Most mother-daughter duos bond over brunch, travel, or binge-watching the same series from different time zones. We do that too — but somewhere along the way, our phone calls started being dominated by one topic: AI. How our organizations are adopting it, where it’s working, where it’s not, and what it looks like from opposite sides of the Atlantic. We realized we had a lot to say. So we decided to write it down.
Welcome to Transatlantic Intelligence.
Who we are
We should probably introduce ourselves.
Suzanne here. I run Customer Experience at CORAS, where we build AI for government. Before this I spent a decade in customer success leadership at SaaS companies, and before that I was in finance — Fannie Mae, Gartner, BP. I came into tech from the business side, which means I think about AI the way the people buying it do: does it work, and does it solve a problem that matters?
Here’s what I see from where I sit. There’s no shortage of push from the top. Executive orders, OMB memos like M-25-21 telling every agency to build an AI strategy, action plans called “Winning the Race.” The mandates are real. But on the ground? Legacy systems, tight budgets, cultures that resist change, and people who need to get through their day. Turning a memo into a Monday morning workflow is hard.
You can go about it two ways. Spend big on a sweeping modernization — or do what we do: find a specific use case that matters to real people, deliver a quick win, and repeat. Over and over. That second approach is where I’ve seen change actually stick.
Government has already spent billions on data modernization projects, and there’s a real risk of repeating that pattern with AI — massive programs, long timelines, unclear returns. It doesn’t have to be that way. We’re finding that targeted use cases can deliver real value fast, without the price tag. In future posts, we’ll dive deeper into the adoption strategies we’ve seen actually working.
And I’m Adeline. I’m a Digital Innovation Trainee focused on AI at the European Stability Mechanism, one of the EU’s key financial institutions. Before that, I spent six months at the European Parliament — first as a Schuman Trainee in the Learning and Development Unit, then as an External Consultant working on data analysis and visualization. I was training Parliament staff on IT tools and analyzing their training data, which gave me a very concrete view of what digital adoption looks like inside a European institution: careful, methodical, and shaped by the needs of a multilingual, multinational workforce.
My academic background is in global governance and policy. I did a joint master’s through Luiss University in Rome and CIFE, studying economic governance, international affairs, and policy intelligence. My thesis used sentiment analysis and Python to study how global powers use soft power in Liberia — so I think about technology through the lens of data, institutions, and power dynamics. I also founded the first student association in CIFE’s history, focused on gender equity, which taught me a lot about building something from nothing inside an institution that wasn’t designed for it.
Where my mother sees adoption from the customer side — what works, what doesn’t, and how fast can you get value — I see it from the institutional side. How do organizations that operate by consensus, across languages and legal systems, bring in something as disruptive as AI? The EU doesn’t ask “how fast can we move?” It asks “how do we make sure we get this right?” Both questions matter. Neither side has a monopoly on the answer.
Why this blog
There’s no shortage of conversation about AI and government. Think tanks, LinkedIn, conferences, procurement offices — everyone has something to say. We think there’s room for one more: an honest, on-the-ground perspective that bridges the Atlantic.
Most writing about government AI falls into two camps. The US-centric view: Silicon Valley speed, government as either a laggard or a customer. The European view: regulation first — the AI Act, GDPR, digital sovereignty — as if the whole relationship with technology is a legal question. Both have truth in them. Neither tells the whole story.
We want to fill that gap. Not as policy analysts or academics, but as two people living this every day on opposite sides of the ocean. Suzanne sees the disconnect between the memos from the top and what it takes to make AI work in an agency. Adeline sees what happens when twenty-seven member states try to agree on what “trustworthy AI” means in practice. Together, we think we can offer something you won’t get from a white paper.
What to expect
This blog is a conversation — between the two of us, and with you. Some posts will be co-written, some by one of us with a response from the other. We’ll write about how AI adoption actually works in government, past the press releases. The cultural differences that shape technology decisions on each side of the Atlantic. What we’re learning inside our own institutions. And the human side of it — the people who have to make these tools work on a Monday morning.
We’re not here to be neutral for the sake of it. We have opinions and we’ll share them. But we’re also curious about each other’s perspectives — and we think that tension is what makes this worth reading. A mother in Washington and her daughter in Luxembourg, talking through AI governance over Sunday video calls, is an unusual vantage point. We think it’s a useful one.
Join the conversation
If you work in or around government, if you care about how AI is reshaping public institutions, or if you’re just curious what happens when a European financial institution and an American govtech company try to figure out the same technology at the same time — this is for you.
We’re glad you’re here. Let’s figure this out together.
Yes, we use AI tools in our writing process — it would be a little ironic not to, given what this blog is about. But the perspectives, the opinions, and the occasional stubbornness are entirely ours.
Suzanne writes from the US. Adeline writes from the EU. The arguments at dinner are free.