More Isn’t Always Better: Using Predictive Analytics to Show Adding More People Doesn’t Always Help

HOW AI-DRIVEN PREDICTIONS BECOME SIMULATIONS This report builds on my, where we we created predictions of what the outcome will be of software delivery work in progress, based on AI models trained on an organisation’s past behavior. Not necessarily, In the distribution there is a “shifting left” or bunching up of durations: Perhaps this grouping […]

HOW AI-DRIVEN PREDICTIONS BECOME SIMULATIONS This report builds on my, where we we created predictions of what the outcome will be of software delivery work in progress, based on AI models trained on an organisation’s past behavior. Not necessarily, In the distribution there is a “shifting left” or bunching up of durations: Perhaps this grouping is due to there being more people to “cleanup” low hanging fruit, the simple changes that are hanging around as there really aren’t enough people to take them on. Exposing things in a simple way with generated summaries and ETA for work in progress may help an organisation feel more confident in their software delivery: Next time I hope to dive more into this exciting world of language models, GPT-3 and explaining what is going on in your software delivery pipeline., if you want to see how CloudBees Engineering Efficiency unlocks engineering productivity data to give you the insights to keep your teams focused on delivering value quickly and predictably.
Source: CloudBees