renépupala®

Getting the most out of AI means continuously experimenting as the boundary moves: probing the limits of context, building harnesses, learning the strengths of each model and continually questioning what needs to be specified, controlled or checked, and what can safely become autonomous.

More importantly, it means questioning the limits you have placed on your own imagination: what you assume the model can or cannot do, what feels too ambitious to attempt and which parts of your work you still believe must happen in the old way. Real AI fluency means staying loose enough to discard yesterday's method and learn the frontier again before the wave hits you from behind. If you are still using AI mainly as autocomplete, you need to wake up.

Taste is a unique expression of your experiences, skills and opinions. A model can generate options, but it cannot supply that point of view for you. Good AI design therefore needs intentionality: rich context, strong references, careful selection and repeated refinement.

It also requires granular controls. Good design is in the details, and it is often easier to tinker with those details directly than to describe them perfectly in another prompt. One-shotting produces a possibility. Turning it into design is still your job.

Better simplicity comes from exploring competing paths, understanding why they work or fail, and then cutting back with intent. That means going deep enough to understand the wider system, follow the consequences of each decision and see where the complexity actually goes.

You go deep so the product, experience or explanation can become simple for everyone else. Without that depth, complexity rarely disappears. It is usually just pushed somewhere less visible, often onto the user.

AI will automate narrow, finite knowledge work first. The durable advantage is combinatory depth: enough mastery across product, technology, design and people to make sound decisions across them, and to see around corners that no single discipline can.

That combination lets you challenge a specialist not by knowing more within their particular lane, but by bringing credible perspectives from beyond it. Each competency changes what you can see in the others, revealing connections, consequences and second-order effects that expertise in only one direction can miss.

Someone can use AI every day and still use it badly. Effective adoption requires fluency: understanding context management, how models behave, the difference between agents, tools and skills, and where autonomy helps versus where verification still matters. Without those foundations, usage creates the appearance of adoption without the improvements in outcomes, efficiency or quality that should define it.

Most organisations therefore have a training and knowledge problem before they have an enthusiasm problem. And if the underlying process is broken, adding AI does not fix it. It lets the broken process operate faster and at greater scale.