How artificial intelligence and machine learning changed the SaaS industry

YairGreen, CTO at GlobalDots, explains how artificial intelligence and machine learning changed the Software-as-a-Service industry

There is no single moment when SaaS (Software-as-a-Service) arrived, because SaaS is a concept that involves numerous components. SaaS has evolved to the point where vendors and suppliers manage their own software and no installation is required because software is distributed instantaneously over the internet (via the cloud). Cloud computing allows businesses to consume computing resources over the internet as a utility — in much the same way they consume water or electricity. The SaaS-based cloud model now offers businesses significant efficiencies and cost savings, although different sectors have moved towards SaaS models at different speeds. In technical terms, SaaS relies on cloud delivery at scale, a minimum degree of widely available connectivity and enterprise-grade security.

And SaaS doesn’t sit still. As part of this continuous evolution, both artificial intelligence and machine learning are playing their respective roles as they become an integral part of the SaaS landscape.

Historically, you distributed software to the consumers and customers, but you didn’t get the insight regarding how they leverage your software — which features are in use and those that are not. When providing your Software-as-a-Service, you also have lots of data and insights that can help you improve your service. You can therefore use this information to provide insight to your customers, you are better able to understand usage patterns and, ultimately, be able to use this data to give intelligent feedback. The SaaS era coincides with the almost ubiquitous concept of big data. And SaaS, which is able to leverage AI and ML techniques, has a distinct advantage over the software provision days of old — the software provider now has access to aggregated data from different customers, which he can leverage to build a better service.

Companies now hold significant volumes of data from customers all in one place. Artificial intelligence and machine learning enables a more automated means of mass data processing. Gartner defines big data by not only volume, but also by its variety and velocity.

Variety refers to the different media we use to represent data (beyond simple figures) and velocity is determined by the speed at which data is collected, analysed and implemented. The ultimate reality is that IT teams are dealing with increasing amounts of data and a variety of tools to monitor that data – which can mean significant delays in identifying and solving issues. And with the whole area of IT operations being challenged by this rapid data growth (that must be captured, analysed and acted on), many businesses are turning to AI solutions to help prevent, identify and resolve any potential outages more quickly.

Marketing is particularly well placed to leverage AI and ML techniques. The data SaaS companies collect need to be relevant and recent. The more up to date the data is the more efficiently it can be implemented.