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10 Sep 09

What Enterprise Could Learn From AI Research History

The analogy itself between neural networks and a real community-based company is striking, and so are the similarities between the limitations of this approach and some Enterprise 2.0 concerns. Neural networks encountered two big problems: relevancy and convergence (they couldn’t ensure to converge onto the desired pattern, and sophisticated training techniques, such as back-propagation, were necessary to ensure convergence). Social media are facing the very same problems in the enterprise: how could we ensure that communities lead to the right consensus for applicable decisions to be taken? I evoked some possible trails in my last post, and this is a crucial point.

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AI networks neuralnetworks socialnetworks connections businessprocess kaizen process

Should, and will, the Enterprise 2.0 follow the same track as AI did? If so, next move would be to get rid of the big business processes we all know, and replace them with micro-processes applicable at individual scale. For instance, the way Japanese coworkers are able to make a consensus emerge from community-based workshops, one of the pre-requisite of Kaizen, rely on their heavy sense of “doing the right thing”. To set up such micro-processes is a radical move from where we are and where the most daring organizations try to go,