Cristina Ornato

Your story: how did you end up in product?

I started my career in strategic consulting in Milan, in a very dynamic environment surrounded by ambitious, self-driven people. What I enjoyed most was the problem-solving mindset: looking at things as opportunities to make them better. I was genuinely curious about how different businesses worked, and I loved being exposed to challenges that were sometimes very far from my own field.

Consulting gave me a strong analytical foundation, which I later brought into my first corporate roles as a Business Analyst. Over time, I became increasingly interested in the product side of the business. I had my first opportunity to move into product management in a corporate environment, initially driven by curiosity, but quickly discovering how much I enjoyed it.

For my next move, I was looking for an environment where I could have a more direct impact on the product I was working on, with a more bottom-up approach and closer involvement in shaping product decisions. I wanted to work in a faster-paced environment, where I could get ever greater ownership and be involved in the entire product journey: from identifying a problem to delivering a solution, experimenting, and understanding whether it actually made a difference for users.

That's what eventually brought me to Vinted. It was a very conscious choice, also on a personal level. I've always been fascinated by the second-hand/vintage scene and had been using pretty much every resale platform out there. So when the opportunity came, something clicked. I could combine my interest in problem-solving and product with something I genuinely believed in.

And that, for me, is where the magic happened: being able to solve meaningful user problems, see the impact of my work through experimentation, and contribute to a mission I could personally relate to "making second-hand the first choice" for people.

How do you define product success?

I think product success is ultimately defined by adoption, but also by retention: how often people come back and how consistently they engage with your product over time.

I don't have one favourite metric. I believe success comes from interpreting all the signals available to us: what the data tells us, but also what we hear through surveys and user interviews. It's about building a complete picture, staying close to users, and understanding what a new feature or product change actually means to them.

For me, it's the combination of these different perspectives that ultimately tells you whether you've built something successful.

What's your biggest PM lesson or mistake?

1. Treat experimentation as one of your greatest allies.

Never underestimate the value of A/B testing. Even when a product has been carefully designed and backed by extensive user research, there's always something new to learn once it goes live. I've seen experiments uncover unexpected bugs, overlooked user behaviours or issues affecting specific markets that we hadn't fully anticipated.

That's why I believe in testing every meaningful iteration and working closely with your team to build efficient experimentation cycles. The goal is to shorten the time between shipping, learning and improving.

2. Connect the dots between all the tools and signals available to you.

I believe the strongest product decisions come from combining different sources of insight throughout the product (or feature) launch cycle.

Start with user research to validate your assumptions and understand the problem. Use statistical experimentation to measure the actual impact of your solutions. And, most importantly, develop a strong understanding of your metrics: don't just look at whether a result is positive, negative or neutral, but understand what it actually means.

One thing I've found particularly valuable is creating fast feedback loops by combining quantitative and qualitative data. For example, running in-product surveys alongside an experiment allows you to understand the story behind the numbers.

I often find it extremely useful to bring user comments and experimentation results together, so you can interpret them side by side rather than looking at each signal in isolation.

Ultimately, it's the ability to connect these different perspectives that helps you understand not just what happened, but why and what you should do next.

How is AI changing product management?

I see AI as an indispensable ally in my work. Of course, it makes me faster and more productive, especially when it comes to things like writing, structuring information or analysing data. But I think the real step change happens when you move beyond using AI as a tool for individual tasks and start building a system around it.

That's actually something I've invested in. I decided to work with an AI coach, which is something I'd genuinely recommend to others. Having someone help you develop a structured approach makes a huge difference: it allows you to identify what works for you, tailor it to your actual needs, and build habits and systems that you can use consistently rather than experimenting with AI in a more ad hoc way.

In my case, we've built what we call a second brain: a system that helps me keep track of the context I manage across projects, documents and different communication channels. I've also created specialised AI agents that I treat almost like interns, each with their own job description and responsibilities - for example, one focused on reporting and another on analysis. I've even personalised them with names and specific roles.

What I find particularly powerful is that I didn't necessarily have a lot of repetitive tasks to automate. The real challenge was managing context: making sure I don't lose track of information across different channels, connecting the dots and being able to build on everything that's happening day to day.

There's a metaphor I really like: using AI occasionally is like talking to a very intelligent stranger. Building a system around it is more like having an incredibly smart colleague who knows your context, remembers what you've been working on and can continuously build on that knowledge.

And that's where I see the biggest opportunity: not just saving time on individual tasks, but having an AI ally that helps me manage complexity, think more effectively and become significantly more productive over time

What would you tell a PM just starting out?

1. Don't be afraid to experiment and stand out.

Don't just optimise your CV: create something people can actually interact with. I remember someone at my university who created an animated video as his CV. Today, AI makes it easier than ever to experiment, build websites, prototypes or even actual products before you're officially in the role. Use that opportunity to showcase how you think and what you can do.

2. Invest in your network and find your community.

Find the communities around the field you want to work in. In Product, for example, there are communities like ProductTank, as well as talent networks such as Nova Talent in Spain. Surround yourself with people at different stages of their careers who can exchange ideas, share experiences and help you navigate your next steps.

3. Your first choice tends to stick.

This might not be the least optimistic piece of advice, but I've increasingly noticed that your first role and area of expertise can have a lasting influence on your career trajectory. So, if you have the opportunity, be intentional about the roles, companies and industries you choose. Think not just about your first job, but about where you could see yourself growing over the next five years, at least.