I’ve recently become enamored with analog journaling. I’ve built a whole journal ecosystem: a leather cover that holds a small stack of different books including a monthly planner, a to‑do list scribbler, a commonplace book, my personal journal, and a design journal. I log everything from random thoughts to information I pick up while reading or researching, and ideas that surface throughout the day. After years of managing everything in digital tools, moving this onto paper has been unexpectedly enjoyable; the slower pace of writing long‑hand and the act of leafing through a tangible collection of my own thoughts feels like actual thinking time, not just organising time.

The more I move my own work onto paper, the more I notice how rare it is for teams to get that same kind of slowness together. As everyone is nudged to move faster with AI, the focus shifts to generating as many options as possible, summarising as much information as possible, and shipping as quickly as possible. A workshop can be a space to offer teams the opposite kind of experience: a deliberately slower space to work, think, and discuss. It creates the space to map problems, argue alternative perspectives, and sit with competing ideas long enough to understand them. Workshops can give people time where the default is human thinking, on its own, in public.

When AI is so readily available, it’s tempting to treat every problem like a prompt: describe it superficially, generate a list of answers, and move on to the next task. The problem isn’t that we lack information or answers; it’s that we don’t spend the time framing it well enough to generate the right ones. Design models like the double diamond all espouse the importance of identifying the right problem to solve. Workshops help us practice solving problems without outsourcing the thinking.

The more people reach for AI by default, the less practice they get at doing the hard parts of thinking themselves. It’s subtle; you still see ideas, documents, plans, but underneath them, the muscles that question, connect, and critique start to weaken. In practice, that’s the benefit of messy, human collaboration: clarifying what’s actually happening, what we know and don’t know, naming the tension points, tracing who is impacted and how. It’s sketching scenarios, writing out constraints, and testing ideas against lived reality instead of a model’s assumptions. It helps us achieve a clearer shared understanding of the problem we’re really trying to solve, and a direction the team actually believes in.

Sure, designing workshops this way might not intuitively seem to generate faster output. But they help build skills that are so necessary in an AI-forward work environment: asking better questions, identifying the right problem to solve, tolerating ambiguity, and keeping human judgment at the centre of the work.

My traveller’s notebook forces me to see where I’m reaching for speed instead of understanding. Workshops are how I establish the same kind of purpose with my teams. They don’t compete with tools or replace them; they act as an additional mechanism to slow down and think together, to stay with the work long enough that our judgment has a chance to show up.

If you want to experiment with this in your own context, a couple of questions can help: