The licences are bought. Now make them count.
In short
AI adoption fails for a predictable reason: tools get rolled out, but the workflows around them stay the same, so people try a chatbot twice and go back to what they know. Adoption works when specific, repeated tasks are rebuilt around the tools, and when the team has clear guardrails about what may and may not be put into a model. We run hands-on enablement on your actual work, not generic prompt training, and leave behind the patterns, guardrails and internal examples that make it stick.
What you get
What changes when this works.
Enablement on your real work
We run sessions on the tasks your team actually does that week, so what people learn is directly applicable rather than a demo they have to translate.
Guardrails people can actually follow
A short, concrete policy on what may go into which tool, written so a busy person can apply it in five seconds, not a 40-page document nobody opens.
Patterns that transfer
The handful of techniques that survive contact with real work, documented with worked examples from your own context.
A shortlist of what to automate next
Enablement surfaces the workflows worth building properly. You finish with a ranked list, and each item on it is a candidate for a 10-day build.
Concretely
What we actually hand over.
No deliverable on this list is a document about the work. Each one is the work.
- Hands-on working sessions run against your team’s live tasks
- A written usage policy covering data handling, review, and disclosure
- An internal pattern library with worked examples from your context
- Tool selection guidance, including where you are paying for capability you don’t need
- A ranked shortlist of automation candidates with rough effort estimates
- A follow-up session once the team has had real time with it
In practice
A professional services team with tools already rolled out
Our clients’ systems are commercially sensitive, so these stories are anonymised at their request. Metrics are reported as ranges or percentages rather than raw figures.
Problem
Licences were bought for the whole team and usage had flattened within a month. Nobody was clear on what was permitted with client data.
Action
We ran hands-on sessions on live client work, wrote a one-page usage policy, and built an internal pattern library from the team’s own examples.
Result
Weekly active use rose from [X]% to [X]%, and the team produced a ranked list of [X] workflows worth automating properly.
Questions
AI adoption & training, answered plainly.
Why did our rollout not stick?
Almost always because the tool changed but the workflow didn't. People are asked to add a chatbot to a process that was designed without one, which makes it extra work rather than less. Adoption sticks when a specific recurring task is rebuilt around the tool and someone demonstrates it on real work.
Is this generic prompt training?
No. We run the sessions against your team’s live tasks that week. Generic training produces people who can prompt well in the abstract and still don’t use the tool on Monday.
What about data privacy and compliance?
We write a short, concrete usage policy covering what may be put into which tool, what needs review before it leaves the building, and what must never be pasted anywhere. Short enough that people actually follow it, a policy nobody reads protects nothing.
How does this connect to the 10-day builds?
Enablement is usually how teams find the workflow worth building. You finish with a ranked shortlist of automation candidates, and any one of them can become a 10-day MVP. There is no obligation to continue, plenty of teams take the list and do it themselves.
Related
The other four.
Most projects touch more than one of these. If you're not sure which yours is, that's what the scope call is for.
Start here
Scope your first workflow.
Tell us the process that eats your team’s afternoons. We’ll come back within one business day with a scope, a fixed price, and an honest answer about whether AI is the right tool for it.
- No sales sequence. One reply, from the person who’d build it.
- If we don’t think it’s worth building, we’ll say so.
Email us what the process is, roughly how often it runs, and who does it today. That is enough for us to come back with a scope.
Email usOr write to admin@vintrexlabs.com directly.
Prefer to talk
Book a 20-minute call.
Bring one workflow. We’ll tell you on the call whether it fits in 10 days, and roughly what it costs.
Pick a 20-minute slot
Opens our live calendar. Bring one workflow and we’ll tell you on the call whether it fits in ten days.
Loads cal.com in this page. No account needed.