Knowledge management is a wide topic. Here we are going to focus on how NLP can be used to perform the complex task of eliciting and formalising knowledge. Many techniques exist in the field of Knowledge Management for elicitation but we believe that NLP can go quite a bit further thanks to its specific and advanced techniques for exploring the structure of subjective experiences.
We call classic elicitation the elicitation which explores 'explicitable' and 'consciencable' knowledge. This kind of elicitation is mostly done by observing and interviewing and is used in most Knowledge Management approaches. Unfortunately, as some of our clients have experienced in the past, this often doesn't seem to give very good results, because much of what people are actually doing is more on the unconscious and implicit level.
This kind of behaviour is sometimes referred to as 'intuition' or 'feeling' for example, but we'll show here that much of these processes can in fact be formalised. This is why NLP can be of great help because it allows knowledge elicitation on many levels.
NLP advanced elicitation
NLP elicitation goes far beyond classic elicitation because it doesn't limit itself to explicit and conscience behaviour. It goes a big step further by taking into account many psychological implicit aspects which are mostly unconscious (whether they can easily be brought to consciousness or not).
NLP can be qualified as applied psychology and the study of the structure of subjective experience, which is exactly what we want to do in knowledge elicitation.
Here are some of the NLP techniques or concepts which make the difference for knowledge elicitation:
Examples of relevant techniques
Meta Programs (see KCard): Meta programs guide one's behaviour. There are a number of identified meta programs all of which can contribute. People often act and make decisions using their meta programs without really knowing so. For example to compare two products quickly (not formalising or following a company procedure) they might seek differences rather than similarities, doing the inverse might give poor results, so it's important to spot this during elicitation.
Modelling universals: Modelling universals are filters through which perceptions are processed during storage and retrieval of perceived (through the VAKO system) information. These filters are classified in three groups which are generalisation, deletion and distortion. The Meta Model can be used as guidance as about how these three groups can be further categorised. When eliciting knowledge it is of prime importance to discover how one is filtering information in both ways as this filtering must be reproduced in order to successfully reproduce his skills.
Meta model: The Meta model is a language technique which is aimed at recovering impoverished portions of one's experience. Specifically it is a set of language patterns one can identify and the reactions to adopt in order to explore a person's model of the world. This is of course a valuable tool in knowledge elicitation as it allows uncovering the expert's knowledge with formalised language techniques. A side note is that many KM techniques focusing only on this aspect (interviewing experts) are much less efficient than PLM on this aspect (while as shown here PLM also explores many other aspects) because PLM offers a clear formalised efficient process to guide the interviews.
State, physiology, internal representation and behaviour: NLP stipulates that states, physiology and internal representation are linked together. That means that change of one will lead to change to another. Also, behaviour depends in particular on state. In eliciting skills, it is important to understand that the state in which the expert is in while performing highly influences his performance, and as state depends on both physiology and internal representations, then it appears that all these three components must be elicited to best realise KM elicitation and obtain reproducible results.
Values, beliefs and attitudes: Attitudes by some aspect guide behaviours, more precisely attitudes are kinds of automatic or reflex behaviours or contributions to behaviours. Attitudes are based on beliefs, which in turn are 'related' to values. Obviously, knowing and understanding one's values and beliefs will help a person reproduce his attitudes, thus his behaviour. This can help understand the motivations for someone's behaviours during knowledge elicitation in order to better make them reproducible.
Milton modes (special): The Milton model has a special role in knowledge management as it isn't a technique directly aimed at eliciting knowledge, it is rather used to facilitate the human interactions which are necessary to attain the primary goal. The Milton model is a set of language patterns which help building communications which will be well accepted by your counterpart. Its basic mechanism is to provide conceptual containers which the listener will fill himself with his own content which depends on his own experience. This avoids the risk of loosing your counterpart's attention or approbation by delivering a too specific message which is badly interpreted because of personal history.
NLP and KM in general
These techniques just point out how NLP can improve the knowledge elicitation process and illustrate how NLP can be used as a foundation for KM. Retrospectively this should not appear surprising to people knowledgeable in both fields, however, using NLP as a core KM practice is quite innovative and very efficient.