AI Readiness and Enablement
Assessment of whether the revenue data can support AI, then the architecture, scoping and enablement required to deploy it safely.
The data before the model
An assistant reading a CRM that cannot be trusted produces confident wrong answers, faster than a person would and at greater volume.
The engagement therefore begins with the condition of the data the model will read, and with the permissions that determine what it can see.
Deployment follows once those hold. It starts with tasks that are high in volume and low in consequence, and it establishes a review habit rather than a one-off sign-off.
The shape of the engagement
Phases, their overlaps, and the point at which each is accepted. Durations are scoped per engagement; the sequence and the acceptance points hold.
Buy this engagement
Assessment and build are drawn against a band of hours.
| Item | What it covers | Price | Add to cart |
|---|---|---|---|
| Hours - Band of 10 | Ten hours of RevOps engineering at $250/hour. Drawn down against any work: builds, fixes, integrations or advisory. | $2,500 |
Scope not listed here is quoted. The full catalogue carries every item.
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Our work on this
Published research and engagements covering the same subject, so the approach described above can be read at length rather than taken on description.
Deploying Claude Across a 140-Attorney Litigation Firm
The architecture in full: workspace isolation, a token scoped to named properties, skills constrained against the ways they can go wrong.
Read itThe Configuration Theory of RevOps
Why the data and object model are the work, and the model is what goes on top once they hold.
Read itWhat is delivered
Each item below is a document or an artefact the client keeps, not an activity performed.
Readiness assessment
The condition of the data the model will read, measured against the tasks it is intended to perform.
Access design
What the assistant may read and write, scoped to the narrowest set that supports the task.
Configured agents
The assistants or automations built, each constrained to a defined task and source material.
Enablement and review procedure
Training for the people whose work changes, and a scheduled review of output quality.
What this includes
How it runs
Readiness assessment
The data the model will read is measured for completeness, duplication and currency against the tasks intended for it.
Access design
Permissions and token scopes are designed to the narrowest set supporting the task, and verified by attempting an action that should fail.
Use case selection
Tasks are selected by volume and consequence, beginning where errors are cheap and frequency is high.
Build and constrain
Assistants are configured against defined source material rather than open retrieval, and their outputs are sampled.
Enablement and review
The affected roles are trained, and a review cadence is established for output quality.
Milestones
| Milestone | Accepted when |
|---|---|
| Readiness reported | Data condition measured against the intended tasks. |
| Access approved | Permission and token scopes agreed and verified by a failed attempt. |
| Use cases selected | Tasks chosen by volume and consequence. |
| Deployed | Assistants configured against constrained sources. |
| Review established | Output sampling on a schedule with a named reviewer. |
Ways of working
The assessment is done-for-you; deployment is ordinarily done-with-you, because the people whose work changes need to shape how it changes. The client provides access to the data in scope, a decision-maker for permissions, and the operators of the processes under consideration.
The engagement plan, the hour budget, the delivery spectrum and the weekly, monthly and quarterly cadence are common to every service and are set out in how we work.
Common questions
AI Readiness and Enablement FAQ
Because an assistant reading unreliable records produces wrong answers with more confidence and at higher volume than the person it replaced. The assessment establishes whether the data supports the tasks intended for it.
By scoping tokens and permissions to the narrowest set that supports the task, then verifying by attempting an action that should be refused. A refusal is the passing result.
Those that are high in volume and low in consequence, where errors are cheap and frequency makes the benefit visible quickly. High-consequence tasks follow once the review habit exists.
Output is sampled on a schedule by a named reviewer. A constraint approved once and never revisited is a constraint that will eventually stop holding.
Tell us about your needs and we'll provide a customized solution and timeline.