AI - Beyond the Hype

About AI - Beyond the Hype

AI - Beyond the Hype is a podcast about what it actually takes to make AI work — and what it actually means when it doesn't. Hosted by Sarah, a data engineering leader, and James, an enterprise technology leader, the show pairs the mechanics underneath AI systems with the executive consequences on top of them. Sarah opens the hood. James asks what it costs, what it risks, and who owns it. Neither of them is selling anything. Each season takes a different angle on the same question. Season one was written for senior executives, technology leaders, and data professionals: a nine-episode arc on the foundations behind successful AI adoption — data quality and observability, modelling, security, privacy, architecture, and operating models. The recurring finding was uncomfortable. Almost every AI failure the hosts examined wasn't an AI failure at all. The system worked exactly as designed. The conditions around it didn't. Season two changes gear entirely. This season answers questions about AI from people who don't work in this field — and who are quietly tired of pretending they follow the conversation. How does it actually know things? Is it thinking? Why does it make things up? Should I let my kids use it? Will it take my job? Not dumbed down, and not a lecture. Deep enough to give a real answer, then stopping before the jargon starts. If you have a question like that, the hosts would genuinely like to hear it. No question is too basic — the more basic, the better. Send any questions, or just get in touch with us at: asksarahandjames@gmail.com (mailto:asksarahandjames@gmail.com) Better AI still starts with better foundations

Publisher

Sara, James & Darryl

Category

business · technology

Language

en

Explicit

No

First episode

13 Apr 2026

Latest episode

7 Oct 2026

Latest episodes

6 of 14 episodes have a transcript.

1. 

   7 Oct 2026

   S02 - No Database Inside: How AI Knows Things, and When It Doesn't

   Host James Clear and AI expert Sarah Smith explore how AI "knows" things without databases, revealing the underlying map of meaning.

2. 

   14 Sept 2026TRANSCRIPT

   S02 - What Is AI? The Question Everyone Stopped Asking Out Loud

   Host Sarah Miller and guest James Smith demystify artificial intelligence for a general audience, breaking down complex concepts into understandable terms.

3. 

   2 Aug 2026TRANSCRIPT

   S02 - AI - Beyond the Hype - Season 2 Trailer

   Host Alex Davies interviews industry experts to demystify AI's core concepts and address common public concerns in this season trailer.

4. 

   30 Jul 2026TRANSCRIPT

   It's a Wrap: Nine Episodes, One Argument, and What We Got Wrong

   Host Sarah and guest James reveal how AI failures often stem from data and organizational issues, not the technology itself, in this season finale interview.

5. 

   3 Jul 2026

   Operating Models for Solid Foundations Part 2 - Fund the Foundation, Not Just the Launch

   Part 2 of 2 in our Operating Models for Solid Foundations series. Part 1 diagnosed the problem: enterprises fragment their technology portfolios when they don't choose an operating model explicitly, and architecture governance without funding power is just advice. Part 2 goes underneath the money. Why does a shared platform that was properly funded at build time get cut in the next annual opex review? Why do the teams building foundations keep losing arguments they should win? And what can a...

6. 

   29 Jun 2026

   Operating Models for Solid Foundations Part 1 - The Model You Didn't Choose

   Part 1 of 2 in our Operating Models for Solid Foundations series. Most large enterprises have project frameworks, architecture tollgates, and governance processes — and still end up with three separate "central" data platforms. In this episode, James makes the case that fragmented technology portfolios aren't a delivery failure. They're the downstream consequence of an operating model that was never explicitly chosen. Sarah comes in sceptical. By the end she's unsettled — and sees for the fi...

7. 

   28 May 2026TRANSCRIPT

   Data Quality Part 2: Fixing It - Critical Data Elements, Contracts, and the One Question That Stops Robodebts

   Host James and guest [Guest Name, Title] discuss critical data elements and actionable strategies for improving data quality in this interview episode.

8. 

   21 May 2026TRANSCRIPT

   Data Quality Part 1: Beyond Accuracy — What "Good Data" Really Means When AI Is on the Line

   Host James and data quality expert Sarah explore the multifaceted nature of "good data" beyond simple accuracy for AI success.

9. 

   15 May 2026TRANSCRIPT

   AI Security Part 3: Why PII and the Privacy Act Are the AI Foundation Most Leaders Skip

   Host Sarah and guest James explore how PII and the Privacy Act form the crucial, often-overlooked foundation for secure AI development.

10. 

    7 May 2026

    AI Security Part 2: When AI Stops Answering and Starts Acting

    Last episode was about AI that answers. This one is about AI that acts — and the moment prompt injection became a board-level risk. Sarah and James pick up where Part 1 left off. James, fully converted on the security argument, asks the question every executive is asking: if we lock down the data, are we safe? Sarah's answer: agentic AI changes the threat model entirely. What we cover EchoLeak (CVE-2025-32711, June 2025): the first zero-click attack on Microsoft 365 Copilot. CVSS 9.3. An atta...

11. 

    29 Apr 2026

    AI Security Part 1: Why AI Without Data Security Is a Breach Waiting to Happen

    Sarah and James open the three-part Data Security for AI series with a simple argument: AI is only as trustworthy as the data underneath it. What we cover The adoption gap: Gartner expects 40% of enterprise apps to embed AI agents by end‑2026 (up from <5%). IBM’s 2025 Cost of a Data Breach Report found 13% of organisations have had an AI-related breach — 97% lacked proper access controls. Structured vs unstructured data: IDC estimates 80–90% of enterprise data is unstructured. Varonis foun...

12. 

    20 Apr 2026

    The Invisible Architecture: Why Data Modelling Is the Make-or-Break for Enterprise AI

    Sarah and James unpack a question most AI programmes never ask early enough: is the data actually modelled? Drawing on recent benchmarks, documented enterprise failures, and hard ROI evidence, they explore why AI accuracy drops to zero without proper data foundations, why 80% of AI projects stall on data — not algorithms — and what leaders can do about it. From the London Whale to Walmart's checkout fiasco, this episode puts data modelling in the language of business risk, competitive advanta...

13. 

    14 Apr 2026

    Why Data Observability Matters Before AI Scales

    In the first episode of AI - Beyond the Hype, Sarah and James explore why data observability is one of the most overlooked foundations of enterprise AI readiness. They discuss how incomplete, delayed, duplicated, or poor-quality data can quietly undermine dashboards, reporting, and AI outcomes — and why better AI still starts with better data. (Sources: https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/cloud-scale-analytics/manage-observability, https://www.ibm.com/th...

14. 

    13 Apr 2026

    AI - Beyond the Hype - Trailer

    Welcome to AI – Beyond the Hype — the podcast for senior executives, technology leaders, and data professionals who want a clear-eyed, practical view of what it actually takes to make AI work in the enterprise. In this short trailer, co-hosts James and Sarah introduce the show, themselves, and the way they'll approach every episode: technical depth from Sarah, executive clarity from James, and an honest conversation about the foundations that decide whether AI succeeds or quietly fails. Expec...

Inside a recent episode

S02 - What Is AI? The Question Everyone Stopped Asking Out Loud

Published 14 Sept 2026 · Transcript excerpt

[…] Fine. Past tense of verbs. If I write a program that memorizes past tenses, it can tell you walked and played because I typed those in. Ask it for the past tense of jump, and if I never gave it jumped, it's got nothing. It fails. It's a look-up table. It's a look-up table. Now, a system that learns from examples, sees a few hundred verbs, works out on its own that there's a pattern and produces jumped without ever having been told jumped. So it's the difference between memorising and generalising. That's the whole thing in one word. Generalising. That capacity to handle something it has genuinely never seen before is what separates AI from everything else we've built. […]

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