Celadon
ServicesAdoption
Most organizations are already paying for AI. Far fewer can show what it changed. Adoption is a fixed-fee program that turns tools your company already owns into daily practice for the teams that were supposed to benefit — with usage, quality, and time recovered measured per team rather than assumed.
Book a strategy call →BCG's widely cited rule of thumb puts roughly 10% of the effort in an AI initiative on algorithms, 20% on technology and data, and 70% on people and process. Most budgets are allocated in almost exactly the opposite proportion. The licences get bought, an all-hands gets scheduled, and the hard part — changing how specific people do specific work — is left to happen on its own.
It does not happen on its own. A finance controller and a field technician need completely different things from the same tool, and a single introductory session speaks usefully to neither. Meanwhile the people most likely to experiment are the ones who least need permission, and the teams where the hours are actually being lost never start.
The result is the pattern every leader recognizes: a healthy licence count, a handful of enthusiasts, and no defensible answer when someone asks what the spend produced.
What is actually being used today, by which teams, for what tasks — established before any program starts, so improvement can be demonstrated rather than claimed.
Concrete, written guidance for each role that matters: the tasks worth handing over, the prompts and patterns that work, and the ones that produce confident nonsense.
Small sessions with each function, using their own live work rather than generic examples. People leave having done something useful, not having watched a demonstration.
A short, specific policy covering what may be entered, what must be reviewed, what must be disclosed, and who to ask — not a forty-page document that protects nobody.
Named champions in each function, briefed and equipped to answer questions after Celadon leaves, so the capability survives the engagement and staff turnover.
Usage, task coverage, and recovered time tracked per team against the baseline — with the honest read on which teams it worked for and which it did not.
Establish what is licensed, what is used, and where the hours are going today. Agree which teams and which tasks the program will be judged on.
Build the playbooks, the policy, and the session plan around those specific roles and tasks — reviewed with the function leads before delivery.
Run the sessions team by team on live work, support the champions through the first weeks, and correct what is not landing while it is still cheap to correct.
Re-measure against the baseline, report what changed per team, and hand over the playbooks, policy, and measurement approach for the business to run.
Attendance is not adoption, and a full room proves nothing. This program is priced as a fixed fee typically in the $20,000–$60,000 range depending on the number of functions in scope, and it is judged against the usage baseline taken at the start. If a team does not move, that appears in the final report rather than being averaged away. Celadon would rather write that down now than deliver a well-attended program that changed nothing.
Adoption does not require a Celadon build. It works on the AI your organization already licenses — Copilot, ChatGPT Enterprise, Gemini, Claude, or a system built by someone else — as well as on systems Celadon delivered. It is not a phase in the delivery progression and it is not a prerequisite for any of them.
Celadon does not resell seats, take vendor commission, or recommend a platform it has a commercial interest in. Adoption does not include building new systems, and it is not an open-ended change management retainer that expands to fill the calendar. It also cannot rescue a tool that is genuinely the wrong fit — if the honest finding is that the licences should be reduced rather than promoted, that is the finding.
If the work needs a system built, that is Build. If a live system needs technical care, that is Operate. If the question is what to invest in next, a Decision Sprint answers it.
A focused conversation about what your teams already have access to, who is actually using it, and what it would take for that to change.
A focused conversation about the business problem, the systems involved, the constraints, and what would need to be true for AI to create measurable value.