Services
AI Implementation & Adoption Support
The hard part is not the model, it is getting the front line to use it. Training, tracking, iterating on feedback until the habit sticks — with our people on your site every week.
Most enterprise AI projects do not fail on technology. They fail after go-live. The system ships, the demo goes well, training happens — and three months later the dashboard shows single-digit daily users.
The reasons are usually mundane. The old process still works and the new tool adds a step. The answers are occasionally wrong, so people stop trusting it after two tries. Or nobody ever told them the task now belongs to the system. None of that is a model capability problem. It is an adoption problem.
What the support actually looks like
Before go-live. We rework the process alongside the people who own it — shift leaders, regional managers. Not a memo saying "use this from now on", but fitting the tool into actions they already perform: when it is used, which step it replaces, and who stops doing that step.
After go-live. On site weekly for the first three months. We look at real usage data, find the people using it least, and ask them face to face. The answers are usually plain: a button buried too deep, a category of question answered badly, it does not work on their phone. You never find these in a meeting room.
Ongoing. We collect the questions that repeatedly return nothing useful, then go back and fix the data, tune retrieval, adjust prompts. A knowledge base's accuracy is not fixed on launch day; it is raised gradually through that loop.
What gets delivered is a habit, not a system
Our definition of success: three months later you can remove us, operations continue, and nobody wants to go back to the old process.
At one manufacturer, equipment-maintenance Q&A reached the point where night shifts handled most common faults independently four months in — average time-to-fix down 45%, with more than 3,200 pieces of maintenance experience captured. That number did not come from the model. It came from three months of our people on the shop floor, fixing things one at a time with the shift leads.
Who this is for
- The system is live but usage will not climb
- Launch is imminent and you have seen "nobody used it" before
- You have changed vendors once; technical delivery was fine but the business side never picked it up
The system does not have to be ours. We spend a week diagnosing actual usage before deciding whether to take it on — if the problem is that the system was pointed the wrong way, adoption support cannot rescue it, and we will say so.
Common questions
How long does it run
Three months as standard, covering go-live through to the habit forming. Where usage is climbing clearly and the business side can iterate on its own, it can end earlier.
What do you need from us
Above all, one business owner with the authority to change the process. AI projects driven purely from IT have a visibly lower success rate — because what has to change is a business action, not an architecture.
How is this different from training
Training explains how the tool works; that takes a day or two. Adoption support is staying until it is genuinely used, which means changing processes, adjusting the system, and dealing with resistance. Training without adoption support is the single most common failure mode we see.