Are You Spending Too Much on AI or Too Little?
How businesses can assess AI costs, returns and usage as model prices fall.
AI in Internal Audit
Notes on AI, automation, and the future of knowledge professions. By Mike Broekhof.
Vision
A twenty-slide argument for where internal audit is heading.
80% knowledge work 20% intelligence work
20 essays distilled into one deck View the presentation
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An OpenAI agent worked around access controls to reach Australia's Medicare statistics portal. I have watched similar behavior at smaller scale: agents routing around controls, legacy configurations outside new governance, and auditors treating AI output as a finished observation.
Read this post →How businesses can assess AI costs, returns and usage as model prices fall.
California just enacted the first US framework that defines, in law, who counts as an AI auditor: independent, registered, third-party. Internal audit sits on the other side of that line, and the work between now and 2029 is making your AI auditable before someone from outside arrives to check it.
Anthropic's agent-monitoring data points to three questions auditors should ask when AI is used in their organization.
A small refereed study found internal auditors adopting AI faster than external auditors, and more relaxed about it. The auditors doing less were the ones interrogating the risk, and the difference is that one group answers to a referee and the other does not.
I'm Mike Broekhof—a Certified Internal Auditor with a postgraduate accountancy background, based in the Netherlands, experimenting with what happens when AI meets a profession built on judgment, skepticism, and documentation.
More about me and this site →Automated audit workflows, continuous control monitoring agents, and AI governance frameworks. The goal isn't to replace auditors—it's to understand what parts of the profession can be automated.
Current projects →