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Organisational fitness is being capable to do the job efficiently and creating growth but fitness does not equally deliver effectiveness, sustainability or long-term survivability.  The latter being characteristics of organisational wellness.  Best outcomes require both fitness and wellness, however, fitness is easier to deliver, measure and reward. Balancing the ease of short-term measurement with long-term consequences is a critical task for leadership. “Fitness” in human terms is a means of doing repetitive exercises with the aim of maintaining or improving physical condition, it is focused on physical health. We can measure a person’s level of fitness. “Organisational Fitness” is an approach to understanding current efficiency and performance of the company based on qualitative measurement. Organisational fitness provides metrics for the critical areas of systems and processes and through KPI’s enable management and reporting by comparison.  Fitness is seen as physical exercise t

The unimagined: unimagined  Your mind will have already assumed that this chapter, from its title, is presenting the unimagined outcomes, a discourse to overly optimistic viewpoints.  It is reasonable that anyone reading a book on Pathways to General AI will have interrupted the word, “unimagined” within this context. Before you skip this chapter as you are a believer that AI is the future or don’t need another comfort blanket chapter speaking to the fearful of AI,  this chapter is neither. It is not a wild fantasy of the possible nor is a negative chapter of the unknown risks and likely machine run apocalyptics. This chapter presents a matrix which enables the holder of either viewpoint to debate and discuss the unimagined together.  However, to reach the matrix we have to create a framework first, which gives the reader a clear and coherent communication tool to position imagined AI initiatives.  THE SIMPLEST ECONOMIC MODEL Figure 1 presents a straightforward process. Raw materials

Data ethics is the integration of ethical thinking with the constraints of data and centres on how to make better decisions that ensures ethical outcomes. Data ethics is a broad topic encapsulating data and ethical decisions, data-based judgement, bias, ethics of collecting and analysing data, and the ethics of automation created by data.   The data we collected in 2010 could paint a low quality, black and white, abstract picture of our world and the data could be used to inform, frame or guide. In 2020, the data collected can describe our world in high-definition colour and is being used to mould, form and shape actions in complicated environments. We are accelerating our collection and use of data.  Boards need to get deep and dirty with data and the ethics of judgement based on data, it is complex and often requires new skills.   Boards are biased towards professionals who represent experience in finance, legal, operations, marketing and sales.  These “core functions” are supported