Business practice
AI adoption.
Buying the tool was the easy part. We help SaaS and AI companies put AI to work where it pays, in the admin inside support, success and operations. Every use case gets an owner and a measure, so adoption shows up in the work, not in a license count.
A license count isn't adoption. It's a receipt.
Most rollouts stall in the same place. The tool arrives, a few people find a use for it, everyone else goes back to how they worked last quarter, and the renewal comes around with no way to say whether any of it helped. Nobody was against it. It just wasn't anybody's job. It's also rarely taught: in BambooHR's 2025 survey of US desk workers, 77% said their company allows AI, but only 32% said it provides training.
Adoption happens when a specific piece of work is named, a specific person owns it, and the result shows up somewhere you already look: the queue, handle time, the hours your team spends on reporting. We start from the work, not the tool. Which tasks AI should take on, which it should stay away from, and what has to be true before either answer changes. The rule underneath all of it: AI is an administrative staff officer. It compresses the admin. It doesn't make the people decisions.
The approach
Three words we work by.
Choose.
We start with your team's work, not the feature list. A short pass across the tasks your team repeats, from ticket summaries to renewal prep to the weekly report, sorted by how much time they take and how much judgment they need. You get a small set of candidates worth trying, and a clear line around the work AI should stay away from, written down so nobody has to guess.
Own.
Every use case gets one name against it. Not a committee, and not a champion network. One person who's accountable for it working and has the authority to change how the task gets done. Where the owner needs guardrails to say yes safely, they get them in writing.
Measure.
Each use case carries a measure chosen before the work starts, in a number your team already tracks. Handle time, first response time, hours on the weekly report, rework caught before it reached a customer. That's what tells you adoption is real, and it's the number that decides whether to widen, hold or stop.
The work
Four workstreams, in sequence.
Use case selection
A short inventory of the repeated work across your support, success and operations teams, scored on time spent and judgment required. You get a ranked shortlist of what AI should take on, and an explicit list of what it shouldn't, so the boundary is a decision rather than an assumption.
Owners and guardrails
Each shortlisted use case gets a named owner, plus the data rules and review steps they need to run it without asking permission twice. Where your AI use policy already answers a question, we point at it instead of writing a second version. Anything that touches performance, pay or separation stays with people and HR.
Build and pilot
The top use cases get built and run on real work with the people who'll use them. Prompts, steps and checks are written down as procedure, not as a demo, so the second person to do the task gets the same result as the first.
Adoption tracking
The measure for each use case is wired into where the work already happens, so adoption is visible without a survey. You leave with a simple view of what's being used, by whom, and what it changed, which is what the renewal conversation actually needs.
Related reading.
All insights Governance
Shadow AI is already in your company. Here's the map
Ask a CEO how many AI tools are in use across the company and you'll usually hear about the ones on the invoice. Ask the team anonymously and the list gets longer: free chatbot accounts, browser extensions, meeting notetakers, and AI features switched on inside software you
Governance
The AI policy your insurer and your customers will ask about
Most companies meeting the AI policy question for the first time do one of three things. They answer no and hope it doesn't matter. They answer yes on the strength of a template somebody downloaded and never circulated. Or they leave it, come back to it, and eventually submit
Questions
Asked before, answered plainly.
No. In most cases the tool is fine and the missing pieces are a named use case and an owner. We start with what you already pay for, and we only raise the question of a different tool when a use case your team cares about genuinely can't be done with it.
From your work and your obligations, not from a generic policy. Customer data covered by your contracts, anything your auditor or an enterprise customer's security review will ask you to explain, and any decision about a person's performance, pay or job. Those get written down as out of scope, so the boundary is visible rather than assumed.
It changes the order, not the answer. We ask people what worries them before anything is chosen, and use cases that would take work away from someone go to the bottom of the list. The first ones we build are the tasks people already describe as the worst part of their week.
A number your team already looks at, moving in the right direction. Handle time on a common ticket type, hours spent on weekly reporting, renewal prep that reaches a reviewer in better shape. If the only evidence is seats assigned or logins counted, that's a license count, and we'll say so.
Weeks, not quarters. The first use case is chosen and running inside the engagement, with its measure taken before and after. If the number doesn't move, that's a result too, and it's a cheaper one to learn now than at renewal.