Building AI in a Regulated Enterprise: Lessons from UBS Wealth Management - Nondas Virvikatis
Yoni, our CEO & Co-Founder, recently sat down with Nondas Virvikatis, who leads product managers building AI capabilities for financial advisors at UBS Wealth Management Americas.
A few months ago, I started recording a podcast with leaders and influencers in our space, specifically ones who have worked to build something of substance with AI.
The latest episode I recorded is with Nondas Virvikatis. Nondas and I have known each other for a while, and I always enjoy speaking with him about AI, where things are going, what is actually working, and what is still painful. You can jump straight into the episode NOW: YouTube, Spotify and Apple Podcasts.
Nondas leads a team of product managers at UBS Wealth Management Americas, building AI capabilities for financial advisors in the US. Before that, he spent years across product, analytics and data, including in financial services and growth-stage startups.
AI is not replacing the financial advisor
When most people hear “AI in wealth management”, their mind probably goes to ChatGPT managing their money, buying and selling stocks, and deciding what they should do with their portfolio.
That’s not what Nondas and his team are building.
Wealth management is a relationship-heavy business. Financial advisors understand the client’s financial goals, tax situation, estate planning needs, personal context, risk appetite and life goals. In the US, Nondas explained, financial advisors often operate almost like entrepreneurs. Their client relationships are deeply tied to them.
So the goal is not to replace the advisor.
As Nondas put it, “AI is not going to replace the humans in this industry, but people who use AI will replace the people who don’t use AI.”
That framing is important. AI is not the product. The financial advisor is still the product, in many ways. AI is the leverage.
This is similar to how I think about AI in analytics. We’ve written before that throwing away BI is a bad idea, and that blindly giving business users an unchecked chatbot can be dangerous. The better path is usually augmentation: help humans do the work faster, better and with more confidence.
That same idea applies here.
A good advisor still needs to sit with the client, understand them, calm them down when markets are chaotic, explain tradeoffs, and help them make decisions. AI can help the advisor prepare better, find information faster, personalize service and operate more effectively.
But you still want the human in the loop.
Generic AI gets you part of the way. Specialized AI gets you further.
We also talked about the difference between generic AI tools, like Microsoft 365 Copilot, and more specialized tools built for a specific workflow.
Nondas sees both as valuable.
Generic AI tools can help with productivity. They can write, summarize, help create questionnaires, draft interview questions, prepare for internal presentations, process information and generally accelerate knowledge work.
But in a regulated, high-stakes environment, “good enough” is not always good enough.
There are cases where a generic tool gets you 80% of the way there. That’s useful. But there are also cases where you need much higher certainty, stronger control, better data integration, better evaluation and workflow-specific behavior.
This is very aligned with what we see in data and analytics. General-purpose AI is amazing, but when you want to connect it to enterprise data, definitions, dashboards, metrics and permissions, you need more than a generic chatbot. You need context, governance, evals, observability and a workflow that understands the job to be done.
That’s why we spend so much time thinking about semantic layers for AI, evals and how to keep a close eye on AI behavior with tools like LangSmith.
The same is true in wealth management.
Copilot is useful. But if you want AI to support financial advisors in specific client workflows, with firm-specific data, policies, expectations and risk controls, you need specialized products.
The Chief AI Officer is not just a fancy title
We talked about the rise of the Chief AI Officer role. Someone on LinkedIn once joked that having a Chief AI Officer today is like having a Chief Electricity Officer 100 years ago.
Funny. Also, not completely wrong.
But in a large enterprise, especially a regulated one, the role makes sense.
Nondas described it as a hub-and-spoke model. The central AI organization helps with tooling, training, infrastructure, responsible AI, governance, communities of practice and avoiding 20 teams building the same thing 20 times.
Then each business area figures out how to apply those capabilities to its own workflows.
That balance matters.
If everything is centralized, AI becomes too detached from the business. You get platform teams building impressive capabilities that no one uses.
If everything is decentralized, every team reinvents the wheel, risk management becomes impossible, and the enterprise ends up with chaos.
The winning model is somewhere in the middle: central enablement, local execution.
That feels right to me.
We’ve seen similar patterns in data. A central data team can build the foundation, governance and core assets, but the business value usually happens closer to the workflow. The people who understand the work need to be close to the AI.
Adoption is a product problem
One of the most interesting parts of the conversation was not technical at all.
Nondas emphasized that building the technology is not enough. You need communication. Training. Repetition. Champions. Use cases. Feedback loops. Follow-up.
Financial advisors, as he said, are wired to sell. They are not wired to adopt new technology just because a product team shipped it.
This is such an important point.
Many enterprise AI efforts fail not because the model is bad, but because the rollout is bad.
People need to understand:
Why should I use this?
How are my peers using it?
What does “good” look like?
When should I trust it?
When should I not trust it?
What happens if it fails?
Nondas and his team spend a lot of time close to the field, speaking with users, collecting feedback, identifying champions, and sharing use cases across teams.
This is exactly the mindset I think enterprise AI needs. You cannot just launch and hope for the best. AI adoption is not a Slack announcement.
It’s a product motion.
And the users of internal AI tools should be treated like customers. You look at usage. You segment users. You interview power users. You interview people who dropped off. You ask what changed. You figure out what triggered adoption or abandonment.
That applies whether you’re building AI for financial advisors, analysts, salespeople, product managers or engineers.
The technical problems are still very real
Of course, there are also plenty of technical challenges.
Nondas called out a few:
Data availability
Data quality and readiness
Performance
Evals
Feedback loops
Fallback processes
Risk controls
That list should sound familiar to anyone building AI inside an enterprise.
The application layer may look simple. A user asks a question. AI gives an answer. Magic.
But underneath, there is a lot of plumbing. Do you have the right data? Is it permissioned correctly? Is it fresh? Is the answer good enough? How do you know? What happens when the model is wrong? How do users report issues? Can they fall back to a traditional process?
This is why I keep saying that AI for enterprise workflows is much harder than demos make it look.
A demo can be built in a weekend.
A production-grade enterprise AI system needs to survive contact with messy data, real users, security teams, compliance teams and business expectations.
As we wrote in (Almost) no AI in production, there’s still a huge gap between experimentation and production. Nondas’ experience reflects that gap, but also shows how serious organizations are starting to cross it.
Regulated environments need levels of freedom
One of the tensions we discussed is the gap between what you can do at home and what you can do inside a large bank.
At home, you can vibe code freely. Open Claude, Cursor, Gemini, ChatGPT, connect things, upload files, build little tools, burn tokens, break stuff, fix stuff, start over.
Inside a major financial institution, that freedom has to be constrained.
And rightfully so.
Nondas described a useful mental model: the broader the impact, the stronger the controls should be.
If you are using AI for yourself, the risk is smaller. You can have more freedom.
If you are building something that affects your team, you need more structure.
If you are building something that affects clients, advisors or the broader firm, you need real controls, approvals, model risk management and governance.
That makes sense.
The trick is not to eliminate freedom. If you do that, you kill innovation.
The trick is to match the level of control to the level of risk.
Small blast radius, more freedom.
Large blast radius, more control.
That is probably the right way to let enterprise AI grow without letting it blow up.
Copilot, Gemini and the “can I do this faster?” habit
Nondas also shared a very practical habit that I loved.
Every time he is about to do something on his computer, he asks himself whether he could do it faster or better with AI.
That’s it.
That habit alone probably separates people who really benefit from AI from people who just occasionally open a chatbot.
He uses AI to draft questionnaires, prepare interview questions, work on internal presentations, process data and support both professional and personal workflows. In his personal life, he uses Gemini heavily because his personal ecosystem is already in Google. At work, his professional context lives more in Microsoft tools.
His comment was great: “your imagination and your connectors, I think, are your two limitations.”
That is exactly right.
The model matters. But the connectors matter too.
If AI has access to your calendar, files, documents, messages, data and workflows, it becomes dramatically more useful. If it is disconnected from everything, you spend your life copying and pasting.
This is why the next few years will likely involve a massive amount of work around APIs, connectors and workflow integration.
The chatbot window is useful. The connected AI system is much more useful.
Vibe coding is addictive
We ended up talking about something I think many of us are experiencing, but maybe don’t always admit.
Vibe coding is addictive.
Nondas described building personal tools with Claude and Claude Code, including an asset aggregator across accounts in Greece and the US, and even a networking database that could help match people in his network based on what they need.
I immediately understood the feeling.
You sit there. You prompt. It builds. It fails. You prompt again. It gets closer. You prompt again. Suddenly your wife is telling you that you need to leave, and you’re saying, “just one more prompt.”
It feels like an arcade machine, except instead of putting in coins, you’re burning tokens.
There is something very powerful about having a machine work with you in real time. It is not passive software. It responds. It tries. It improves. It sometimes frustrates you. It sometimes surprises you.
And yes, sometimes it goes too far.
Nondas made a great point about models overthinking or continuing too long before checking whether they are on the right path. His approach is to constrain the work: do this for three profiles first, let me review it, then scale it.
That’s a good lesson for all of us.
Treat AI like a junior teammate. Give it enough room to be useful, but don’t let it run 500 miles in the wrong direction before checking in.
AI is not cheating
In the lightning round, I asked Nondas for an AI myth he would bust.
His answer was that people should stop feeling uncomfortable admitting they used AI to get to an outcome.
I strongly agree.
There is still a weird stigma around using AI, as if using it means you didn’t really do the work.
That’s nonsense.
Of course, don’t plagiarize. Don’t blindly submit something you didn’t review. Don’t pretend you wrote every word by hand if you didn’t.
But using AI to think, draft, analyze, improve, summarize, structure, code, research or prepare is not cheating.
It’s using a tool.
In a few years, I think this stigma will look silly. Just like no one says you cheated because you used Excel instead of doing math on paper.
The question won’t be whether you used AI.
The question will be whether the output was good.
The next five years: connectors, connectors, connectors
When I asked Nondas for one AI prediction for the next five years, he made it conditional.
If we invest more in APIs and connectivity between applications, AI will really take off.
I think he’s right.
Models will keep improving. But for enterprise use, the bigger unlock may be access. AI needs to connect to the systems where work happens.
CRM. BI. Data warehouses. Documents. Email. Calendar. Ticketing systems. Product tools. Financial systems. Workflow engines.
Once AI can securely and reliably operate across those systems, the value changes.
It stops being a chatbot.
It becomes a worker.
Not a replacement for humans, at least not in most of the enterprise workflows we discussed. But a worker that can help humans move faster, make better decisions and spend less time fighting the machinery around them.
That’s the direction we’re all heading.
I thank Nondas for spending the time with me on the podcast. You can listen to our conversation on YouTube, Spotify and Apple Podcasts.
In the meantime, if you would like to learn more about Solid, reach out to us.
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