Across organisations, AI adoption rises in dashboards but stalls in day-to-day behaviour. Teams experiment, run pilots, and launch “innovative” initiatives that quietly fade out. The pattern is consistent: most AI projects die before they ever change how people actually work. Not because the models are weak. Because the experience isn’t usable.
AI is often conceived top-down, but it only lives bottom-up. That is where the gap appears. AI does not succeed because it can. It succeeds when it feels like the most natural, logical way to get work done.
Most AI projects start with fanfare and end in silence. The technology typically does what it promised in a demo. What’s missing is usability in the real workflow. Employees are not waiting for another tool, another login, another browser tab. They are trying to get through their day with fewer obstacles.
That is where many organisations go wrong. AI is often built for the organisation, not for the individual user. It feels like something added on top of existing work, not something that replaces and simplifies it.
In practice, this shows up in a few consistent ways. AI lives in a separate environment instead of inside the tools people already use. It appears at the wrong moment in the day. It requires too many steps to get value. And the output quality varies, which erodes trust. Without trust, no new habit forms.
On top of that, many implementations are rolled out top-down, without clear ownership inside teams. AI remains something people are “trying out” instead of “using to do the work”. The outcome is predictable: innovation that sounds strategically important, but changes very little operationally.
Strategically relevant. Operationally irrelevant.
Usability is often confused with a nice interface. In reality, it is about minimising friction. Friction is anything that creates irritation, doubt, or extra cognitive effort. When you ask employees why they don’t use a new AI feature, they almost never mention the model or infrastructure. They say it takes too long, it doesn’t show up where they work, it appears at the wrong moment, or it doesn’t feel reliable yet.
That is the critical moment. Usability is what determines whether someone thinks, “this makes my work easier” or “this will cost me time”.
Organisations that implement AI successfully optimise less for technical sophistication and more for human behaviour. Usability is not a cosmetic layer. It is the core engine of adoption.
Consider a simple maturity model for AI in organisations: from prompts to templates, to workflows, and finally to agents. It is tempting to treat these as technical milestones. But there is a crucial, often-missed insight: You can only move to the next stage when the behaviour of the previous stage is truly accepted.
If prompts are inconsistent, you cannot build strong templates. If templates are unused, workflows have nothing to stand on. If workflows are chaotic, you cannot trust agents to operate autonomously and reliably.
A maturity model only works when every step is designed for usability:
AI adoption is therefore not just a technological roadmap. It is a behavioural growth model, phase by phase.
A recent article in the Dutch business newspaper Het Financieele Dagblad (FD), “AI-lessen uit de boardrooms van Ahold, ING, KLM en NS”, illustrates the same pattern at scale. AI projects tend to fail the moment they are pushed top-down as something employees must comply with.
This is not just a corporate problem. Mid-sized businesses experience exactly the same thing.
Teams start trusting AI when it genuinely makes their work lighter, not when it merely sounds strategically sensible in a board presentation. For marketing, sales and customer teams, “strategic alignment” is not enough. They judge AI on whether it helps them close the gap between ambition and execution.
Top-down creates implementation. Bottom-up creates adoption. Usability is the bridge between the two.
When something works smoothly, appears at the right moment, and delivers immediate value, usage follows almost automatically.
Moving AI beyond perpetual experimentation is more practical than it looks. It is less about adding features and more about making a few disciplined choices that naturally accelerate adoption.
First, AI must become the fastest route to a result, not an extra route. When AI clearly saves time instead of adding steps, people choose it without being pushed.
Second, users need to be involved in building. The teams closest to the work know exactly where things get stuck and where AI can create real leverage.
Third, AI should be introduced at real moments in the workflow, not in isolated sandboxes. After a client conversation, before a presentation, during a performance review or campaign analysis.
Fourth, success should be measured by behavioural adoption, not just by features shipped. The critical question is not: does this work technically? It is: are people using this multiple times per week?
Finally, once AI reliably works, old processes have to be deliberately retired. As long as the old way of working remains fully available, AI will rarely become more than an experiment. Replacement creates adoption; addition rarely does.
Designed this way, AI shifts from “something we are trying” to “the way we work”. That is where real transformation begins.
For B2B organisations, the stakes are high. Marketing, sales, and communication teams are under pressure to produce more, faster, with fewer resources. All while staying perfectly on-brand and data-driven.
In that reality, AI that merely exists in a playground is not enough. AI has to sit inside your core workflows: campaign planning, content creation, sales enablement, reporting and optimisation.
At Marcus, we design AI around that principle. Our platform is built to:
The goal is simple: move AI from concept to habit, so it becomes the most natural way for teams to work.
AI adoption does not fail on models, frameworks, or infrastructure. It fails when usability is an afterthought.
The crucial questions are no longer: “What can AI do?” or “How advanced is our stack?”
The real questions are: “How easy does this feel to use?” and “How irrational would it feel to go back to the old way?”
Usability is what separates an AI project that sounds impressive in a presentation from an AI system that quietly delivers value every single day.
That is the real challenge for 2026: not building more AI, but building AI that people genuinely want to use.
