Artificial intelligence (AI), based on the principles of machine learning, has long been a driving force in fiction. But with recent advances in computing power and the availability of big data, some of these fictitious ideas have become reality today.

AI and machine learning is one of the big trends, and it's no surprise that SaaS is a big part of this "big trend." A few weeks ago, Google CEO Sundar Pichai said at a Recode-sponsored event that the effects of artificial intelligence on humanity "could be deeper than, I don't know, the electricity or fire". In this article, I'll look at how SaaS companies can use AI/ML in the future.

Read More : How do I make a successful SaaS app?

The SaaS Marketplace Today:
A report by IDC shows that the SaaS segment, which accounts for 68.7% of the total cloud market share, was the slowest growing segment of the cloud market with a CAGR of 22.9% during the last year.

Venture capital funding, typically an indicator of how "hot" investors view a market, has declined for SaaS startups. TechCrunch attributed this largely to market saturation and the fact that newcomers looking for funding often try to compete with big, established players.

The SaaS market will still grow somewhat, but probably more slowly than before. SaaS markets are coming of age, and it looks like whoever wins has to be there for the next "big thing."

AI and machine learning can help SaaS create a more strategic position

All the big market players like Amazon, Google and Microsoft are announcing offers that integrate AI. Oracle, another big player in the SaaS market, said it was betting big on AI and machine learning to overtake the SaaS sales force. This is a strong indicator that AI and machine learning could be the next step in differentiating a SaaS and helping it gain market share.

How SaaS use AI and machine learning

SaaS is embracing the trend towards AI and machine learning, and investments in this area continue to increase. Below are some of the SaaS solutions where machine learning plays a strategic role.

1. Personalization
AI can bring the ability for hyper-personalization to SaaS, which we've seen in mobile apps in particular (see Starbucks' "My Starbucks Barista"). In SaaS, natural language processing and the ability of AI to learn from past user interactions can help design user interfaces in a way that works for people.

For example, if you are thinking of a SaaS without AI capability, adding more features or functionality will lead to UI clutter and increasing complexity for the user. AI can not only help with personalization, but also with easier adoption of functions.

2. Automation
Automation is demonstrated in SaaS with embedded AI in different ways. Take control where manual functions were previously needed, such as with chatbots that help users provide answers to basic questions.

Automation reduces costs by eliminating the need to hire additional staff to do more work. A bot answers login reset questions with an automated response in a knowledge base link, freeing up customer service reps to focus on more difficult questions.

One of the challenges for SaaS is maintaining an engaged customer base remotely. It can be difficult to keep up with customer service requests and ensure that every customer has a good experience. AI can help by reducing this distance and stepping in to complement human effort.

For example, there are already several use cases (Verizon, banking apps, etc.) where chatbots partially answer questions but refer users to human operators when needed.

3. Predictive analysis
There are many potential ways for AI embedded in SaaS to leverage predictive analytics to create better user experiences and/or help stop SaaS churn. For example, machine learning can help predict user preferences or behavior and then potentially trigger alerts or actions when it appears the user is logging out.

4. Product Search
When the user searches for a product, how do we find the best results for the user? One factor used in product ranking is user click through rate or product sale rates. Additionally, user behavior data provides the link between a request, a product page view, and the purchase event. The in-depth analysis of query log data allows us to create graphs between queries and products, and between different products.

We may also extract data to understand the intent of the user's query. When a user searches for "Toyota Prius", are they looking for a new car or just fixing auto parts? Query intent detection is based on the user's understanding of what other users are searching for and the semantics of the query terms.

5. Version management
The consequences of a SaaS program programmed and deployed too soon, to have a crash or bug that affects all users, can be very costly. Reputation issues and potential liability abound, but being able to resolve them quickly can be a key advantage. When operating in a crowded market, the difference between being ahead and behind can be whether you're first to people.

AI is a game-changer for SaaS developers because it can improve your own coding skills by providing the necessary checks to see if the coding is right. Deployment can be reduced from months to a very short time if the AI ​​can verify that the SaaS is designed for thousands of users.