When everyone’s using the same tools, standing out becomes harder, not easier.
AI is brilliant at helping brands Zig: it can streamline processes, sharpen positioning, and amplify what’s already working. It’s fast, efficient, and ruthlessly consistent. But if your brand’s power lies in the Zag (in questioning convention, rewriting the rules, and breaking patterns that competitors follow), AI will struggle to take you there on its own.
Brand strategist Marty Neumeier defined the concept of “Zagging”, a way of thinking about differentiation, in his book Zag: The Number One Strategy of High-Performance Brands (2006). His premise: when everyone zigs, you zag.
When competitors follow predictable patterns, differentiation comes from doing the opposite, the unexpected, the previously unthinkable. It’s how Apple positioned itself against Microsoft, how Patagonia stood apart in outdoor retail, how Liquid Death disrupted bottled water.
The question now is whether AI can help brands zag, or whether it simply makes everyone better at zigging together.
The AI paradox
AI can analyse a thousand craft beer brands and tell you what they have in common, but it can’t tell you to sell water in a tallboy can and call it Liquid Death. It mirrors taste but rarely provokes it. While it can predict trends based on existing data, it cannot start movements that break free from those patterns.
Consider the B2B technology sector. Run a dozen company websites through an AI content analyser and you’ll find remarkable similarity: “leading,” “innovative,” “trusted partner,” “transforming businesses.” Now ask AI to write positioning for a new entrant. Unless carefully prompted otherwise, it will synthesise that same pattern. It will zig with everyone else.
This isn’t a flaw in AI. It’s how the technology works. Large language models are prediction engines trained on existing content, optimised to produce statistically probable outputs. They’re exceptional at recognising and replicating patterns. But zagging, by definition, means breaking the pattern.
A 2023 study involving BCG consultants found that GPT-4 improved individual performance on a creative product innovation task. However, the ideas produced across the AI-assisted group were collectively 41 per cent less diverse than those from the control group. For brands, this is the risk: AI can improve individual outputs while pulling everyone towards similar answers.
Source: Boston Consulting Group and Harvard Business School.
In creative problem-solving tasks, those using GPT-4 produced more consistent but less diverse ideas, with diversity dropping by 41%.
Where AI excels
None of this means AI isn’t valuable. It is transforming brand strategy in measurable ways. AI accelerates research and analysis, processing customer feedback, market data, and competitive intelligence at a scale no human team could match. AI uncovers patterns in audience behaviour, reveals sentiment shifts across markets, and surfaces insights that inform smarter decisions.
It brings speed and consistency to content creation, helping teams maintain tone of voice across dozens of touchpoints whilst freeing strategists from repetitive execution. It enables testing and optimisation at scale, refining messaging, testing propositions, and improving performance across campaigns.
At Mailchimp, AI tools analyse millions of email campaigns to surface what drives engagement, helping small businesses make data-informed decisions about their marketing. At HubSpot, AI synthesises CRM data to help sales teams understand customer behaviour patterns and time their outreach more effectively.
This is AI powering the zig: making the expected faster, sharper, more reliable.
Where humans must lead
But here’s what AI cannot reliably do: make the conceptual leap that defines a distinctive proposition, a big idea, or a brand strategy that reframes the rules of a category.
When Volvo focused on safety and BMW on performance, an algorithm would never have told Audi to zag with “Vorsprung durch Technik” (Progress through Technology), reframing the category around intelligent engineering, and, in time, transforming how the world perceived German industry itself. That sort of leap required human judgment, sensing cultural tension, competitor weakness, and brand potential.
AI struggles with cultural and emotional nuance. It can tell you sentiment is negative, but it can’t feel the weight of a word in a specific cultural moment. When Liquid Death’s founders decided to position water as rebellion against wellness culture, they weren’t following data. They were reading the room in a way AI couldn’t codify.
Taste and intuition remain human domains. AI can generate a hundred taglines, but it takes human judgment to recognise the one that carries the right balance of emotion and restraint. Slack’s “Make work wonderful” reflects a human instinct no algorithm would prioritise.
Perhaps most critically, provocation requires intent that goes beyond pattern recognition. When Patagonia ran “Don’t Buy This Jacket,” they weren’t optimising for conversion. They were challenging consumption itself, setting a new standard for brand purpose. AI, optimised for engagement and approval, rarely suggests you tell customers not to buy.
The B2B opportunity
For B2B brands, this balance between analytical intelligence and creative intuition is especially critical. In sectors dominated by technology, compliance, and efficiency, communication often becomes overly rational, precise, and entirely forgettable. Every cloud platform promises scalability, every consultancy promises transformation, every software company promises seamless integration.
AI naturally strengthens the analytical dimension: data interpretation, process optimisation, performance measurement, and logical argument construction. Humans drive the intuitive dimension: storytelling, empathy, emotional resonance, and meaning. The most effective B2B brands integrate both, becoming analytically rigorous in how they think and imaginatively bold in how they connect.
Stripe zagged in fintech by treating developers as the primary audience, writing documentation that was technically precise yet unexpectedly elegant. Slack zagged in enterprise software by making workplace communication feel human again, with warmth and personality in every interaction. Both strategies required data-informed thinking and creative courage.
The dual forces of modern branding
The strongest brands unite machine intelligence with human imagination, combining analytical power and creative intuition.
Use AI to power the zig
Let it accelerate research, surface insights, maintain consistency, and optimise performance. Use it to understand what exists, what’s working, and where patterns emerge. This is the foundation: knowing the landscape, understanding the rules, seeing where everyone else is positioned.
Reserve human judgment for the zag
Deploy creativity, intuition, and courage where differentiation matters most: in concept development, strategic positioning, cultural insight, and provocative thinking. This is where you break the pattern: challenge convention, overturn assumptions, and claim unexpected territory.
Integrate both continuously
The best strategies emerge from dialogue between data and intuition. Let AI inform your creative decisions with evidence. Let human insight guide your AI prompts with context, nuance, and strategic intent. Neither operates effectively in isolation.
The risk of over-reliance
There is a real danger emerging. As AI becomes easier to use and more impressive in its outputs, the temptation grows to let it make strategic choices that require human judgment. Companies are already building brands that sound like everyone else because they’re all using the same tools with the same prompts, trained on the same corpus of existing content.
The result is a kind of convergent mediocrity: brands that are perfectly competent, entirely predictable, and utterly forgettable. Everyone zigs together, and no one stands out.
What this means for brand leaders
If your competitors are using AI to get better at zigging – and you are too – you’ve achieved equilibrium, not advantage. Differentiation now depends on how effectively you zag: how you apply human creativity, taste, and courage to break the patterns that AI naturally reinforces.
This doesn’t mean rejecting AI. It means being deliberate about where you deploy it and where you don’t. Use it to handle the knowable: research, analysis, efficiency, optimisation. Preserve human capacity for the unknowable: the insight that contradicts the data, the proposition that feels wrong before it feels right, the creative leap that makes competitors question their assumptions.
The brands that will win aren’t those that use AI the most. They’re the ones who know when not to.
In the AI era, your edge won’t come from automation but from imagination. Human intelligence, grounded in empathy, curiosity, and creativity, must guide the choices you make: which problems to solve, which risks to take, which ideas to build.
