In the pursuit of clean and infinitely reusable AI data, I aim to help technical and decision-making executives understand how to apply long-proven, reliable management techniques to build AI that accelerates the gains in productivity and learning available to their companies.
Because AI is the perfect trigger to get serious about capturing the knowledge across your business, it also provides the impetus to introduce professional management disciplines to that effort to build the platform of knowledge-driven advantage this affords.
I first wrote a version of this post based on my experience of working within the 200-strong technical development team of a Big Four Australian bank on a transformational technical development intended to deliver its services via an array of mobile devices.
As a writer, I was enlisted to help produce high-quality technical documentation that could help the bank’s developers share knowledge and learn about the technologies that were its subject.
That its development team was not familiar with the editorial processes common to professional publishing was, in retrospect, not surprising, given that, to the best of my knowledge, none had worked in it.
But, as an AI engine is only as good as the data it is trained on, my experience there now enables me to offer some timely guidance on to how best to capture the full wealth of a team’s knowledge within a business seeking to optimise its productivity at the same time as building enhanced reliability into its AI data modelling.
Moreover, these straightforward, known techniques can be applied at low cost in parallel with the use of the now-commonplace workplace social technologies already in use within their businesses.
Applying such simple disciplines will position your business a long way ahead of those which have no such practices in place.
As in the sidebar to the right, the menu of pieces in this series runs as follows: