During the pilot phase, you test the AI solution under real-world conditions in a clearly defined area of your facility.
This is not just about simply “switching on” the software, but about demonstrating that the technology relieves your team and improves the quality of care.
The subsequent evaluation provides you with a sound basis for decision-making: Is the investment worthwhile for a facility-wide roll-out?
Pilot projects create a protected environment for this. They make it possible to test AI in practice, clarify open questions and further develop the application together.
A meaningful pilot requires you to clearly define the test area and assemble a mixed test team. You can find tips on this in Step 5: Involving stakeholders.
Ask the manufacturer directly about a trial or pilot licence. Test phases can often be offered at a reduced price by the provider or financed as a funded business case from your own digitalisation budgets.
A protected pilot run protects you from costly bad investments and helps ensure practical success:
Real-world test instead of a paper exercise: You can see clearly whether the AI works with your real care data and processes.
Fix problems on a small scale: Resolve technical hurdles or data-protection questions within a protected environment.
Build acceptance within the team: The pilot professionals become local experts and convince the rest of the team through their own experience.
• Are the concept-defined goals (e.g. time saved on documentation) being achieved?
• What concrete added value do care staff and people receiving care experience in everyday practice?
• Does the system help your facility to be perceived as a modern employer?
• What everyday obstacles arise? Is the AI used regularly or bypassed?
• Is the software intuitive to use, or does it require too many clicks/inputs?
• Are the AI results plausible and traceable?
• Who reviews recommendations? Is it ensured that decisions are not adopted without review?
• What process adjustments or new roles are needed in everyday practice?
• Are the training concepts sufficient, or is more training-on-the-job needed?
• Does the AI system work equally reliably in all care situations and for all resident groups?
• Does the AI for example correctly handle different accents, dialects or language proficiency levels within the team without disadvantaging staff with a migration background?
• How do we monitor the performance and accuracy of the AI after implementation?
• Which metrics do we track over the long term?
Use these questions as a checklist for your project evaluation. They help you identify potentially overlooked topics and derive targeted improvement measures.
The success of an AI project is not measured solely by technical metrics such as response time or failure rate. Only when all three dimensions are rated positively is your system ready for scaling.
| Evaluation dimension | What you need to look out for | What you need to look out for |
|---|---|---|
|
1. Technical performance |
Does the software run stably, accurately and reliably within the usual workflow? |
Error rates, system failures, software response times. |
|
2. User acceptance |
Does the team trust the system? Does the software make their work easier, or does it cause frustration? This is where you can see whether the involvement from Step 5 has had the desired effect. |
Surveys, observations in everyday practice, usage statistics. |
|
3. Quality of care |
Does the tool increase resident safety or save time for personal interaction? |
Fall rates, care reports, feedback from residents and family members. |
Standard training sessions in a seminar room are rarely sufficient. Support staff directly on site during the first weeks of work. This training-on-the-job makes it possible to clarify questions in day-to-day care immediately and address concerns directly.
Do not consider the use of AI only from a cost perspective. In times of skilled-worker shortages, modern technology is a genuine competitive advantage.
“Care professionals can now choose where they want to work. [Employee benefits] today include things such as the use of technology and assistive tools that make day-to-day care easier, where you receive the best possible support while working […] and, with higher levels of care needs, can spend more time with people.”
Every care facility has its own structures. A good core system is not enough – the provider must be willing to make system changes that fit your processes.
Avoid false perfectionism. The aim of the pilot phase is a functional, safe system that is accepted by the team – not a flawless solution from the very first minute.
Demand flexibility from the software manufacturer. Describe your specific workflows and ask directly: “How can your software be adapted to our requirements?”
At the end of the evaluation, you will have the following foundation:
Evidence-based data on the benefits, costs and time savings of AI.
Feedback from your care management team on barriers and adjustment needs.
A clear, well-founded decision for or against a facility-wide roll-out of the AI system.
Was the pilot phase successful and do the figures add up? In the final Step 7: Scaling, you will learn how to establish the solution sustainably throughout the entire facility and ensure its continued use.
