Hi everyone! 👋

My name is Tonia and, after spending several years living and working in Copenhagen, Denmark, I am now based once again in my home city of Athens, Greece.

I’ve worked in the tech industry for more than a decade and, although my background is originally in arts and culture, the Future of Work movement won me over back in 2017. That’s when I transitioned into the world of HR — and I haven’t looked back since.

I consider myself an HR Solutions Architect at heart. I love looking at how people, processes, technology, and culture come together, and there’s hardly a People & Culture challenge, big or small, that I don’t enjoy digging into and trying to solve.

Outside of work, I love cooking and baking, propagating plants, spending time by the sea, discovering new spots and restaurants around the city, and having meaningful conversations with interesting people.

That last one is also part of what made me jump at the opportunity to lead the In Good Company Chapter here in Athens. I’m really looking forward to bringing HR & People professionals together locally, while also connecting, exchanging ideas and sharing knowledge with this wider community online.

I’m very happy to be here and look forward to getting to know many of you!

Take care and have a lovely summer! 🏖️

Most modern ATS platforms now include some form of AI matching, and for what the ATS was designed to do, that layer genuinely helps. The ATS was built to manage application flow, maintain an audit trail, handle compliance, and give the recruiting team one place to move candidates through the pipeline. AI matching sits on top of that and speeds up sorting, which is real value.

What still comes up in most conversations with talent teams is that the shortlist does not always convert into a strong hire. The pattern is usually the same. Matching engines compare the language of a CV against the language of a JD, and CV language was never built to carry a reliable signal of capability. A backend engineer writes "built event-driven services on Kafka" while the JD says "experience with distributed systems at scale," and the engine scores it as weak even though it is a strong fit.

The way I have started thinking about it is that this is less a failure of the ATS and more a mismatch between expectation and design. The ATS is doing what it was built to do. Capability signals have to come from a different layer, whether that is validated skill assessments, verified project work, or evidence from the platforms where the work actually lives.

When you evaluate AI matching layered on your ATS, what test do you run to check whether it is reading skill or reading vocabulary? Would love to hear what has worked for your team.

Thanks to everyone that joined us for our first chapter event! 😃

There are technologies out there that allow you to compare candidate resume with job descripton and then provide an analysis if the candidate is a good fit or not. LinkedIn is also doing it.

Do you think if we add Interview transcripts and then conduct AI analysis based on a set criteria that additional data point will further help in evaluating candidates

The higher up you get, the less honest feedback you get. Your team has worked out it's safer not to say it. By the time you notice, it's already been happening for a while.

I once worked for someone who would have lost it if anyone was honest with him. So nobody was. We'd nod in the meeting. Smile. Agree. And the second he left the room - pick apart everything he'd just said.We subtly resisted his ideas. Never fully trusted him. And he had absolutely no idea.

I think we've all worked for a leader like that.

Here are 3 things that can change it.

#1
Watch how you respond when someone pushes back.Not in a feedback session. In the meeting. When someone says something you didn't want to hear:

Do you get curious?

Or do you make it clear - without saying a word - that it wasn't welcome?

Your team is clocking that every single time. And deciding what's safe to bring you next.

#2
When the feedback feels vague - don't move past it.

"You can be hard to read." Or "People feel hesitant around you."

You hear it and you're not sure what to do with it. So you move on.

That's actually the real feedback. Just wrapped in something safer.

Ask: "Can you tell me more about what that looked like from where you sat?"

That one question tells your team you actually want to hear it.

#3
Make the change visible.

Your team needs to see something different - not just hear that you're working on it.

Pick one specific thing. Do it consistently. And tell them.

"I know I can move fast in a way that cuts people off. I'm working on that. If you see it happening - call me on it."

That sentence does more for trust than a year of asking for feedback ever will.

The managers and leaders people stay for/are motivated by are the ones who made it safe to give them feedback.

While AI is speeding up the process and overwhelming the teams leaders to make choices. One has to be skeptical what these technologies can deliver. I believe structured interviews conducted by talent teams who understand what to measure and score and how to conduct these interviews are still ahead of the curve. These fundamentals are statistically proven to deliver a positive outcome if done correctly.

Are you conducting structured interviews and scoring candidates?