The use of Artificial Intelligence (AI) in testing is still in its infancy. Organisations are keen to apply smart analytics for critical decision making and to optimise testing activities to achieve maximum quality with lower costs (in terms of both time and money). But are yet to find a complete answer. Some testing professionals are rightly sceptical of a future where all testing will be carried out by machines, but to ignore the potential for AI to supplement testing activities (rather than replacing it) would be foolish.
At P2 Consulting we’re specifically excited about the applications of AI for Predictive Analytics and my colleagues Richard Rikards and Adam Skinner have already written about its application in our Programme Management Office (PMO) practice .
Within our Testing & QA practice, I believe that Predictive Analytics is a must for organisations over the coming 2-3 years. Initially focusing on helping to make the key decisions in the testing process, such as which tests to run and how many tests should run for a release so that testing can be really targeted to maximise the use of limited resources without taking excessive risks to production quality.