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For most of the AI projects I run now, the hardest part is the data – whether it's accurate, current, and something a team can actually trust. Models, integrations, and the tools themselves have become routine. The data is where a project works or falls apart. Gartner has predicted that through 2026, organizations will abandon 60% of AI projects whose data isn't ready to support them.* That matches what I see in my own work. So I want to share how I work with data – collectin

I'm not an academic. But I work with researchers, and I've spent a lot of time in that world lately – through workshops at universities, and through longer projects where we go deep into building real AI-supported workflows for research teams. What I keep seeing is this: academia has an enormous amount to gain from AI tools. Not because AI will do the research for anyone – it won't, and it shouldn't. The years of expertise, the original ideas, the ability to ask the right que

When companies and organisations tell me they want to implement AI, the first thing that comes to mind is usually a training. A two-day intensive workshop, lots of material, lots of tools – and done. I understand the logic. A training is something concrete. It has a date, an agenda, an end. The problem is that the results rarely stick after two days. Over the past several months, I've been working with organisations in a different model – multi-month projects where we meet re

Over the past two months, several people have asked me the same questions: "Monika, where do you even start with AI?" , "Is there a course you'd recommend?" , "How did you actually learn this stuff?" So I decided to write it all down in one place. This is where things stand as of March 2026 – AI moves fast, and some of this might look different a year from now. 0. Where I'm coming from I spent many years working in product management – at corporations and startups, in Poland