People
- Task frequency, repeatability, and required judgment
- Time split across high- and low-cognitive-load work
- Readiness and comfort with AI-assisted workflows
Who knows what?
AI Adoption
Your AI program is approved. No one has mapped where to deploy it.
The problem.
The board approved the investment and the vendors are selected, but no one has mapped which workflows are genuine automation candidates or how to sequence the transition. AI deployed into an unmapped organization fails gradually: adoption lags, resistance runs high, and the gains that justified the spend take twice as long to arrive.
What OWI does.
OWI builds the map down to the task level: what people actually do, and how rule-based versus judgment-dependent each task is. We identify where AI augmentation accelerates output, where errors are costly enough to keep human oversight, and how to sequence adoption around operational reality.
What the model captures.
Process
- Rule-based versus exception-heavy workflows
- Handoff points where AI would reduce latency
- Processes where human oversight must remain
Where can AI help?
Systems
- Tools already capable of AI augmentation
- Data quality and availability for automation
- Integration readiness of existing platforms
Where is data copied?
What you get.
A ranked, prioritized set of automation opportunities, ordered by the work people describe as the heaviest, each with a clear human-versus-AI designation.
See your organization's map.
We'll show you how the map is built, how anonymity is protected by design, and how it supports the decision in front of you.