Why Most Companies Are Measuring AI Success Wrong ft. Karol Przystalski, Exadel | E128

What does AI engineering actually look like inside modern enterprises — and how close are we to truly autonomous software development? In this episode of The Recursive Podcast, we sit down with Karol Przystalski, Chief Data and AI Officer at Exadel, to explore how organizations are moving beyond AI experimentation and into AI-native operations. We discuss the evolution from AI copilots and coding assistants to fully orchestrated engineering systems, why many companies are measuring AI success incorrectly, and how enterprise leaders should think about ROI, productivity, and organizational change in the age of autonomous engineering. Karol also shares insights into Exadel Colleague — an AI-powered engineering teammate designed to support entire software teams—and explains why the future of AI isn't about replacing humans, but helping them focus on higher-value work. In This Episode: 🔹 Why AI adoption is shifting from experimentation to real business value How enterprises have moved beyond AI proof-of-concepts and are now focused on productivity, efficiency, and measurable outcomes. 🔹 The difference between AI-enabled development and AI-native engineering Why coding assistants are only the first step—and how autonomous systems are transforming entire software development lifecycles. 🔹 How to measure AI ROI beyond token usage The metrics that actually matter, including productivity gains, human-equivalent hours saved, accuracy, and business impact. 🔹 The future of autonomous engineering and the role of humans Why AI won't replace software engineers, but will fundamentally change how teams work and what skills will matter most. 🧠 Whether you're an engineering leader, CTO, product executive, or simply curious about the future of software development, this conversation offers a practical look at where enterprise AI is headed next. 🔔 Subscribe to The Recursive for more stories at the intersection of tech, society, and startup ecosystems. --- Chapters: 00:00 Why Companies Are Finally Taking AI Seriously 04:20 AI Tools vs AI-Native Organizations 06:05 The AI Adoption Mistake Everyone Makes 08:40 Will AI Replace Engineers? 11:40 Why Vibe Coding Isn't Enough 15:40 What Makes a Company Truly AI-Native? 16:50 The AI ROI Metrics That Actually Matter 19:10 Tokens Explained in Plain English 26:20 How Much Work Will AI Automate? 28:05 Meet the AI Engineering Teammate 36:15 Real Enterprise Transformation Case Study 41:10 The Future of Autonomous Engineering