『AI - Beyond the Hype』のカバーアート

AI - Beyond the Hype

AI - Beyond the Hype

著者： Sara James & Darryl
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AI - Beyond the Hype is a podcast for senior executives, technology leaders, and data professionals who want a clear-eyed view of what it really takes to make AI work in the enterprise.


Each short episode is designed for easy consumption by busy leaders and executives, offering concise, practical conversations on the foundations behind successful AI adoption — from data quality and observability to governance, operating models, architecture, and trust. Through thoughtful, conversational dialogue, the show connects executive priorities with the technical realities that determine whether AI delivers meaningful value or simply creates more noise.


If your organisation is asking big questions about AI readiness, digital transformation, and data-driven decision-making, this podcast is designed to help you quickly separate what sounds impressive from what actually works.


© 2026 AI - Beyond the Hype 経済学
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  • Operating Models for Solid Foundations Part 2 - Fund the Foundation, Not Just the Launch

    Operating Models for Solid Foundations Part 2 - Fund the Foundation, Not Just the Launch
    2026/07/03

    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 leadership team actually change — without rewriting the chart of accounts?

    What we cover:

    • The CapEx/OpEx accounting trap: why building a platform looks like an investment but running it looks like overhead — and how that difference alone explains most platform degradation after go-live
    • The producer-consumer funding gap: why every shared platform's costs land in one place while the value is spread across every team consuming it — and why that structure makes the platform impossible to defend in a budget review
    • From projects to products: what the product operating model actually means for how you fund, staff, and measure a shared foundation — and why McKinsey's research shows it produces higher technology returns
    • FinOps as an enterprise governance tool: how showback and chargeback make a platform's value visible to finance teams and business leaders before the annual budget cycle, not during it
    • Closing the governance loop: what it means to give architecture a seat at the funding table instead of the review table — and the one sequence change that prevents the next fragmentation cycle from starting
    • Five Monday-morning moves for senior leaders: from the capability map to the product funding pilot — concrete actions that don't require a transformation program

    "The moment a CEO or CFO asks 'show me the capability map' — it gets made."

    Key references:

    • McKinsey — The bottom-line benefit of the product operating model, technology funding and returns: https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/the-bottom-line-benefit-of-the-product-operating-model
    • FinOps Foundation — Managing shared cloud and platform costs, showback and chargeback framework: https://www.finops.org/framework/capabilities/invoicing-chargeback/
    • IAS 38 (IFRS Foundation) — Intangible asset capitalisation standards, CapEx treatment of software development: https://www.ifrs.org/content/dam/ifrs/publications/pdf-standards/english/2021/issued/part-a/ias-38-intangible-assets.pdf
    • Ross, Weill & Robertson — Enterprise Architecture as Strategy (MIT CISR), operating model and engagement model: https://cisr.mit.edu/publication/enterprise-architecture-as-strategy

    Better AI still starts with better foundations.

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

    Operating Models for Solid Foundations Part 1 - The Model You Didn't Choose
    2026/06/29

    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 first time why so many of the data problems she's spent her career fixing kept coming back.

    What we cover:

    • Why "operating model" is a specific strategic choice — not a generic description of how the business runs — and the two axes that define it
    • The four operating model types (Diversification, Coordination, Replication, Unification) and why each implies a completely different architecture and funding logic
    • How architecture tollgates become rubber stamps when they're disconnected from investment decisions — and what a real IT engagement model looks like instead
    • The "three central data platforms" problem: why every team that built one was responding rationally to the signals they were given
    • How DBS Bank cut AI deployment time from 18 months to under 5 months — not through better models, but through an explicit operating model and funded platform foundations
    • Why delivery teams that do everything right — including funding the operational run budget — still see their platforms degraded by sweeping opex cuts they had no language to resist

    "The wiring can't be right if nobody decided what the building is supposed to do."

    Key references:

    • Ross, Weill & Robertson — Enterprise Architecture as Strategy (MIT CISR), foundational operating model framework: https://cisr.mit.edu/publication/enterprise-architecture-as-strategy
    • MIT CISR, architecture learning and management practices that help EA create value: https://cisr.mit.edu/publication/2012_0901_ArchitectureLearning_RossQuaadgras
    • McKinsey — DBS Bank platform transformation and AI deployment case: https://www.mckinsey.com/capabilities/tech-and-ai/how-we-help-clients/rewired-in-action/dbs-transforming-a-banking-leader-into-a-technology-leader
    • INFORMS — UPS ORION route optimisation, built on unified operational data foundations: https://www.informs.org/Impact/O.R.-Analytics-Success-Stories/UPS

    Better AI still starts with better foundations.

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

    Data Quality Part 2: Fixing It - Critical Data Elements, Contracts, and the One Question That Stops Robodebts
    2026/05/28
    Part 2 of 2 in our Data Quality series.In Part 1, James came in skeptical and walked out sold on the problem. In Part 2, we deliver the fix — the discipline, the architecture, and the eight concrete moves executives can make on Monday morning. This is the episode for leaders who heard last week's case studies and asked "okay, but what do we actually do?"What we cover:The one question every CEO should be asking this week: what are our Critical Data Elements, who owns each one, and how do we know each is fit for purpose?Why fixing all the data is how data quality programs die — and how ruthless tiering (50-300 fields, not 50,000) is how they surviveData contracts: the quiet revolution in how serious organisations manage producer-consumer relationships, popularised by Andrew Jones at GoCardless and Chad SandersonThe five default checks every Critical Data Element should pass: freshness, volume, schema, distribution, referential integrityThe five-layer reference architecture: contracts, validation, observability, lineage, governance — and why governance is where most organisations failUnity Technologies 2022: how contaminated training data cost $110M in revenue and $5B in market capitalisation in a single dayRobodebt: the Australian government program that issued ~470,000 invalid debt notices, ended in a Royal Commission, and cost $1.8B in settlement — and the three-word question that would have stopped itThe eight-step Monday-morning move: a complete executive action planThe case study James can't name: a global enterprise (90,000 people, $50B+ revenue) six years into a serious data strategy — with every right concept on paper, an aggressive AI rollout underway, and a green dashboard hiding the reality. Why "the mandate is not the implementation" is the most dangerous gap in enterprise AI today.The one question that stops Robodebts: "Fit for purpose for what?"Key references:Wang & Strong (1996), foundational dimensions of data quality: https://doi.org/10.1080/07421222.1996.11518099DAMA UK — Six Core Data Quality Dimensions: https://www.sbctc.edu/resources/documents/colleges-staff/commissions-councils/dgc/data-quality-deminsions.pdfCritical Data Elements Explained: https://www.dataversity.net/articles/critical-data-elements-explained/ISO/IEC 25012:2008 — Data Quality Model: https://www.iso.org/standard/35736.htmlSambasivan et al., "Everyone wants to do the model work, not the data work" — data cascades in high-stakes AI (Google Research, CHI 2021): https://research.google/pubs/everyone-wants-to-do-the-model-work-not-the-data-work-data-cascades-in-high-stakes-ai/IBM Institute for Business Value — 2025 CDO Study: https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2025-cdoBCBS 239 — Principles for effective risk data aggregation and risk reporting: https://www.bis.org/publ/bcbs239.htmRoyal Commission into the Robodebt Scheme — Final Report (2023): https://robodebt.royalcommission.gov.au/publications/reportUnity Technologies Data Quality Issue: https://www.fool.com/investing/2022/07/17/2-reasons-unity-softwares-virtual-world-is-facing/Andrew Jones — Driving Data Quality with Data Contracts: https://andrew-jones.com/data-contracts-101.pdfChad Sanderson — The Rise of Data Contracts: https://dataproducts.substack.com/p/the-rise-of-data-contractsChad Sanderson — Data Products and Contracts (Data Quality Camp): https://www.youtube.com/watch?v=1CSTSdfe0qgIf this series helped, share it with the loudest voice on AI strategy in your organisation. If their AI strategy doesn't have a data quality strategy underneath it, you now know what to ask them.Better AI still starts with better foundations.Send us Feedback
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    34 分
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