Inside Vibe Design: An Agency's Field Guide to Designing with AI in 2026
Why 92% of companies use AI with no measurable results, and how to actually use it in the design process: research, the right tools, brand consistency.
"Vibe design" is the term circulating to describe how designers now work with AI: prompts instead of wireframes, generation instead of manual iteration, an interface born in minutes instead of days. The problem is that most agencies and in-house teams use it this way: as a shortcut on time, not as a change in process. The result is the same as every other superficial adoption of AI. 92% of companies already use AI tools, and for most of them it produces no measurable result. It's not a model problem. It's a method problem.
Vibe design works when it replaces the mechanical part of the work, not when it replaces the thinking that precedes it. A designer who generates twenty screen variants in ten minutes instead of two days has gained time. If those twenty variants come from no research into who will actually use that interface, the time gained just produces more wrong options, faster. AI accelerates whatever you feed it: a good brief becomes a good output faster, a vague brief becomes noise at scale.
Where AI genuinely changes design work
The real advantage of AI in design isn't in the final phase, the polished screen ready to show a client. It's in the phases that used to get skipped for lack of time: research into real users and prototype testing before a single line of production code gets written. A team that used to interview three users because of budget constraints can now transcribe, synthesise, and cross-reference twenty interviews in the same amount of time, with a language model working on raw material instead of a hand-made, already-filtered summary.
The same applies to iteration after launch. Real user behaviour data, collected once a product is live, can now be read and interpreted far faster than before: where a user drops out of a flow, which onboarding variant converts better, which copy inside an interface causes confusion. Design that drives growth is the kind reviewed constantly against real usage data, not the kind approved once and never touched again: it's the principle behind every UI/UX Design project we build, with or without AI in the mix. AI doesn't replace research here: it makes research possible at a scale that used to require a far bigger budget.
The right tool for the right problem, not one model for everything
The second mistake, after skipping research, is using a single model for every task, usually whichever one is getting the most attention that month. A generic language model is fine for drafting an email. It's not fine for generating design content that requires precise reasoning, or for synthesising hundreds of up-to-date sources on a visual trend, or for running a voice flow that needs to sound human in front of a client.
In the AI Integration projects we run, we choose the tool based on the problem, not the other way around: Claude and Anthropic's models for complex reasoning and content that requires precision, Perplexity when up-to-date synthesis of external sources is needed, ElevenLabs when a project calls for synthetic voice indistinguishable from a human one, n8n to connect all of it to the systems a team already uses, without anyone having to copy data by hand from one tool to another. An agency that sells "we've integrated AI" without specifying which tool does what, and why, is selling a subscription, not a process.
Where vibe design fails
The most common failure isn't technical. It's a team that starts designing "by feel," generating screens until one looks right, with no explicit criterion for judging it. Without research upstream, "looks right" just means it resembles something already seen elsewhere: the direct consequence is a wave of interfaces that all look alike, generated from the same visual style the models absorbed during training. Vibe design without process produces consistency with the rest of the web, not consistency with the client's brand.
The second failure is inconsistency across touchpoints. A team that uses AI to rapidly generate variant after variant, without a system of applicable rules like the ones we build in Branding & Visual Identity projects, ends up with an interface that looks different from the website, different from the sales materials, different from social. Every inconsistency is a point where the user trusts you a little less, and generating more variants faster doesn't solve that problem: it multiplies it.
What doing vibe design well actually means
I spoke about this at TEDx Hensemberger in Monza, where the central point was the same one that applies to design: AI doesn't remove the need to know what you're doing, it makes it more visible. A team with no process, handed a tool that speeds everything up, produces the same mediocre work faster than before. A team with a solid process, handed the same tool, compresses weeks of mechanical work into days, and reinvests that time in the part no model can do on its own: genuinely understanding the user's problem before designing the solution.
Doing vibe design well means treating AI as a multiplier of research, not a substitute for it. It means choosing the right tool for each phase instead of relying on a single model for everything. It means maintaining a set of brand rules that AI follows, instead of letting every generation reinvent the style from scratch. The design that converts in 2026 isn't the design produced faster: it's the design guided by the same criteria as always, with AI removing the mechanical work that used to sit in between.
Want to figure out how to bring AI into your design process without losing consistency? Let's talk.
