AI is complicated. But what if we’re the ones making it more complicated than it needs to be?
In most conversations today, AI sounds like alchemy. Acronyms, hype, and talk of billion-parameter models dominate the narrative. Yet the most impactful AI systems I’ve seen or helped build didn’t come from massive models or secret sauce algorithms. They came from something much less glamorous: AI Infrastructure.
Not the kind that makes headlines. The kind that quietly does its job in the background and looks like clean data flows, thoughtful abstractions, and systems designed to learn, adapt, and fail gracefully.
If you’re building AI or thinking of integrating it into your company, here’s the hard truth: most of the work that makes AI great happens before the model.
The AI that works isn’t flashy
AI didn’t start with magic. It started with math.
The field began by answering simple questions: Can a machine learn from examples? Can it adapt when the world changes? These led to decades of research, infrastructure, and experimentation. The core idea was clear, but making it work in practice took 50+ years. And this is essential to remember.
The best systems today (the ones powering logistics, underwriting, search, and personalization at scale) aren’t built on hype. They’re built on clear principles: data quality, continuous monitoring, reproducibility, and human oversight.
Yes, the model matters. But what matters more is what wraps around it.
You can build an AI tool in a weekend. But I believe that’s part of the problem.
Today, LLMs and no-code platforms make it easy to spin up AI demos in hours. But for most teams, going from demo to production is where the real challenge begins. And, if you’re wondering why, it’s because we’ve simplified the process, but not the thinking.
Too many teams rush to plug in GPT or fine-tune a foundation model without asking the basics:
- What real problem are we solving?
- What does success look like?
- How clean is our data?
- What happens when the model fails?
AI without this foundation doesn’t scale. It will, however, most probably drift or even break. Lastly, surprise-surprise, it gets abandoned. This is the story behind 95% of failed AI projects.
The AI edge isn’t just model performance. It’s systems thinking.
The companies seeing real ROI from AI today don’t just have talent. They have structure.
They invest in:
- Robust data infrastructure and observability;
- Clear ownership and accountability;
- Real-time feedback loops;
- Simplicity in design: fewer moving parts, clearer incentives.
Throughout my +10 years of work, I’ve seen this over and over. Whether we’re building something from scratch or plugging into existing workflows, the turning point is rarely the model. It’s whether we can make the system around the model work.
What CEE needs to know
For the broader CEE ecosystem, this matters more than ever. We’re seeing incredible talent, energy, and experimentation, but also a tendency to treat AI like a checkbox: fine-tune a model, plug it in, call it innovation. I want to highlight that this approach won’t get us far.
I believe the region has a unique opportunity to lead by doing things differently:
- Start small, ship fast, learn from real usage.
- Prioritize systems that are maintainable, not just impressive.
- Focus on data you actually control. This is your moat.
- Partner smart: don’t just hire ML talent, build full-cycle teams that understand product, ops, and engineering.
Simplicity is the hard part, but it’s what can truly make waves
I’m certain that the next leap in AI won’t come from bigger models, but from asking better questions: Do we need AI for this? What’s the smallest useful version we can build? Can we explain what this system does to someone who’s not technical?
Great AI is useful, not just impressive in demos. And that usefulness starts long before the model.
