The expert – dumber than the crowd/machine?

The book uses the diagram above to show that where prediction is needed, the Expert is often beaten by AI, and  never  better than a collective view.

Does this mean “the death of the expert”? In the new connected world, do the experts have a role, or are they squeezed out?

We have addressed this issue already on this blog (for example here and here) where we recognise that a Community of Practice will ideally always contain more knowledge and experience than any one expert. This results in a shift in the expert’s role. From being the “font of all knowledge” they become the “stewards of knowledge”. Stewardship is different from ownership – a steward maintains and nurtures something that is not theirs, for the benefit of others.

The exerts can take on a Knowledge Management role that involves becoming a Practice Owner or Practice Steward for their domain of practice, and playing a coaching an supporting role in the relevant Community of Practice.  They also share their own knowledge through coaching, training, and contributions to the Community, and take technical roles on difficult and challenging pieces of work where they can apply, with wisdom, the knowledge of the community. 

The point which the diagram above does not make, is that the Expert becomes a critical part of the collective. Without the experts taking part in the collective, the collective can become “the blind leading the blind”.   We have seen this in one large organisation, with huge communities in which the experts take no part as they are “too busy”. Not only are questions in the communities not answered (or answered with platitudes), the experts deride the communities as having no relevance. 

Similarly the expert can be squeezed out by AI – but only to an extent. remember, the AI algorithm has to come from somewhere, and generally it comes from the expert themselves. A knowledge engineer, will work with the expert to expose and codify their knowledge, and to convert it into a machine-readable algorithm. The AI can then reproduce the decision making process of the expert, mking decisions faster and more reliably that the expert ever could, based on a range of data the expert could not handle. 

However the expert still has a role – to monitor teh algorithm, to fill in the gaps where the algorithm does not yet work, to interpret the insights and correlations which the AI identifies, and to understand the “Why” behind the algorithm. AI becomes a tool for the expert as much as a replacement for the expert.