danai's space

This is a more personal article, aiming to share my journey toward becoming a product professional. I hope you find it inspiring. Let’s start!😉 From my early days at university, I was good at organizing initiatives and managing projects. Whenever I was assigned to something, I truly owned it. I felt a strong sense of responsibility and took care of it until I saw it take off. Still, I wasn’t sure I had found my true calling. I thought project management might be a good fit, but I first wanted to experience working on projects and large initiatives before jumping into organizing them. I started my career while still pursuing my Bachelor’s degree in Computer Science. Early on, I was mainly involved in web development and system administration, a period that lasted around two to three years. During that time, I worked mostly in very small companies, trying to figure out who I was professionally. One advantage—and at the same time a challenge—of working in such companies is that you get...

One of the toughest moments is when you have to kill your product 💀—when you see that your product is failing.  In this article, I am sharing my piece of advice about reading early on the signs, potential challenges that I have encountered in the AI product development, and last but not least, mitigation strategies! 🔎 Reading the signs A few signs that indicate your product should be removed from the market: Low performance in primary KPIs:  Measuring performance from day one is crucial, especially if your product is costly. When ROI is significantly low and your initial impact estimates are far off—for example, when technical costs are much higher than the value the product brings back to the company—it’s a strong indicator that the product isn’t viable. Post release analysis is absolutely necessary to indentify potential improvements before deciding to retire the product. The company is changing direction: Strategic decisions beyond your control can influence the sur...

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...