• Petr Podrouzek joins IP Fabric as Chief Technology Officer (CTO) after leading engineering teams and implementing GenAI (GenAI) initiatives at global organizations.

• GenAI can boost engineering productivity by refactoring legacy code, automating Quality Assurance (QA), and more.

• Organizations should implement GenAI gradually, identifying and testing it in targeted areas before scaling to the rest of a product.

IP Fabric is thrilled to welcome Petr Podrouzek as our new CTO. With over 15 years of engineering leadership experience from Emplifi, Barclays, and IPS AG, Petr has managed teams with 170+ engineers spanning Europe, the U.S., and the Middle East. 

This week, Petr sat down with us to discuss his vision for IP Fabric, as well as his thoughts on the future of AI in engineering.

What drew you to IP Fabric? What’s the problem that you’d like to help customers solve?

Enterprises are struggling to deal with sprawling, hybrid networks that are almost impossible to understand—let alone manage effectively. 

I experienced this firsthand at a previous company, where our engineering team had amassed several AWS accounts from a series of mergers and acquisitions. This not only ran up infrastructure costs, but also created obstacles that our DevOps team had to deal with every day. 

In situations like these, IP Fabric’s value becomes clear. The platform delivers a digital twin of all cloud, network, and security systems, which can be indispensable for planning projects and cutting costs across the infrastructure.

Learn how to simplify mergers and acquisitions with IP Fabric.

What opportunities do you see to advance the IP Fabric platform? 

I’m particularly excited to bring my data expertise to IP Fabric. I find myself constantly asking questions like: “What if we bundled analytics, machine learning (ML), or reporting capabilities to deploy a lightweight data warehouse alongside our platform?" This could help customers unlock even deeper insights from the data we're already collecting.

That being said, it's not just about delivering more insights. Part of IP Fabric’s ethos is to make sure that you can use the insights that you get by ensuring that they’re normalized and contextualized in terms of actual network behavior. This helps with any number of use cases, but I think the most exciting one is for more reliable infrastructure automation.

Learn more about IP Fabric by visiting our careers page.

At Emplifi you spearheaded several GenAI initiatives. What excites you most about the next wave of AI in enterprise software?

AI can be a huge productivity booster to engineering teams. What if your team inherits thousands of lines of old code, but the documentation’s long outdated? It can be a real mess. AI can help you to understand what that code actually does, and can update documentation automatically. Refactoring legacy code is just one example, though; I’ve also seen significant gains by automating QA and generating test cases. 

These benefits extend to end users as well. If they can use natural language to query support documentation, SQL, or business intelligence tools, then it makes the product easier to use. The easier the product is to use, the greater adoption will be. The key is ensuring that LLMs are trained with the proper context, including product knowledge and brand voice.

Power reliable AI and automation with IP Fabric.

Many organizations struggle with moving AI from experimentation to production. What’s your strategy for making AI features commercially successful? 

First thing’s first: Don’t give in to gimmicks. You shouldn’t just deploy AI everywhere to see what sticks. AI can deliver a lot of value, but you need to be strategic about where it can be most useful. 

The trick is to start small. Integrate AI into a specific part of your product, and then pause to ask yourself: “How does this help me to achieve my business goals?” For example, did your feature lead to increased product usage? Did it reduce support tickets? Did it improve user satisfaction?

If—and only if—you’re able to demonstrate the value of AI in that situation, then it’s time to scale by refining your model and expanding to additional use cases.

Documentation is a great starting point for automation; many companies have seen immediate value generated by embedding docs into an LLM so customers can query them more conversationally.

“The ‘Yes, and…’ principle taught me to accept ideas and develop them instead of looking for problems right away.”

How do you challenge yourself outside of work?

As an engineer, I tend to focus on identifying and solving problems. But I’ve been seeking out ways to adopt a more optimistic, goal-oriented mindset. That pursuit has landed me in a rather unexpected place: improv classes.

It’s not easy to step in front of people and improvise. I felt self-conscious at first, but month after month, I began to feel more relaxed in adopting a “Yes, and…” mindset. It’s helped me to accept that even if I don’t succeed at something right away, I can still learn something valuable in the process. 

IP Fabric is hiring! Discover open roles here.