tgroenwals shared this post · Jul 21

Alex Miguel Meyer

Jul 20 · archived Jul 21

Adding a human next to the AI is not oversight.
Regulators do not care that your AI is fast.
They care who signed off.

EU AI Act Article 14. NIST AI Risk Framework.
Colorado AI Act. Texas TRAIGA.

All four require human oversight with three elements.
Context. Authority. Rationale.

Most governance frameworks skip all three.
They add a checkbox. Not a checkpoint.

Here is how to deploy Human-in-the-Loop across the 5 governance layers.

→ Ethics:
Named reviewer with real veto authority.

→ Risk Management: Tiered checkpoints.
High-risk pauses. Low-risk runs.

Muneeb Ahmed One thing I've noticed is that meaningful human oversight isn't about inserting a person into the workflow. It's about making sure someone has both the information and the authority to challenge the AI's recommendation. Without those two things, the approval becomes procedural rather than a genuine control.

Muneeb Ahmed Alex Miguel Meyer Exactly. Both are necessary, but there's a third piece as well: accountability. When a decision can be traced back to a specific person with the context and authority to intervene, oversight becomes much more than a compliance exercise.

Tanzeela S. This aligns closely with ISO/IEC 42001. Effective AI governance requires clearly defined roles, documented decision-making, and accountability throughout the AI lifecycle. Human oversight should be a measurable control, not just a policy statement.

Alex Miguel Meyer Author Tanzeela S. Well said. Standards like ISO/IEC 42001 move human oversight from intention to execution by making accountability something you can actually demonstrate.

tgroenwals shared this post · May 6

Alex Miguel Meyer

May 6 · archived May 6

50% of SMEs are stuck in Phase 1.

Another 30% are stuck in Phase 2.

That's 80% of companies that bought AI tools and got nothing back.

Here's why:

They skipped the ladder.

AI adoption isn't a tool decision.

It's a 4-phase climb.

Skip a phase and the gain disappears.

Phase 1: Foundation
Executive ownership of outcomes. Strategy tied to business goals.
Honest readiness audit before tools are bought.

Phase 2: Enablement
Clean data. Governance guardrails.
2 to 3 high-impact pilots running in parallel.

1 / 3

Micheal Akinbowale Most just get AI tools, paid for it and abandoned it later, Instead of getting the one that is most needed at the phase of their business. May 6

Tom Waite This breakdown explains why so many AI initiatives stall. Most teams rush into tools without doing the uncomfortable work in Phase 1 and 2. May 6

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