One of the longtime advantages of SaaS is that the provider can monitor usage and performance of the application. This can identify low usage features, help the customer use these features, identify features that maybe need reworking or removal from the application and identify performance issues. These tools are often referred to as SaaS Analytics. One of the barriers to using SaaS analytics is of course the large amounts of data involved. AI is being used as a tool to help understand this data and improve the application and application adoption.

The Gartner Magic Quadrant for AI Code Assistants is a new Magic Quadrant report this year which analyzes and rates the tools for using AI to generate code. The report is available free from Github, one of the leaders. Amazon Web Services, Google Cloud and GitLab are also all leaders. If you aren't familiar with these tools one of the most useful things about this report is the list of required features, a great way to learn about these kinds of tools. If you are looking to improve developer productivity this will provide you with some ideas.

Gartner has also released the new versions of the AI Developer Services Magic Quadrant and the Data Science and Machine Learning Platforms Magic Quadrant. Although related and with some of the same vendors as leaders, the focus is somewhat different. AI developer services are the building blocks for actually developing AI applications and are typically used by developers. Whereas the Data Science and Machine Learning Platforms are more predeveloped tools and services that can be used on your own data by a variety of data scientists.