Answers are not traceable
A model produces a result, but you can’t show what it is based on. For a government organisation, that is a problem.
Driver
AI is on the agenda, but your data isn’t ready for it
The question comes from the top: what are we going to do with AI? The first pilot looks good. Until someone asks where that answer comes from, and it turns out that three departments mean something different by the same concept.
What’s Coming Your Way
Three Things You’ll Need to Address
The same term means something different everywhere
As long as concepts are not defined unambiguously, a model is adding apples and oranges.
Knowledge is written down nowhere
What isn’t written down can’t be used by any system. Much process knowledge exists only in people’s heads.
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Foundation first, then application
We check whether your data is ready
Where which data is stored, who owns it, what the quality is. This advice comes before any AI application, and sometimes the outcome is that you are not ready yet.
We record the meaning of your data
A model is only as reliable as the definitions beneath it. In BegrippenXL™ it is clearly stated what a concept means, and where that definition comes from.
We make sure your knowledge is recorded
In WikiXL™ you record how processes work and which agreements apply. In the Gold variant, you can ask questions about this with AI, based on what you have recorded yourself.
Is your data ready for AI?
Tell us what you are planning, and we will tell you what needs to be in order first. An honest answer, even if that answer is no.
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