danai's space

ProductCon London 2026 really nailed it. One of the best conferences I’ve attended — even virtually. I genuinely felt energized and inspired by the talks. Still digesting everything. Here’s what really stayed with me: The importance of human-in-the-loop in AI systems to build trust. Ashley Nutter framed this in a practical way—clarifying when humans step in, what their role is, and what happens after they intervene. How PMs can inspire truly autonomous teams even without formal authority . Nilan Peiris was a strong reminder that real impact comes from influence, clarity, and trust — not title. The idea that ChatGPT is the product manager your customers are already talking to — the “shadow PM” who understands their pain points. Pavel Fabrikantov’s point about AI discoverability really made me rethink a few things. The shift of product managers toward becoming builders, and the need for real frameworks to drive AI-native transformation . Great perspective from Carlos...

On Friday 19/12/2025, I was invited to a university workshop at the Computer Science Department of the Aristotle University of Thessaloniki to talk about what a Product Manager / Product Owner does and to share the key aspects of product management. The goal was simple: to inspire students by introducing them to career paths they might pursue in the future. I truly love workshops. I love the energy, the passion, and the interaction with people. I also deeply enjoy creating presentation decks (confession time: it’s my guilty pleasure 😄). 😮 Key realization: These students were in their third year of studies , yet they were not really familiar with how an AI model is built. Given that, we decided to start from scratch. I mainly focused on real-world examples of AI-powered products that were already familiar to them, using those as an entry point to discuss product thinking, user needs, and business impact. 👉 What do you think? Should students...

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...

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...