Mike Olsen

AI Governance for Professional Work

Using AI while preserving professional accountability.

Thinking about AI, professional accountability, and the future of technical work.

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Essays in recommended reading order. Each builds on the previous.

1
How to Use AI Without Losing Accountability A practical framework for using AI where it is strong, avoiding unreliable answer-first workflows, and keeping professional accountability intact.
2
Knowledge as Capability: A Better Foundation for Human-AI Cooperation An operational definition of knowledge as the ability to produce outcomes, and why that makes human-AI cooperation easier to reason about.
3
Agent-Relative Tacitness: Why Some Knowledge Is Tacit for Humans but Not for AI Tacit knowledge depends on which agents can express and transfer it, and under what conditions.
4
Tacit Space Shrinkage: How AI Makes More Expertise Explicit As AI improves, more knowledge can be expressed, operationalized, and reused. That changes how organizations think about expertise and governance.
5
AI as Oracle vs Assistant: The Choice That Determines Whether You Can Govern It The most important AI deployment choice is whether you ask for answers or execution. One creates verification problems; the other makes governance possible.
6
The Strong Oracle Trap: Why Better AI Dialogue Still Does Not Solve Verification Thorough AI dialogue can improve outputs, but it still does not solve the verification problem in professional work.
7
Executable Knowledge Architecture for AI in Professional Work A framework for integrating AI into professional work while preserving expertise, improving reproducibility, and closing the governance gap around AI-generated output.
8
Capability Governance: How to Govern AI-Generated Artifacts A practical framework for governing AI-generated artifacts through defensibility, exposed methodology, empirical track record, and clear stakeholder obligations.
9
Agentic AI as Universal Interface: Why Expertise Becomes Software Agentic AI removes two barriers that kept expertise from becoming software: coding skill and system access knowledge.
10
Why Automating Expertise Gets Easier as AI Improves As AI lowers the barriers to implementation, more expertise becomes software. This changes where value holds and where it moves.
11
Ontology Generation: How AI Logs Turn Work Into Organizational Knowledge When AI becomes the universal interface, interaction logs can reveal both what an organization does and the concepts it uses.
12
What AI Benchmarks Miss About Real Professional Work Benchmarks like GDPval measure well-specified tasks with verifiable answers. They miss the ambiguous, judgment-intensive work where professional expertise still matters.
13
AI-First Software: What Changes When Language Becomes the Interface Generative AI changes software design by making language a usable interface, shifting applications from fixed screens toward on-demand interaction.
14
The Return of the Assistant: Why AI Beats DIY Knowledge Work AI assistants let knowledge workers delegate execution while retaining the autonomy they gained from doing the work themselves.
15
AI Governance Update for February 2026 Two years of AI progress tested the oracle-versus-assistant distinction. The framework still holds, but agentic AI created new governance gaps.
16
Answers to Critics: Where the AI Governance Framework Holds and Changes A direct response to serious criticism of the framework, including what changed, what was clarified, and what still holds.

Essays here begin as AI-generated drafts, which I direct, review, and revise. The ideas, frameworks, arguments, and style are wholly mine.

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