Micro Experiments
Test where AI makes work better before you scale the story.
Micro Experiments are bounded tests of usefulness: one piece of work, one value hypothesis, and enough evidence to decide whether the idea deserves another step.
The Micro layer
Small enough to learn. Specific enough to matter.
Micro is OSC’s applied test layer. Instead of starting with transformation, it asks what changes in a real task, decision or handover when AI enters the workflow.
Start with real work
Anchor the experiment in a recurring task, decision, handover or bottleneck. “Use AI more” is not a testable unit of work.
Define what better means
Look for an observable change in judgment, quality, speed, rework, clarity or friction — not merely more generated output.
Keep failure cheap
Keep commitment low enough that a negative result remains useful evidence instead of becoming a success story the organization has to defend.
Keep judgment accountable
AI can generate, structure and compare. A human still validates meaning, risk and whether the result deserves the next commitment.
Latest experiment
See the Micro method in practice.
The newest experiment is the quickest way to see what OSC is testing now: one bounded piece of work, one value hypothesis, and evidence that can change what happens next.
A Workflow Is Not a Collection of App Clicks
An AI-native workflow carries reliable knowledge into human decisions and preserves what happens afterward. A practical approach to organizational handoffs.
Latest experiments
Previous Micro Experiments
Browse earlier bounded tests of where AI changes the work, what evidence appeared, and which ideas earned another step.
A Workflow Is Not a Collection of App Clicks
An AI-native workflow carries reliable knowledge into human decisions and preserves what happens afterward. A practical approach to organizational handoffs.
A Task Brief Is Not an Organizational Brief
A product leader's lesson: an authorized request can still miss what customers and the organization need. AI agents need distributed context, reasoning and clear authority boundaries.
AI Agent Delegation: Don’t Tell AI What You Would Do. Tell It What Success Looks Like.
AI agent delegation works best when leaders define success, context, risk tolerance and failure protocols without forcing the system to imitate their own judgment.
The Cognitive Exoskeleton Should Make It Cheaper to Be Wrong
AI-powered experimentation should let more ideas reach evidence before investment. Human judgment still decides which deserve to move forward.
The Work No Longer Needs to Travel to the Expert
An expertise queue forms when work has to wait for scarce human knowledge. AI can move reusable cognition closer to the work while preserving judgment, trust and accountability.
AI Maturity Is the Wrong Unit of Analysis
An AI maturity assessment can reveal capability gaps, but it is a weak starting point for deciding what deserves investment. Start with the workflow, the value mechanism and the evidence—then use maturity to close the gaps that matter.
AI Is Making Me Much Faster at Consulting Work. That Makes the Old Value Logic Awkward.
AI consulting value changes when research, synthesis and reporting get cheaper but judgment, trust and accountability do not. That changes what clients should pay for and what consultants should optimize.
Why I Am Firing My AI Specialists
My specialist GPTs made complex work easier to govern. Then their handoffs became part of the problem. I am rebuilding the AI operating system around shared context, reusable capabilities and orchestration.
AI Value Hypothesis: Use AI to Start the Idea… Then Try to Kill It
AI can help us explore ideas before certainty exists. The harder discipline is using the same cognitive leverage to challenge those ideas before enthusiasm becomes investment.
AI Agent Decision Quality Improves When I Stop Writing Better Prompts
Better prompts helped me use AI. Continuous enablement changed how I work with it. The real leverage came from turning repeated corrections, trade-offs and exceptions into a system that improves AI agent decision quality over time.
AI Did Not Give Me More Ideas. It Stopped Existing Ones From Dying.
AI creates a resource paradox: it consumes scarce physical capacity while releasing scarce cognitive capacity. A personal experiment in project continuity shows how that released capacity can preserve and advance work that would otherwise decay.
When AI Makes a Decision I Do Not Like
Trust in AI grows when decisions are traceable to durable reference data, constraints are explicit, exceptions require approval, and human judgment remains the validation gate.
Related thinking
Read the pattern behind the experiment
Use these routes when a small result raises a broader question about leverage, friction, scale or executive implication.
Cognitive Leverage
Where AI expands the reach of human judgment by reducing the cognitive effort required to explore, structure and synthesize.
Interface Tax
Where the effort required to access AI capability consumes the value the capability was supposed to create.
Macro Frameworks
What repeated signals from individual experiments imply for operating models, decision rights, investment and scale.
Executive Briefings
Weekly synthesis of what emerging evidence and OSC’s frameworks mean for executives navigating the Cognitive Revolution.
From Micro to Macro
Small evidence matters when it changes what you are willing to believe next.
Micro tests whether AI improves a bounded piece of work. Macro asks what repeated evidence means for operating models, trust, decision rights and scale.
Explore Macro FrameworksFollow the weekly synthesis in the Open Source Executive Briefing →
