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Daniel Lereya is Chief Product and Technology Officer at monday.com, the AI work platform trusted by 60% of the Fortune 500 and valued at approximately $8 billion. He joined the company when it had 30 people and $4.5M ARR, and has since grown his team from 5 to nearly 900 people as monday.com crossed $1 billion in ARR.
In this episode, Daniel draws on nearly a decade of scaling one of the world's most adopted work platforms to share what it actually takes to rebuild product thinking from scratch when AI changes everything you thought you knew.
In this conversation, we discuss:
- Why the instincts that made monday.com successful are the exact ones Daniel says had to be dismantled to build AI-first products.
- What the critical difference is between building a demo that impresses and an agent that actually works in production, and where most teams get it wrong.
- Why Daniel believes wrapping AI inside rigid workflows produces better results than giving agents full discretion, and what monday.com learned the hard way.
- What happened when 2,000 of 3,000 monday.com employees started building their own apps in just two weeks, and what it revealed about the future of who gets to build software.
- Why Daniel argues that when an AI agent makes a mistake, the real question leaders should be asking has nothing to do with the technology.
- Why the biggest barrier to AI adoption is not the technology itself, and what Daniel says companies must stop waiting for before they start.
Explore:
00:00 Why AI Adoption Is Harder Than It Looks
00:53 Introduction + AI Commerce Standards: Google, OpenAI & Visa
04:30 Daniel Lereya's 9-Year Journey Scaling monday.com to $1B ARR
09:00 How AI Forces a Complete Reset in Product Thinking
12:25 The "AI Month" Initiative: Pausing R&D to Rebuild from Scratch
14:57 Building AI Products When the Output Is Non-Deterministic
20:47 What 250,000 Customers Taught Us About AI in the Real World
25:38 Responsible AI: Guardrails, Governance, and Data Control
31:28 Who Is Responsible When an AI Agent Makes a Mistake?
37:04 The Future of Work: Humans, Agents, and What Comes Next
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