Evolution of engineering productivity with AI & ML
Customer obsession renaissance is afoot. In today’s highly competitive era, customer satisfaction drives business. Dealing with fickle customer mindsets and habits requires speed, agility, the ability to maneuver in motion to a change in strategy while still delivering efficiently. Hence, the focus on engineering productivity – a critical aspect of agility – is greater than ever before. However, traditional measures of engineering productivity have always lacked measures of developer engagement or experience. High performers such as Google, Amazon, Facebook, Netflix and Etsy routinely and reliably deploy code into production hundreds or even thousands of times per day. How can such “Delivery Velocity” be achieved by others? One step is to identify patterns of commonalities in customized development processes. Another is to leverage machine-learned gaps in developer practices that can crystallize into patterns where AI and Machine-Learning (ML) solutions can augment engineering productivity.