Putting AI to work where it helps people practice, decide, and move faster.
Applied experiments in AI-supported practice and workflow intelligence.
Lab: interactive prototypes. Concepts: speculative futures.
Two live experiments — open them in a new tab.
A continuous operating rhythm.
Find high-value workflows.
Identify where work is slow, inconsistent, repetitive, risky, or knowledge-heavy. Look for expert bottlenecks, manual synthesis, repeated handoffs, slow decision cycles, and workflows with unclear ownership.
- Step 01Discover
Find high-value workflows.
Identify where work is slow, inconsistent, repetitive, risky, or knowledge-heavy. Look for expert bottlenecks, manual synthesis, repeated handoffs, slow decision cycles, and workflows with unclear ownership.
OutputWorkflow inventory and opportunity backlog.
AI adoption · continuous operating rhythm
Four ways to see adoption move.
AI Use Case Portfolio
Opportunities moving across discovery, pilot, scale, sustain — adoption as a managed portfolio, not a wish list.
AI Adoption Health
Behavior change, not seat licenses — adoption surfaced as a quiet line that rises when work actually changes.
The systems people actually practice with.
AI-Supported Practice Infrastructure
Sparring partners across the table — scored against a rubric so learners know exactly what to sharpen next.
Evaluation + Guardrails
Rubrics, harnesses, and review checkpoints that keep AI behavior honest — judgment held by a human hand.
