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.
The original interview, conducted by Niki Papazoglou, was published in Greek on Insider.gr. We’ve translated the full conversation into English below.
Where are we really today in terms of business maturity?
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.
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.
Small pilots, or a full transformation rolled out in phases?
It’s not a dilemma. The implementation has to be incremental. The design, though, has to be holistic.
For us, the right approach has three stages:
- 1Design the central AI platform.
- 2Put governance and observability in place from the start — not as an afterthought.
- 3Implement use cases with clear prioritization based on business value.
AI isn’t a project roadmap — it’s a transformation roadmap.
If a CEO could keep just one piece of AI advice for 2026, what would it be?
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.
Takeaway
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. For most organizations, the real risk is no longer moving too fast — it’s standing still.
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