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AI products require deep analysis at many stages — from opportunity assessment to post-rollout meta-analysis. In this article, I’ll share my personal experience on navigating the analytical complexity of product development and collaborating effectively with data teams. 💣 Here Comes the Challenge What happens when the data science team supporting you needs to answer open-ended questions and extract insights without clear guidance? What happens when you have to go through a dozen reports to get the full picture? What is the role of the Product person there? What happens when the team keeps analyzing and analyzing without ever reaching a conclusion? How do we balance the need for rigor with the need for decisions and action? And what if the analysis yields inconclusive results? 👩 From Data Science to Product Throughout my career, I’ve received analyses from many different perspectives — and earlier, I used to prepare and deliver them myself. I’ve worked with aca...

What I enjoy most about the role of a Product Owner is product discovery. Many of these sessions have ended in failure 😓—but looking back, I now see those failures as invaluable. Connecting the dots helped me realize that each misstep carried a lesson. Out of these experiences, I’d like to share the most common mistakes I made (and I believe many newbies do too). Leading by example in reverse !  💀 Using discovery sessions for validation: Let me explain by using a story.  When I go to yoga, my lovely instructor always says "Use your mind like a beginner. Approach each position as if you’re experiencing it for the first time." . In Greek it's called the αρχάριος νους. Αccording to  Shunryu Suzuki: In the beginner's mind there are many possibilities, but in the expert's mind there are few. I try to bring this principle into discovery sessions today—but it wasn’t always the case. In my early days, I’d tal...

Looking for some inspiration or probably how you could improve in the product space? I gotcha you! Here some of my bookmarked links: Opportunity assessment framework: https://artkai.io/blog/product-opportunity-assesment , by ARTKAI How to deliver  AI OKR s https://www.linkedin.com/posts/marilynika_the-ultimate-guide-for-setting-okrs-in-ai-activity-7126258168837394433-gA7U/?utm_source=share&utm_medium=member_desktop , by Marily Nika https://www.romanpichler.com/blog/okrs-and-product-roadmaps/ , by Roman Pichler How to prioritize your roadmap items https://productschool.com/blog/product-fundamentals/ultimate-guide-product-prioritization , by the product school Product Management Glossary https://productschool.com/resources/glossary?utm_source=linkedin&utm_medium=social&utm_campaign=daily_glossary-ld_glossary , by  the   product sc...

Bridging the gap: Guiding stakeholders into the age of AI Transitioning to the AI era isn’t always easy—especially in more traditional, old-fashioned industries. People are often reluctant to change or may believe that “if something works, why bother fixing it?” But the product lifecycle tells another story. At some point, every product will need a drastic revamp, a re-introduction to the market, or a complete phase-out. Let me walk you through my journey, and then we’ll dive into the practical aspects—which are, honestly, much more interesting! Who Am I?  👋 Hi folks! I'm Danai Aristeridou—a Product girl who landed (somewhat organically) in AI product management from a data science (DS) background. I still remember my early days in a hands-on DS role. Stakeholders struggled to understand what we were doing. There was a lack of trust and a need for control. We had to educate them, build understanding step-by-step, and iteratively move toward our goals. It wasn’t always smooth, but...