Three frameworks we use every time.
AI adoption isn't a technology problem. It's a design problem. These are the three diagnostic and design tools we built to solve it.
The Trust Boundary.
Every AI workflow needs a clear line between what the system decides on its own, what it recommends for a person to approve, and what it should never touch. That line is the trust boundary.
- Data classification
- Draft generation
- Routine summarization
- Pattern matching at scale
- Customer responses
- Pricing and discount calls
- Hiring signal review
- Contract and SOW drafts
- Final client decisions
- Terminations and layoffs
- Legal or regulatory filings
- Board communications
The 30/60/90 Proof Timeline.
Adoption is not a feeling. It's a sequence. These three checkpoints turn AI rollout into something a board can read.
Who is using the redesigned workflow, how often, and on what. If usage is not there, nothing else matters.
The first efficiency gains show up in cycle time, cost, or throughput. Numbers replace anecdotes.
The story a PE partner or CEO can put in front of a board: what changed, what was saved, what scales next.
The Workflow Debt Audit.
When AI tools are live but the efficiency gains are not, the debt is in one of four places. Name it and the fix follows.
Nobody knows who signs off when the AI recommends something. Decisions stall. Work piles up in a queue no one owns.
The team does not trust the tool's output. They rerun everything manually. The AI is a second opinion, not a first draft.
AI output is produced, but the next person downstream has no way to receive it. Work moves into email, spreadsheets, and Slack threads.
Nobody measures whether the tool is actually reducing cost or cycle time. Adoption is assumed, never proven.
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