However, anyone dealing with AI technology must not ignore the important aspect of interfaces. They are, in fact, the key to success, because tools and data per se do not achieve added value. They must be used for the right use cases and integrated into the relevant organizational processes.
It is therefore necessary to clarify how the technologies can be linked with applications and workflows to achieve a smooth overall process. For example: The data generated by AI usually is also required by other systems such as CRM or marketing solutions. In many cases, Azure provides a suitable platform for this, as AI solutions can be integrated smoothly into Teams, Office or other Microsoft applications.
In our above example, this means linking object recognition and the alert system for the shift supervisor. Here, the AI-calculated top alert level must result in the red light lighting up and an audible alert needs to be activated. To achieve this goal, the compatibility of the two systems needs to be verified.
In addition, the use case needs to be embedded in the organizational processes. It needs to be specified what actions on the part of the shift supervisor are required to remove the object lying in the way. Will she do it herself or delegate the task to a safety officer? Is a log entry required or does the management need to be informed?
These questions, in turn, may result in additional processes. If, for example, objects being in the way is a particularly frequent occurrence, production processes may have to be modified or safety precautions taken to mitigate such hazards. In the case of human error, training may be required to improve the handling of materials or tools.