Most AI work fails after the model ships. Full Deployment is our end-to-end program: a 30-day assessment, a ruthless prioritization of where AI actually creates value, then build, pilot, and roll out — with the change management, governance, and training that turn a working model into a working business.
Most companies have a folder of impressive demos and a board deck full of pilots. Almost none of it is in production, used daily, by the people whose workflows it was built for.
The build team hands off. The business doesn't pick it up. There's no incentive system, no training, no measurement, no champion — so adoption flatlines two weeks after launch.
A consulting firm wrote the slides. An agency built the thing. A vendor sold the tool. Three different bills, three different theories of the case, and nobody answerable for the outcome.
A structured diagnostic across the business. We score every function on AI readiness, workflow friction, data infrastructure, and leadership appetite — and identify where AI moves the needle in the next six months versus the next two years.
From the assessment, a ranked use-case backlog with sized impact, build cost, and a path-to-value timeline. We choose Wave 1 — the two or three builds that can ship in the first quarter and pay for the program — and confirm the right governance posture before a line of code is written.
Our builders embed with your team and ship the Wave 1 use cases to production in 8–12 weeks. Live pilots with real users, baseline measurement against the metric we agreed on in Phase 02, and tight iteration loops — not 12-week sprints that produce a demo.
The build is the easy part. We run the change management — training programs, prompt libraries, internal champions, leadership comms, and the measurement loop that proves the program is working. Wave 2 builds start in parallel. By month 12, you're operating, not piloting.
A real program is custom. But the shape is consistent: a foundation in the first month, Wave 1 builds shipping by Q2, change-management and operating-model work running in parallel the whole way, Wave 2 builds in the back half.
A model is 20% of the work. Adoption is the other 80% — and it's where every AI program either compounds or quietly dies. Full Deployment includes it from day one.
Every Wave 1 build is paired with a rollout plan: which users go first, what training they need, who their champion is, and how their workflow has to change.
A practical AI operating model — usage policy, data handling, model selection, vendor approval — designed so the safe path is the default path and teams don't route around it.
Function-specific enablement, shared prompt libraries, and an internal champion network — so AI fluency keeps growing after our team rolls off.
Wave 1 + Wave 2 use cases live, used by real teams, measured against the metric you agreed to in Phase 02.
A baseline of AI fluency across the org, a champion network that keeps growing, and a prompt library that doesn't get stale.
A working governance framework — policy, review process, vendor stack — that survives the next wave of tooling change.
Measured against the baseline we recorded before the build. We don't take credit for outcomes we can't show on a ledger.
Thanks for your interest in deployly.ai. Tell us where you are with AI today and where you want to be 12 months from now. We'll come back with a scoping conversation within one business day.
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