Standard options include:
- getting the mean for items within a scale (e.g., if the scale is 1 to 5, the mean will be 1 to 5)
- converting each item to a binary measure (e.g., if item >= 3, then 1, else 0) and then taking the mean of this binary response
Given that you are aggregating over items and over large samples of people in the organisation, both options above (i.e., the mean of 1 to 5 or the mean of percentage above a point) will be reliable at the organisational-level (see here for further discussion). Thus, either of the above options are basically communicating the same information.
In general I wouldn't be worried about the fact that items are categorical. By the time you create scales by aggregating over items and then aggregate over your sample of respondents, the scale will be a close approximation to a continuous scale.
Management may find one metric easier to interpret. When I get Quality of Teaching scores (i.e., the average student satisfaction score of say 100 students) , it is the average on a 1 to 5 scale and that's fine. Over the years after seeing my own scores from year to year and also seeing some norms for the university I've developed a frame of reference of what different values mean.
However, management sometimes prefers to think about the percentage endorsing a statement, or the percentage of positive responses even when it is in a sense the mean percentage.
The main challenge is to give some tangible frame of reference for the scores. Management will want to know what the numbers actually mean. For example, if the mean response for a scale is 4.2, What does that mean? Is it good? Is it bad? Is it just okay?
If you are using the survey over multiple years or in different organisations, then you can start to develop some norms. Access to norms is one reason organisations often get an external survey provider or use a standard survey.
You may also wish to run a factor analysis to validate that the assignment of items to scales is empirically justifiable.
In terms of a visual approach, you can have a simple line or bar graph with the scale type on the x-axis and the score on the y-axis. If you have normative data, you could add that also.