AI - Beyond the Hype copertina

AI - Beyond the Hype

AI - Beyond the Hype

Di: 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 Economia
Episodi
  • 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
    May 28 2026
    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 min
  • Data Quality Part 1: Beyond Accuracy — What "Good Data" Really Means When AI Is on the Line

    Data Quality Part 1: Beyond Accuracy — What "Good Data" Really Means When AI Is on the Line
    May 21 2026

    Most executives think data quality means one thing: is the number right? Three decades of research — and a string of nine-figure disasters — say it's actually at least seven different things, and AI is now scaling whichever one your organisation got wrong.

    In Part 1 of our Data Quality in the AI Era series, James starts skeptical. Surely "is the data accurate" covers it? Why is this being made harder than it needs to be? Sarah walks him — and the listener — through what data quality actually is, the seven dimensions that matter for enterprise AI, and the killer distinction that explains most of what goes wrong: valid is not the same as accurate.

    What we cover:

    • Why "we cleaned the data, it's accurate now" has been doing damage for thirty years
    • The seven dimensions of data quality — and why a single quality score is dangerous
    • Public Health England: 15,841 COVID cases lost because an Excel file silently truncated rows
    • NASA Mars Climate Orbiter: a $327M spacecraft lost to a unit mismatch that was perfectly valid
    • Citigroup / Revlon: how three fields, six eyes, and one missing range check became an $894M wire transfer
    • A heavy-industrial safety story where the data wasn't catastrophically wrong — it was catastrophically ambiguous
    • Why AI doesn't inherit these problems gently — it scales them, in a tone of voice that sounds correct
    • A teaser for Part 2: the Robodebt case, and the one question that would have prevented it

    For executives, senior technology leaders, and data leaders trying to get real value from AI investment — without funding it on a foundation nobody has actually inspected.

    "Polished on the surface, shaky underneath." — James

    Episode length: ~21 min
    Series: Data Quality in the AI Era — Part 1 of 2

    References:

    • The MIT Total Data Quality Management Program — https://web.mit.edu/tdqm/www/about.shtml
    • MIT Sloan Management Review, Wang & Strong (1996), "Beyond Accuracy: What Data Quality Means to Data Consumers" — https://doi.org/10.1080/07421222.1996.11518099
    • DAMA UK Working Group, "The Six Primary Dimensions for Data Quality Assessment" (2013) — https://www.sbctc.edu/resources/documents/colleges-staff/commissions-councils/dgc/data-quality-deminsions.pdf
    • ISO/IEC 25012:2008, Software engineering — Software product Quality Requirements and Evaluation (SQuaRE) — https://www.iso.org/standard/35736.html
    • Sambasivan et al., "Everyone wants to do the model work, not the data work: Data Cascades in High-Stakes AI", 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: The AI multiplier effect" — https://www.ibm.com/thought-leadership/institute-business-value/en-us/report/2025-cdo
    • BBC News, "Covid: 16,000 coronavirus cases missed in daily figures after IT error" (5 October 2020) — https://www.bbc.com/news/uk-54422505
    • NASA, Mars Climate Orbiter Mishap Investigation Board Phase I Report (1999) — https://llis.nasa.gov/llis_lib/pdf/1009464main1_0641-mr.pdf
    • Citi cites human error in accidental $900M transfer — https://www.bankingdive.com/news/citi-cites-human-error-in-accidental-900m-transfer/584156/
    • Royal Commission into the Robodebt Scheme, Final Report (7 July 2023) — https://robodebt.royalcommission.gov.au/publications/report


    Related episodes:
    Episode 1 — Why Data Observability Matters Before AI Scales

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

    AI Security Part 3: Why PII and the Privacy Act Are the AI Foundation Most Leaders Skip
    May 15 2026

    You can have the most secure AI stack in the country and still be in breach of the Privacy Act before lunch.

    Sarah and James close the series with the foundation underneath the foundation: personal information. James, now grounded on the security side, opens with a healthy push-back — surely if we own the data, we can use it however we want? Sarah, with the OAIC determinations in hand, takes that apart.

    What we cover

    APP 6 and purpose-binding: under Australia’s Privacy Act 1988, personal information collected for one purpose generally cannot be used for another. AI training, inference, and agent actions are all “uses,” yet most organisations haven’t mapped AI use cases to APP 6.

    The 2024 amendments: the Privacy and Other Legislation Amendment Act introduced a statutory tort for serious privacy invasions, a children’s privacy code, and stronger OAIC enforcement, including AUD $66,000 infringement notices.

    OAIC determinations: cases like Clearview AI, Bunnings/Kmart (facial recognition), and I-MED (patient data shared for AI training). I-MED’s de-identification was accepted, but it became a key APP 6 risk example.

    The bank scenario: three walkthroughs — inference drift, indirect prompt injection, and multi-agent purpose laundering — showing how compliant data becomes non-compliant AI use.

    Recommended controls: purpose registers, consent provenance, retrieval scoping, agent identity, and Meta’s “Agents Rule of Two.”

    Sources

    Privacy Act 1988: https://www.legislation.gov.au/C2004A03712/latest/text
    Privacy and Other Legislation Amendment Act 2024: https://www.legislation.gov.au/C2024A00128/asmade
    Australian Privacy Principles (OAIC): https://www.oaic.gov.au/privacy/australian-privacy-principles
    OAIC — Clearview AI determination (PDF): https://www.oaic.gov.au/__data/assets/pdf_file/0016/11284/Commissioner-initiated-investigation-into-Clearview-AI,-Inc.-Privacy-2021-AICmr-54-14-October-2021.pdf
    OAIC — Bunnings determination: https://www.oaic.gov.au/news/media-centre/bunnings-breached-australians-privacy-with-facial-recognition-tool
    OAIC — Kmart determination: https://www.oaic.gov.au/news/media-centre/18-kmarts-use-of-facial-recognition-to-tackle-refund-fraud-unlawful,-privacy-commissioner-finds
    OAIC — I-MED preliminary inquiries report: https://www.oaic.gov.au/privacy/privacy-assessments-and-decisions/privacy-decisions/Investigation-inquiry-reports/report-into-preliminary-inquiries-of-i-med
    EU AI Act overview: https://artificialintelligenceact.eu/
    California ADMT — CPPA announcement: https://cppa.ca.gov/announcements/2025/20250923.html
    Meta — Agents Rule of Two: https://ai.meta.com/blog/practical-ai-agent-security/
    NIST AI RMF: https://www.nist.gov/itl/ai-risk-manag

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    37 min
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