In a wide-ranging interview with Insider.gr, Dimitris Balaouras, co-founder of helvia.ai, argues that 2026 is the year AI stops being a side experiment and becomes part of how a company actually runs. His one-line answer, if you only keep one: the quality of your AI implementation is directly tied to the quality of your data. And the organizations that pull ahead won't be the ones running the most pilots — they'll be the ones that design a single transformation strategy with clear governance, good data, and measurable targets.

The original interview, conducted by Niki Papazoglou, was published in Greek on Insider.gr. We've translated the full conversation into English below.

AI is everywhere in the public conversation now. Where are we really today in terms of business maturity? And what's the core difference between "experimenting with AI" and "producing business value with AI"?

The Greek market has moved past the phase of AI demos. What's needed now are companies that already operate as AI-native — and that's the real threshold of 2026.

Over the last two years we saw thousands of pilots and experimental chatbots. That phase was necessary, so we could all understand what the technology can and can't do. At helvia.ai we see the shift in the conversation with leadership clearly: today they don't ask us "what can AI do." They ask us "how do we govern it, how do we control it, how do we put it into production."

The difference between experimentation and business value isn't technological. It's architectural. A pilot is interesting. An AI system that runs every day inside an organization's critical workflows is what actually drives its transformation.

When you treat AI as one more project, you stay in experimentation. When you treat it as a new operating layer for the business, you gain a real competitive advantage.

Both views are out there — so which is better: start with small pilots, or orchestrate a full transformation even if you roll it out in phases?

It's not a dilemma. The implementation has to be incremental. The design, though, has to be holistic.

We've seen organizations run dozens of AI pilots in parallel and never capture the aggregate value. Usually it isn't the technology's fault — what's missing is a unified architecture. The result is siloed solutions, growing technical debt, and no way to measure overall ROI.

For us, the right approach has three stages. First, you design the central AI platform. Second, you put governance and observability in place from the start — not as an afterthought. Third, you implement use cases with clear prioritization based on business value.

At helvia.ai we live this every day. When you work with large corporate and institutional organizations, you quickly understand that you're not bolting on fragmented tools. You're building a new operating layer on top of existing processes. AI isn't a project roadmap — it's a transformation roadmap.

Are there practical steps a CEO should take before deciding to invest in AI?

The first thing a CEO needs to do is change the question. Not "how do I use AI?" but "how will my company operate when AI is part of every process?"

AI isn't a technology decision. It's an operating decision. And it asks three things of a CEO. First, operational clarity. Where is the friction in your processes? Where is time being lost to repetitive work? Without that mapping, every investment is made blind. Second, clear ownership. AI can't be a side project of the IT department. It needs an executive sponsor and real ownership at the C-level.

Third, the build-vs-buy decision — and this one is probably the most misunderstood. Many organizations try to build what they could buy, or buy where they should be building a strategic advantage. The rule is simple: buy where you don't differentiate; build where the AI capability becomes part of your DNA — and therefore can't be copied by anyone. Above all, at this stage, waiting is now a bigger risk than acting.

In which business areas do you see the most immediate and measurable ROI from AI investments today? Are there specific use cases that almost always pay off quickly?

Three areas almost always pay off fast — and we see them every day with clients across Greece, Europe, and the US.

First, business-process automation with customer-facing AI Agents. In sectors like energy, transport, insurance, and car rentals, AI Agents take on the repetitive requests and free up human teams for the complex cases. The result is cost and customer experience improving at the same time.

Another is employee enablement through AI Personal Assistants — perhaps the use case with the largest overall impact on productivity. When you work at scale — from leading US HR-technology providers serving hundreds of thousands of businesses, to Fortune 500 companies supporting strategic accounts in tech, pharma, and healthcare — you see directly what changes in how employees find information and make decisions.

The third is the redesign of critical digital services. One of the clearest examples is our involvement in designing AI Agents for the country's critical public digital infrastructure. This isn't customer-service automation — it's rethinking how millions of users interact with services they rely on every day.

To those three I'd add a fourth area, with perhaps the most undisputed ROI today: AI-powered software engineering. Companies that have embedded AI tools into their software-development workflows report significant productivity gains — from generating code to testing and documentation. It's probably the most mature use case of 2026, and one helvia.ai uses internally every day.

These use cases aren't abstract examples. They run today on the Helvia.ai AI Agents Platform, which we designed precisely so organizations can build, govern, and optimize AI Agents in production — not as scattered tools, but as a unified operating layer.

The common denominator across all of them: the value is measured cleanly — time, cost, speed — and the AI fits into existing flows without demanding a radical reorganization.

Are there cases where AI ends up costing more than it delivers? What's the most common "red flag" that an AI project shouldn't go ahead?

There are more than you'd imagine from reading the success stories. But before I get to where AI costs more than it returns, it's worth reframing the question. Because AI rarely fails on its own. What almost always fails is the original decision to start a particular AI project.

There are specific patterns we've seen repeat. The first is when an AI project starts to impress rather than to solve a problem — and that happens more often than people think. A leadership team wants "to have an AI strategy," a CIO wants to show innovation at the next board meeting, a vendor promises a demo that wows in ten minutes. A viable investment rarely comes out of that starting point.

The second is when the ROI case rests on fully replacing the human workforce. We know this well from customer-service projects. Around 80% of interactions automate easily and cheaply. But the 20% — edge cases, emotionally charged scenarios, high risk — takes so much investment to automate that it breaks the economics of the whole project. Those who design for 100% usually pay disproportionately for that last 20%.

The third, and more technical: when we try to solve everything exclusively with Large Language Models. It's a seductive approach — the same technology doing everything. But as a system's complexity grows, relying solely on LLMs becomes slow and far too expensive in production. Mature AI systems combine LLMs with structured business logic. That architectural choice is, in practice, the difference between a demo and a system that holds up at scale.

A simple way to check whether an AI project is on healthy footing: ask what problem it solves today, not what vision it serves. If the answer is vague, the ROI will be vague too.

What are the key questions a leadership team should ask before choosing an AI partner?

Choosing an AI partner is one of the most critical decisions an organization makes today, and it's a decision built through good conversation. From our own experience architecting these systems for organizations that don't have the luxury of failure, there are specific questions that help leadership understand whether a partnership fits.

The first should probe "is your solution model-agnostic?" The language-model landscape changes every few months. An architecture that can dynamically pick the best model for each task protects your investment over the long run.

Next, "how does the partner monitor and fix an AI system once it's in production?" It's a question that quickly reveals a partner's maturity. People who have actually run AI in production can explain to you precisely how they keep the system consistent and reliable, day in and day out.

You should also explore "how are LLMs combined with structured business logic?" That conversation exposes your partner's technical approach and the system's ability to hold up at operational scale.

The last and perhaps most practical one: "can we see AI Agents running in production for real clients, with real numbers?" Demos are a first step and they have their value. But real maturity shows in a system that works every day for thousands or millions of people.

On top of all of that comes the level of institutional seriousness: ISO 27001 and ISO 9001 certifications, full GDPR compliance, strict access-management controls. When AI touches the sensitive data of millions of people, these aren't details — they're prerequisites.

How can a CEO tell a real AI solution apart from simple automation that's been "baptized" as AI?

It's less of a technology question than it looks, and more of a business one. In the reality of 2026, almost every modern automation tool embeds LLMs in one way or another. So the distinction is no longer "does it have AI or not." The distinction is "what does this AI do for you that's worth its cost."

What truly sets a mature AI system apart comes down to three things.

The first is the ability to hold a conversation in natural language — not as a gimmick, but as a genuine way of interacting. Plenty of systems advertise "AI" but in practice require the user to learn specific commands or follow predefined flows. A mature AI Agent understands what a person wants, even when they don't phrase it "correctly."

The second is the ability to take responsibility for the outcome. That means not just offering the user suggestions, but actually executing — calling an API, updating a CRM, completing a transaction. This difference is crucial: a system that only "talks" costs many times more to use than one that actually gets things done.

The third, and perhaps the most critical for a CEO, is consistency in production. It's easy to build an AI that impresses in a demo. It's an entirely different thing to build an AI that delivers the same quality of answer the 100th, the 1,000th, and the millionth time. That consistency is the real product.

So when you're evaluating an AI solution, the question isn't "does it have AI?" The question is: "which of these three does it give me, and to what degree?"

How much does the role of employees change when AI enters a business?

The right model isn't "Human versus AI." It's "Human together with AI." The employee's role doesn't disappear — it's relieved. AI takes on the speed, the consistency, and the 24/7 availability. It does the heavy lifting on everything repetitive and predictable. The human focuses where they're truly irreplaceable: on empathy, on judgment in ambiguous or emotionally charged situations, and on decisions that carry high business risk.

But there's also a psychological dimension few people talk about. When users interact with AI Assistants regularly, they anthropomorphize them. They don't treat them as plain software; they ascribe intentions, character, even emotions to them. They speak to them naturally, they thank them, they judge them.

This isn't strange. It's a fundamental design consideration. When you build an AI system that will work with real people — employees of a large group, citizens using a public service, customers of a multinational — you have to design not only its logic, but the limits of its behavior. Because people will remember that behavior.

If a CEO could keep just one piece of AI advice for 2026, what would it be?

If I had to keep only one, it would be this: the quality of your AI implementation is directly tied to the quality of your data. In most cases where an AI implementation didn't deliver what was expected, the cause wasn't the technology. It was the data — scattered across different systems, inconsistent, locked in silos. So before you go hunting for the perfect AI tool, it's worth spending time on the foundations: getting your data organized, reliable, and accessible to your teams.

It's probably the most underrated investment around AI today. And the most decisive for everything that comes after.

Takeaway

The thread running through every answer is the same: in 2026, AI is an operating decision, not a technology purchase. Buy where you don't differentiate, build where you do, fix your data first, and design for people and AI working side by side rather than one replacing the other. The timing question has flipped, too. For most organizations, the real risk is no longer moving too fast — it's standing still.

This is an English adaptation of an interview with Dimitris Balaouras, co-founder of helvia.ai, conducted by Niki Papazoglou and originally published in Greek on Insider.gr: https://www.insider.gr/tehnologia/413165/helviaai-o-dimitris-mpalaoyras-apokodikopoiei-ton-odigo-toy-ceo-gia-ai

helvia.ai designs, builds, and manages enterprise AI Agents that automate tasks, engage users, and deliver business outcomes at scale. Contact: contact@helvia.ai