By Anthony Woollacott at September 20 2018 08:59:49
It is usual that knowledge processes take the form of projects to manage their execution. If the output of the process is a unique product, managing work as a project will result in obvious advantages. There are certain guidelines that can help an organization willing to improve their knowledge processes: Provide process description on how to approach work Try to figure out the best way to carry out a knowledge process, by making the best practices existing in your organization (or in your industry) explicit. Publish process definitions in a format that is easy to consult and understand. Provide tools that facilitate and standardize work Decide which tools are best to help knowledge workers carry out their work. Involving all affected knowledge workers in the process of deciding which tools will be used is very convenient, in order to obtain user buy in.
The key to process improvement is to clearly communicate process definitions (the way in which the company wants the processes to be carried out) to the people in charge of their execution (through training, process descriptions publication, etc...). The better process participants understand the process definition, the higher the probability that the process is carried out according to it. They are better implemented through obtaining buy-in than through imposing directives.
That is, they are increasingly becoming part of the basic circuitry of computers or are easily attached adjuncts, as well as standing alone in special devices such as office payroll machines. Many different applications algorithms are now available, and highly advanced systems such as artificial intelligence algorithms may become common in the future. Artificial Intelligence (AI), a term that in its broadest sense would indicate the ability of an artifact to perform the same kinds of functions that characterize human thought.
The possibility of developing some such artifact has intrigued human beings since ancient times. With the growth of modern science, the search for AI has taken two major directions: psychological and physiological research into the nature of human thought, and the technological development of increasingly sophisticated computing systems. In the latter sense, the term AI has been applied to computer systems and programs capable of performing tasks more complex than straightforward programming, although still far from the realm of actual thought. The most important fields of research in this area are information processing, pattern recognition, game-playing computers, and applied fields such as medical diagnosis.