Where do I begin?

photo by Maria Kostyleva (IG:masha_and_film)

If you work in Data & AI (the exact job title does not matter), your days tend to look surprisingly similar:

You’re officially part of two or three important projects. The kind that have names, roadmaps, and stakeholders who care deeply. Alongside them, there’s a long tail of smaller initiatives: POCs, ad-hoc analyses, “quick questions,” experiments meant to take a week that somehow never really ended.

Your calendar?

A living organism of its own. It’s constantly hijacked by brainstorming sessions: “Let’s explore the potential of AI here.”

 “Can data help us make better decisions there?”

 Everyone seems to suffer from a mild but persistent case of FOMO, and no one wants to be the team that “missed the AI wave.”

If you’re ambitious, you’re probably also enrolled in some kind of leadership program. Not because you had free time, but because it felt like the right move. Future-you would thank present-you. Or you hope so.

At the same time, a quiet question keeps coming back:

Do I really want to stay in a big organization forever?

Maybe. Maybe not.

So you sign up for a startup founders’ bootcamp. Just to explore. Just in case. You tell yourself it’s optional—until it isn’t.

And because life enjoys irony, you’re also learning a foreign language. You actually need it now. For work. For people. For situations you didn’t anticipate a few years ago.

Projects, meetings, leadership, startups, languages. All running in parallel.

What keeps everything from falling apart?

Not productivity hacks.

Not longer hours.

A single, slightly unglamorous superpower:

𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧.

And here’s the part many people skip: you don’t have to figure this out alone.

When everything feels equally important, talk to someone who has been there before. A more senior colleague can help you zoom out, spot what truly matters, and identify what can wait or be dropped entirely. And if you don’t have such a person around you at work, find a mentor outside your organization. An external perspective is often even more powerful.

In Data & AI complexity is a given.

Clarity comes from prioritization and from learning to ask for guidance when you need it.