This week, I've been thinking about something slightly uncomfortable.

Last weekend, I was reviewing one of my older architecture diagrams from five years ago. A cloud-native migration plan I was deeply proud of at the time. It was clean. Structured. Scalable.

And then I asked myself:

If I were to rebuild this today in the era of generative AI…

Would I build it the same way?

The honest answer?

No.

Not because it was wrong.

But because our assumptions have changed.

Two years ago, AI was a feature.

Today, AI is shaping architecture decisions.

We're not just designing systems anymore.

We're designing systems that design, generate, predict, and automate.

And here's the tension I keep seeing in enterprise conversations:

Everyone wants AI.

But very few are asking:

"What technical debt are we creating while chasing it?"

That's why today's conversation matters.

Today, I'm joined by Maxim Salav, based in Australia, someone who works deeply in enterprise architecture and technical debt remediation.

And this episode is not about hype.

It's about responsibility.

Because AI doesn't remove architectural complexity.

In many cases, it amplifies it.

Let's get into it.

Chapters

00:00 Introduction to Technical Debt and Architecture 01:34 The Impact of AI on Technical Debt 04:12 Generative AI and Architectural Challenges 08:40 Adopting AI in Organizations 12:26 Building AI Strategies and Governance 17:33 Data Quality and AI Integration 22:43 Guardrails for AI Adoption

Episode # 181

Today's Guest: Maxim Silaev, Technology Advisor and Enterprise Architect

He is a technology advisor and enterprise architect with more than two decades of experience working with high-growth companies, complex systems, and business-critical platforms.

• Website: Arch-Experts

What Listeners Will Learn:

• What technical debt really means in the AI era
• How generative AI can unintentionally increase hidden system risk
• Why architecture remains critical despite AI coding tools
• The importance of governance and verification layers in AI systems
• How large enterprises are cautiously integrating AI
• Why strategy must precede AI deployment
• The evolving role of enterprise architects in AI-native environments

Resources:

• Arch-Experts