Productivity
"Can we make people 20% faster?"
Capability
AI Transformation
Compounding value
Find the value that scales over time
Capability expands, workflows improve from real use, and the operating model stays resilient as the AI landscape shifts.
01Expand capability
Not just productivity, Capability.
Productivity is a useful signal. Capability is the transformation target.
Productivity
"Can we add more ai to current workflows?"
Capability
The company becomes queryable by people and agents
Productivity
"Can we ship more software with AI?"
Capability
Software is designed around real work from day one
02Self-improving loops
Value compounds when workflow improves itself.
Each run leaves usable signal for the next decision, action, and automation.
Capture
Requests, exceptions, service moments, and repeated work.
Context
Relevant files, history, options, and missing information.
Judgment
People or policy gates decide what should happen next.
Execution
Systems update, people are notified, and state moves.
Learning
Usage, exceptions, and outcomes shape the next run.
Capture
Requests, exceptions, service moments, and repeated work.
Context
Relevant files, history, options, and missing information.
Judgment
People or policy gates decide what should happen next.
Execution
Systems update, people are notified, and state moves.
Learning
Usage, exceptions, and outcomes shape the next run.
Learning → Capture
03Stay resilient as AI shifts
Build around the work, not one model.
Models will change. Keep the workflow stable, and route each task to the right mix of people, rules, and AI.
Node
Route work by what it needs.
Use people, rules, owned systems, and frontier models where each one fits.
Urgent + complex
Frontier cloud
Best intelligence.
SOTA models
Large context
Speed
Cloud
Not urgent + simple
Self-owned system
Best reliability.
Small models
Controlled context
Consistency
Self-owned
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