The AI Storm Podcast

AI and the Role of Engineering Judgement

AI is reshaping how systems are built — but it’s also reshaping how decisions are made. In Episode 2 of Season 2 of The AI Storm Podcast, we explore a deeper and often overlooked question: What is the role of engineering judgement in an AI-driven world? This episode is a reflective conversation between two engineering leaders, focusing on: * How engineering leadership has evolved as systems become more complex * Why speed without clarity often creates long-term drag * The difference between heroic recovery and real system health * When leaders should pause and reframe — and when momentum truly matters * Why AI amplifies engineering judgement rather than replacing it If the first episode of this season explored AI as a leadership challenge, this conversation examines how engineering judgement is formed, tested, and exercised under pressure — especially in environments where systems are probabilistic and outcomes are uncertain. 🎙️ The AI Storm Podcast Season 2 · Episode 2

S2 E1 - AI Hype vs Reality - Conversation with a CEO

AI is no longer just a technology trend — it’s a leadership challenge. In the opening episode of Season 2 of AI Storm, we move beyond hype and experimentation to explore what AI really changes when leaders are accountable for outcomes. This episode looks at AI through a CEO’s perspective, focusing on: * Why many AI initiatives struggle beyond proof-of-concepts * What organisations truly mean when they ask for “AI” * How accountability and decision-making shift in AI-augmented organisations * Why leadership judgement matters more than tools This is not a discussion about models or platforms. It’s a conversation about responsibility, trust, and delivery in the age of AI. If you’re a CEO, CIO, CTO, or senior leader navigating AI adoption, this episode will challenge how you think about success. 🎙️ AI Storm — where AI meets leadership reality.

Episode 10 — The Human Element: Job Security, AI & Jevons Paradox

Season 1 Finale AI isn’t coming. It’s already here. In this season finale of The AI Storm, we step away from theory and research — and speak from lived experience. From building AI POCs inside a global enterprise… To launching an AI startup without a large team… To navigating fear, trust, security, job anxiety, and speed-to-market pressures… This episode is about what really happens when AI meets real organisations and real people. You’ll hear: * * Why speed-to-market is no longer about team size or funding * * How AI reshapes cognitive load — not just productivity * * Why job fears increase even as opportunity expands (Jevons Paradox) * * What leaders get wrong about readiness, trust, and control * * How one POC changed the way Krishna thought about building products forever This is not a prediction episode. This is a reflection episode. And it closes Season 1 of The AI Storm — before we move into a new chapter of video conversations and interviews. 🎙️ Listen now. 🌩️ Stay thoughtful. Stay curious. Stay ahead of the storm.

AI Reasoning: Who Decides, When to Trust, and Where Humans Must Stay in Control

AI can decide faster than humans.But it cannot decide responsibly.In this episode of The AI Storm, we explore the most critical question facing leaders as AI systems become more autonomous:👉 Who should decide — the machine or the human?As organizations move into 2026, AI reasoning is no longer experimental. Systems are already making decisions across logistics, cybersecurity, finance, and operations. But not every decision should be automated.In this episode, we cover: * What “AI reasoning” really means (and what it doesn’t) * Where AI decisions outperform humans * Where human authority must always remain * Why leadership is shifting from managing people to orchestrating intelligence * A simple decision framework every executive should use before trusting AI autonomy This isn’t a technical conversation.It’s a leadership one.🎧 Listen now and stay ahead of the storm.

The AI Automation Gap: Why Enterprise AI Breaks After the Demo

Why Enterprise AI Breaks After the Demo AI rarely fails because the technology is weak. It fails because it’s deployed inside workflows that were never designed for intelligence. In this episode of The AI Storm, we explore The Automation Gap — the space where AI pilots succeed, demos impress, and real-world execution quietly breaks down. You’ll learn: * Why automating broken workflows amplifies chaos instead of fixing it * How poor data quality, missing context, and fragile integrations cause AI to fail silently * The difference between human-in-the-loop and human-on-the-loop — and why leaders must design for both * Why most AI failures are operational and leadership problems, not model problems * The four workflow design questions every executive must answer before scaling AI This episode isn’t about better models. It’s about better workflow design. If AI in your organisation “works… but only sometimes,” this episode will help you understand why — and what to redesign next.