The new distinguishing competency for organizations is simultaneously managing intelligence and artificial intelligence (AI). Managing human intelligence is a big enough challenge for most organizations, but managing that along with an artificial intelligence infrastructure requires different skills and approaches.
It is difficult to avoid the hype surrounding artificial intelligence, but artificial intelligence means different things to different people. Just as humans have different capabilities, artificial intelligence has different abilities. At a fundamental level, artificial intelligence is made up of large datasets and some embedded logic for processing the large data sets.
The simplistic approach is to look at artificial intelligence as an overlay on human intelligence where each is augmented by the other.
Artificial intelligence is typically optimized for specific applications, and there is not a general purpose form of artificial intelligence. Using AI as a blanket term for its different applications oversimplifies the implications that AI has for organizations and society
At some level, there are basic rules for the governance of artificial intelligence, but they tend to be at a higher level. There are few, if any, rules for managing the interaction between different algorithms.
But the governance of artificial intelligence is a multi-faceted challenge.
Co-mingling the insight of humans with the insight of machines is more of an art than a science.
As with most things, there are different approaches that individuals and organizations can take:
- Wait and see – watch what other organizations are doing
The danger is that a lot of the intelligence in the machines is built on top of third party data. This is both necessary and insufficient.