AI governance maturity can be misleading. Many organizations rely on frameworks, policies, and dashboards that signal control but fail to reflect true understanding of AI systems. This episode explores the Governance–Understanding Gap, highlighting why unclear decision ownership and limited system insight create hidden risks in AI, ITSM, and Enterprise Service Management environments.

In this episode, we answer to:

What is the worst decision an AI system could realistically make in practice?

Which AI system in the organization is least understood and hardest to explain?

If an AI system makes a harmful decision, who is accountable for it?

Resources Mentioned in this Episode:

NIST website, framework "AI Risk Management Framework", link https://www.nist.gov/itl/ai-risk-management-framework

European Commission website, policy "Artificial Intelligence", link https://digital-strategy.ec.europa.eu/en/policies/artificial-intelligence

ISO Standards website, ISO/IEC 42001 standard, link https://www.iso.org/standard/81230.html

MIT Sloan Management Review website, article "A framework for assessing AI risk", link https://mitsloan.mit.edu/ideas-made-to-matter/a-framework-assessing-ai-risk

Stanford Human-Centered AI website, article " AI Index 2025", link https://aiindex.stanford.edu

Connect with me on:

LinkedIn: https://www.linkedin.com/in/theitsmpractice/

Website: http://www.theitsmpractice.com

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Credits:

Sound engineering by Alan Southgate - http://alsouthgate.co.uk/

Graphics by Yulia Kolodyazhnaya