INFORMAZIONI SU QUESTO PODCAST 🔗
The Modern CDO is the podcast for data and AI leaders who run enterprise organizations — not for people who want to become them.
Hosted by Richard Muirhead — Fractional CDO, AI strategist, and former IBM Distinguished Engineer with 30+ years of enterprise experience and 130+ patents — each episode delivers a single sharp thesis, three sector-specific evidence cases, a practical decision framework, and one falsifiable prediction
No guest roundtables. No vendor content. No hedging. Episodes run 5-10 minutes.
Hosted by Richard Muirhead — Fractional CDO, AI strategist, and former IBM Distinguished Engineer with 30+ years of enterprise experience and 130+ patents — each episode delivers a single sharp thesis, three sector-specific evidence cases, a practical decision framework, and one falsifiable prediction
No guest roundtables. No vendor content. No hedging. Episodes run 5-10 minutes.
Conduttori:
Frequenza di aggiornamento:
daily
Durata media dell'audio:
7 minutes
Inglese
Regno Unito
38 episodi
da 01 Maggio 2026
episodic
ULTIMO EPISODIO 🔗
For fifty years, the rule was simple: garbage in, garbage out. Bad data produced output that obviously looked bad, so someone caught it and fixed it. AI broke that bargain. Feed a copilot flawed data and it returns a fluent, confident, well-structured answer that happens to be wrong, and nothing on…
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EPISODI PRECEDENTI 🔗
Most AI governance ends at the moment the real risk begins. The day a model goes live is the day it starts to drift.
A model learns a snapshot of the world, and the moment it ships that snapshot starts to age. Accuracy and fairness degrade silently, with no alarm, because the governance was all spe…
Generative AI is most dangerous at the exact moment it is most convincing.
It fails fluently. It produces a confident, polished, authoritative falsehood with no signal that it is wrong, leaks whatever an employee feeds it, and varies its output from one run to the next. Govern the real failure mode…
A model can be 95 percent accurate and systematically unfair to the people who matter most. The aggregate number hides it.
Treated as a late-stage checklist item, bias gets discovered too late to fix, rationalized away under deployment pressure, or missed entirely. A single accuracy number is not a…
The moment a real decision can be blamed on a model, you have built a machine for laundering accountability.
A model makes a consequential decision and no human owns it. The data scientist points to the business, the business points to the model, the sponsor treats the output as objective. Everyone…
When a model starts making bad decisions and you cannot trace what it learned from, you do not have a problem. You have an outage with no off switch.
A model runs beautifully for a year, then starts producing bad outcomes nobody can diagnose, because its lineage was never captured. An untraceable m…
Standing up a separate AI governance function is how you rebuild data governance from scratch and get it wrong twice.
Most AI risk is data risk with higher stakes: bias from training data, privacy from data use, unexplainability from missing lineage. Wall off a separate tower and you rebuild lineag…
Most companies wrote their AI policy before they had any idea how much AI they were running.
Writing the policy is the easy part. The AI it governs is mostly invisible: shadow models, AI features inside SaaS tools, employees pasting company data into public generative tools. You cannot govern what …
AI governance that only knows how to say no does not make AI safer. It drives the risky work into the shadows.
A brake is not a steering wheel. A function that can only stop makes itself an obstacle, and obstacles get routed around. The risky AI work continues with no oversight at all, while the fu…
MDM is never finished. It is operated. And the operating budget is the line item every program forgets.
Every MDM business case has a detailed build budget and a stewardship line that is tiny or missing. Go-live is not the end. It is the start of the longest and most important phase. The exception …
Dichiarazione di non responsabilità: Il podcast e la grafica incorporati in questa pagina provengono da Richard Muirhead, che è di proprietà del suo proprietario e non è affiliato o approvato da Listen Notes, Inc.
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