Want to catch the AI native wave? Learn the lessons of the cloud-native shift

From the personal computer to the internet to mobile and cloud native, transformative technologies bring new challenges and opportunities. While every innovation is different, Pini Reznik says there are patterns that can be applied when adopting any new technology.

In the latest PurePerformance podcast, Reznik talks to hosts Andi Grabner and Brian Wilson about his new book From Cloud Native to AI Native: Catching the Next Wave of Innovation, and how organizations can take a pragmatic approach to AI adoption.

The AI Native transformation process

Here’s how Reznik describes the process of adoption AI, or any other transformational technology:

• Experiment: Start with a small, skunkworks-style team operating in a sandbox. Their mission isn’t to deliver production-ready systems but to experiment, learn, and identify viable business cases.
• Find a small win: Once you find a promising use case, build a minimal viable product that demonstrates tangible value. Success here justifies incremental investment.
• Build a foundation: Build the infrastructure, develop the team structure, and foster the new culture necessary to adopt the technology at scale.
• Scale up: Expand as you grow, transitioning from legacy systems to new solutions carefully.

The biggest mistake organizations make is skipping the first two phases and jumping straight to large-scale initiatives. That’s why you hear so much about failed AI pilots: Organizations try to scale before they validate their use cases.

“Transformation isn’t a one-time project; It’s a way of thinking.”

— Pini Reznik, CEO and Co-Founder, re:cinq

AI for the little guy

In the previous episode of PurePerformance, Laura Tacho made the case for AI’s true potential in software development being not in code generation but in speeding up feedback loops and helping ensure that developers build the right things. Where Tacho focused on developer experience and the software development lifecycle, Reznik focuses on organization-wide potential, particularly for companies that haven’t traditionally built much, if any, software in-house. AI could make it affordable for smaller companies to build custom software based around their unique value propositions.

Our perspective: Start small, validate value early, and ensure every step is measurable and observable. When teams can see the impact of AI in real time—on performance, cost, and outcomes—they make smarter decisions and scale with purpose.