brand while interpreting it inaccurately. It may understand the brand while finding insufficient evidence to support a recommendation. It may regard several brands as credible while seeing little meaningful difference between them. Eventually, we arrive at the old branding question wearing new digital clothes: Why this one?

As AI reduces the effort required to generate content, more organisations will be able to produce plausible articles, polished descriptions, competent videos and endless streams of perfectly acceptable communication. The internet will hardly suffer from a shortage of words.

That makes strategic clarity and distinctiveness more valuable.

If five competitors describe themselves as innovative, sustainable, customer-focused and passionate about excellence, a machine faces much the same problem customers have faced for years. Everyone sounds splendid and nobody sounds particularly different -and, unfortunately, that’s what we’re living through right now-.

Brands require associations they can genuinely own, evidence that reinforces them and enough consistency for those associations to accumulate over time.

This is one reason brand positioning becomes more important in an AI-mediated environment. It provides the stable strategic centre around which products, behaviours, experiences and information can organise themselves.

At Allegro 234, we approach positioning through Ambidextrous Strategy: preserving the meaning, assets and sources of trust that create value today while developing what the brand needs to remain relevant tomorrow. The arrival of AI discovery fits naturally into that tension.

Your Brand Will Be Interpreted Whether You Manage the Interpretation or Not

A company may present itself beautifully on its homepage. The wider digital environment may tell a more complicated story.

AI systems can potentially encounter corporate content, product information, customer reviews, interviews, specialist articles, news, employment discussions, social commentary and third-party descriptions. The resulting interpretation emerges from a broader set of signals than the traditional brand-controlled message.

For companies, this makes coherence increasingly valuable.

When the corporate proposition says one thing, customer experience another, employees something rather different and products offer little evidence for any of them, artificial intelligence has simply gained another way of detecting a problem that already existed.

Branding has always involved managing perception without owning it. AI increases the number of intermediaries involved in that process.

Our Branding approach starts from the idea of the brand as a strategic business platform: something connecting company purpose, business strategy, positioning, expressions, experiences and behaviour.

That connection matters because machine legibility ultimately begins with organisational clarity.

A company that genuinely knows what it stands for, where it creates value, whom it serves and why its offer is different has a much easier story to express consistently. Digital optimisation can then help make that meaning accessible. It has something solid to optimise.

Behaviour Is Becoming Searchable

The relationship between what a company says and what it does becomes particularly interesting here.

• Consider Patagonia again. Its environmental positioning acquires substance through repair services, Worn Wear, durability initiatives and operational commitments. Someone encountering the brand can connect the message with observable behaviour.
• Or consider IKEA. Circularity becomes visible through second-hand programmes, spare parts, repair guidance and initiatives designed to extend product life.

These behaviours matter to people because they help establish credibility. They also create information that digital systems can discover, connect and interpret.

This provides a contemporary bridge to Brands with a Conscience.

We understand conscience as a decision system: purpose, values and principles become criteria through which organisations make choices, establish limits and behave. Over time, these choices produce experiences, actions and evidence.

This is especially relevant when machines can compare corporate claims with a wider body of information.

Purpose with proof becomes considerably stronger than purpose with a paragraph.

The same principle applies beyond sustainability. A bank claiming simplicity should leave evidence in its processes and interfaces. A technology company claiming privacy should express that commitment in product design and policies. A premium brand claiming craftsmanship needs evidence in materials, processes and service. A B2B company promising partnership should probably behave like a partner once the sales presentation has finished.

Increasingly, what brands do becomes part of what machines know about them.

Ambidextrous Branding for an AI-Mediated Market

AI introduces another strategic tension. Brands need consistency because meaning accumulates through repetition. They also need adaptation because technologies, channels, expectations and modes of discovery continue to evolve.

This is precisely the territory of Ambidextrous Branding. Some elements deserve protection: purpose, central meaning, trusted associations, positioning, distinctive assets and principles. Others should evolve: content structures, propositions, digital experiences, interfaces, forms of expression, services and the ways information is made available to emerging platforms.

The aim is a brand stable enough to be recognised and flexible enough to remain relevant.

That distinction becomes particularly useful when companies start experimenting with AI discovery. A rush to optimise everything for algorithms could flatten the very differences that make the brand valuable to people. Total resistance would leave an increasing part of discovery unmanaged. That is a strategic decision before it becomes a technological one.

Going to the tactical level, AI tools can help organisations analyse how different platforms describe their brand, identify gaps between intended positioning and external interpretation, explore category associations, compare evidence and detect inconsistencies.

Human judgement still has to decide which findings matter.

A dashboard may tell you that your brand is rarely mentioned alongside a strategic attribute. It cannot, by itself, determine whether chasing that attribute makes sense for the business, strengthens differentiation or sends the brand galloping enthusiastically in the wrong direction.

Tools can reveal possibilities. Strategy chooses.

Designing a Brand that Both Audiences Can Understand

The practical implications extend across the organisation. Brands will increasingly need clear positioning, recognisable category associations and distinctive ideas. Corporate and product information will need enough structure for machines to interpret it accurately.

Taglines, slogans and claims will need supporting evidence. Experiences will need to reinforce the intended meaning. Reputation will matter because third-party signals become part of the informational environment.

Marketing, technology, customer experience, communications, culture and branding therefore become even more interconnected. This also gives companies a useful way to test themselves. Imagine asking an AI assistant five questions:

• What does our company stand for?
• Who is it particularly relevant for?
• How is it different from its main alternatives?
• What evidence supports those differences?
• Why would you recommend it?

Then ask customers the same questions. The interesting part begins when the answers disagree. Those gaps can reveal unclear positioning, weak evidence, fragmented experiences, inconsistent language or an association the company believes it owns far more strongly than the market does.

AI becomes more than another channel through which brands are discovered. It’s a mirror, the bathroom one at 7 a.m. which has been preparing us for this for years-.

Two Audiences, One Underlying Requirement

Artificial intelligence changes the mechanics of discovery and recommendation. It also reinforces some rather old truths about branding.

Brands need clarity, meaningful difference, evidence, coherence between what the company thinks, what it does and what it says, enough continuity for associations to accumulate and enough adaptability to remain useful as behaviour and technology change.

The growing role of AI simply makes those requirements easier to expose.

For people, the brand still has to create relevance, memory, confidence and preference. For machines, it has to create intelligibility, evidence and contextual relevance.

When both read roughly the same story, something important has happened: company, business and brand have probably become considerably more coherent.

Perhaps the real challenge of branding for people and AI is therefore surprisingly familiar.

• Build a company that knows what it means.
• Create enough evidence for others to believe it.
• Then make it easy to understand.

Humans tend to appreciate that. Apparently, machines do too.

Further reading from Allegro 234

• AIR | Artificial Intelligence Resources
• Businesses, Leaders and Brands with a Conscience
• Brand Positioning through Ambidextrous Strategy
• Ambidextrous Brand Strategy | What Companies Need When Today Is Not Enough
• AI and Branding | Automation and Trust
• Brand Strategy and Branding Services

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Kindel Media, Pexels