For years, brand guidelines leaned on adjectives such as “bold,” “premium,” and “customer-first.” Human teams made those words work because they absorbed context over time. The people in the room knew when “transparent” felt reassuring and when it felt reckless. That unwritten layer, argues a new MarTech analysis, is exactly where AI-led execution breaks down.
The challenge is not that AI cannot read brand language. It is that AI can act on a vague instruction thousands of times before a human notices the drift. One confused employee may misinterpret a guideline for a single customer. A confused model can misread the same rule for 10,000 customers before lunch.
The human middleware disappears
Every brand team has a “Sarah” — the tenured manager who knows that “empathetic but efficient” means one thing for an angry customer and another for a confused one. She remembers exceptions leadership made, even when those exceptions never made it into the brand book.
When AI takes over a decision chain, Sarah is no longer between the guideline and the customer. The gap that tenure and instinct once filled is simply empty. Therefore, guidelines have to carry more of the judgment Sarah used to supply.
Brand moves from expression to behaviour
Traditionally, brand strategy was most explicit in marketing-controlled touchpoints: a logo, a tagline, a campaign. It got fuzzier as customers moved into service, billing, disputes, and renewals. Those moments were managed by culture and individual judgment.
AI changes that division. It recommends, adapts, escalates, and commits before a human reviews the action. So the brand has to answer behavioural questions, not just expressive ones:
- What does the brand do when two values collide?
- What does it refuse to do, even if it costs revenue?
- Who decides in situations the guideline never anticipated?
This echoes the “Canva principle,” where brands loaded logos, fonts, and colours into shared rails. That solved the static 10% of consistency. The harder part is tone under pressure, trade-offs, and exceptions — the judgment layer AI now forces teams to codify.
What marketing teams can do next
Marketers do not need to turn every subjective decision into a rulebook overnight. But they should identify the highest-volume AI touchpoints where an unstated assumption could scale into a customer-facing mistake. Start with decisions that are frequent, sensitive, or involve money — for example, whether a technically valid denial should still trigger a refund.
Then convert broad adjectives into observable behaviour. Instead of saying “premium,” specify what premium does: how quickly it responds, what language it avoids, which trade-offs it makes when speed and empathy conflict. The point is not to copy how humans always behaved. Humans are inconsistent. The opportunity is to extract the judgment of the best people, make the important parts explicit, and build systems that can carry it.
Source: MarTech
