AI CEO : How to turn your company AI Native (+50% revenue in 1 year)

Make scaled its revenue and customer base by 50% in one year without hiring more people. I wanted to understand what actually changed inside the company, so I invited Fabian Q. Veit, CEO of Make.com. Make is a visual platform used by hundreds of thousands of companies to build and manage automations and AI agents. But beyond the product, Fabian has been rebuilding the way Make operates internally with AI. He also sees how thousands of companies are trying to do the same: what works, where teams get stuck, and why some AI projects never create real impact. This conversation is for CEOs, CPOs and founders who are trying to figure out where to actually start with AI inside their company. Fabian shares the framework he uses with leaders in the first 30 days of an AI transformation, the operating model that helped Make grow without growing the team, and why the companies making progress with AI are not always the ones hiring the most AI experts. They are the ones changing how work gets done. We get into questions like: - What AI-first really looks like - Why AI projects fail early - Where CEOs should start with AI - How to justify AI investment - What stays human vs automated - Leading a company run by agents Enjoy the episode. - Evaluate your product’s performance: https://www.wearestellar.io/ Follow me on LinkedIn: https://www.linkedin.com/in/timothe-frin-547ba064/ Subscribe to my Product Newsletter: https://timfrin.substack.com/ - I’m Timothé Frin, Co-Founder of Stellar. In this podcast, I meet founders, builders, and product leaders — across Europe and the US. I dive into how they think, how they build… and how they make decisions. We talk product strategy, design, artificial intelligence, team culture, and execution. Concrete conversations, action-oriented, no fluff. - TIMELINE 00:00 – Introduction 02:35 – What Make actually does 04:29 – The biggest mistake in AI transformation 07:02 – Top-down vs bottom-up adoption 09:20 – Justifying AI investment without clear ROI 13:15 – Why everyone flipped on AI in late 2025 15:30 – The first 30 days: skill, will, environment 17:30 – How Make uses its own product 21:43 – When a marketer builds her own agents 23:29 – Engineering at Make in 2026 25:36 – The "Triple A" framework 26:57 – 50% bigger, same team size 27:43 – Automating expense reports at Celonis 30:11 – 1000 people stuck on credit applications 31:12 – What to automate, what to keep human 33:00 – From doer to orchestrator 34:58 – The AI jobs debate 39:23 – Make's evolving value proposition 43:53 – The future of triggers: voice, glasses, visual 46:48 – Positioning in a crowded landscape 49:57 – Can product still be a moat? 53:02 – The OpenAI consulting move