In the last few years more and more Saas companies have been created all with the same goal of providing value and disrupting existing markets.
The role of artificial intelligence is to automate tasks commonly done by humans and to provide new tools and services that either reduce costs or generate more revenue.
What is AI SaaS?
An Artificial Intelligence Saas company is one that provides tools and/or SaaS services to businesses while using artificial intelligence in their processes.
That simply means that they use technologies such as machine learning and deep learning in their products to automate tasks, discover insights from data or create new revenue sources.
All these technologies are based on patterns recognition and their capabilities to automatically learn from data without requiring the need of explicit programming.
Are AI Saas companies entirely new?
AI SaaS isn’t really something new.
Every Saas company can claim to be using AI even if it’s just a part of one of their product features.
That’s because these technologies are general enough to allow them to be applied to almost any domain.
However, what is new is the fact that more and more companies are creating new products and services that use AI to either lower costs or generate new revenue streans.
The reason why it has received staggering growth is simply due to the amount of data we’re producing.
Another factor that has made these technologies more accessible is the fact that their costs have dropped significantly in the last few years.
Does SaaS use AI?
Well, in most cases yes. Pretty much any Saas company uses AI to automate tasks, discover insights from data or to provide new services.
For example, imagine a SaaS company that helps people run their business by managing tasks, contacts and communications.
Now imagine that they have an algorithm that automatically suggests the perfect times to send emails or calls to your contacts based on their emails and calls history.
In other words, it analyzes data from other users that have similar characteristics as the ones of your contacts to determine the perfect time to send them messages.
This is just one example of how AI can be used in a Saas company. I’m sure you can think of many more!
What’s the next step for these companies?
AI SaaS is still a very young market and as it matures we’ll start to see more and more services and tools designed by these companies.
The goal is simple: making AI accessible to any business regardless of their size.
The main issue right now is that many companies are still experimenting with AI and trying to understand how it can be applied to their business case.
In other words, they’re still at the “start from scratch” phase.
This means that their products are not yet being used by a large number of customers and as a result, it’s very difficult for them to show that they have the necessary data to automate certain tasks.
What Saa companies in the financial sector?
Financial companies have been using predictive analytics and machine learning to make predictions about the future.
However, there’s still a big opportunity in applying these technologies to specific use cases such as fraud detection or recommendation engines.
For example, imagine an AI Saas company that provides a platform to predict credit risk and classify companies depending on their level of risk.
Another example could be a company that analyzes data from clients to recommend financial products based on their previous purchases.
Are AI Saas companies trustworthy?
The main idea behind these technologies is that they’re not limited to any specific domain.
As a result, the same tools that are used to detect “fake news” can also be used for other purposes such as detecting credit risk.
This is a good thing because it’s part of the democratization of AI.
However, there’s a catch: this means that it can be used in both good and bad purposes.
Though remember that AI itself is not prone to making biased decisions 90% of the time.
It’s the data that it works with which can be biased.
For example, an AI that analyzes news and tries to identify the language in which a certain article is written might be tricked by an article written in french but not about an event that occurred in China.
The reason? It wasn’t taught to identify articles written in French about Chinese events.
What does it take to build a profitable AI Saas company?
Three main factors:
1. Scale the product so that it can be used by a lot of people
2. Maintain a strong commitment to research and development
3. Find a customer acquisition channel that’s scalable and repeatable
For example, a company that provides a marketplace to connect companies with AI specialists is most likely to initially focus on one specific industry.
Doing this will make it easier to show the value of their product and grow.
As the company starts to grow, they can expand into other domains and later on to new ones.
This requires a certain amount of commitment and focus because it could be difficult to expand into new markets and maintain a good operational structure.
As an AI Saas company, you need to turn a profit.
The problem is that you need to have a lot of clients to be able to afford the costs associated with research and development.
So how do you scale a Saas R&D?
The ideal scenario would be to find a middle ground where you can provide value and make money.
Imagine if you could provide a product that’s good enough for 50% of companies and you only have to do half the research.
The other 50% will be using it for free but they’ll be providing valuable feedback to help you improve your product.
This will allow you to make progress faster while generating revenue.
To wrap up:
So here are the main factors to consider when building an AI Saas company.
Finding a scalable customer acquisition model, finding a profitable market and focusing on R&D are all important factors to consider.
Predictive technologies have a lot of potential but there’s a catch: they can be used for good and for bad.
The democratization of these technologies means that companies will have to find new business models to stay profitable while providing value.