Rolling out Copilot with PromptingBirds
What we have learned from more than 100 projects – and why the rollouts that do everything right on paper still fail.
Most companies fail at adopting AI because they try to bring 100% of their employees along.
A dedicated team, clear roles. Why this is the difference for you between “the project works” and “the project fizzles out.”
Real numbers from ongoing client projects. Which KPIs we use, which ones we deliberately don’t, and why “adoption rate” on its own tells you nothing.
What you will learn in this video
- How a Copilot adoption rate was raised from 14% to 67% — and which three steps were needed to do it
- Why, in the HR department of a care services provider, the decisive ROI factor was not the working time saved but the reduced litigation risk
- How automated ticket categorization in IT support lowered the escalation rate from 38% to 11% — and what that has to do with employee satisfaction
- Why a cleanly measured baseline before the project is the basic prerequisite for any demonstrable success
- Which three criteria determine whether a use case even offers enough leverage
- When, from an ROI perspective, an AI project honestly isn’t profitable — and how that is identified in the audit
Who is this relevant for?
This page is aimed at decision-makers who have to justify an AI investment internally — managing directors, CFOs, IT managers and project leads in manufacturing companies, service businesses and service-intensive organizations. It is especially relevant for anyone who has already rolled out a Copilot license and isn’t making progress with adoption or with proving its value.
The next step
Anyone who wants to find out whether similar results are possible in their own company can clarify that directly in an initial consultation — based on their own departmental structure, processes and starting situation.