Why I'm Fascinated by AI

What I'm Seeing

AI implementation is still in its early stages, and I'm fascinated by the patterns that are emerging. What makes some organizations successful while others get stuck in proof-of-concept cycles?

I'm particularly interested in the infrastructure and coordination challenges that emerge when organizations try to scale AI beyond individual tools. How do you build systems that allow multiple AI capabilities to work together effectively?

Areas of Exploration

I want to share insights on AI implementation, what I've believe makes systems work together effectively. The focus is on infrastructure patterns, coordination principles, and the practical approaches that differentiate successful implementations from proof-of-concept cycles.

These perspectives explore everything from technical architecture decisions to organizational patterns and AI tools available, with the goal of understanding and sharing what enables AI systems to achieve measurable outcomes at scale.

Why "Palingenesis"?

Palingenesis means "rebirth" or "regeneration"—the idea that something can be fundamentally transformed while retaining its essential nature. This captures what I find most interesting about AI implementation: how organizations can regenerate their existing systems and processes with intelligence, rather than replacing everything from scratch.

The most successful AI implementations don't throw away existing infrastructure and workflows. Instead, they systematically enhance what already works while addressing the coordination challenges that emerge when multiple intelligent systems need to work together.

Connect and Collaborate

I want to connect with others working on enterprise AI implementation, especially those exploring systematic approaches to AI implementation. I'm interested in sharing perspectives and learning from others who are tackling similar challenges.