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AI in SaaS: How to Create SaaS Product + Top Ideas

Discover the best ways to use AI in SaaS products and how to avoid risks associated with this technology.

July 4 | Updated on July 8 | 10 min
Alex Padalka

Alex Padalka

CEO at JetBase

Artificial intelligence has been making waves across various industries, and Software-as-a-Service (SaaS) is probably at the forefront. AI in SaaS is gaining steam as a powerful tool that enhances or even redefines usual solutions. Instead of vying for interest in a highly competitive market, you can get ahead by using this powerful technology.

Now, we understand that AI is currently a hyped-up technology that seems to be recommended for just about everything. But we’d argue SaaS is the perfect use case for it. Whether it’s automation or personalization, AI-powered features enhance software. We’ll explain just how to achieve that below.

We will also talk about the potential of AI use in SaaS, showcase some good product ideas, and lead you through the building steps. Then, the guide will balance things out with an in-depth look at the risks that such projects tend to face, as well as ways to mitigate them. This should cover all the standard questions and apprehensions about AI.

Ultimately, we hope you will see a clear picture of AI’s importance and usability in SaaS. For now, let’s start with the core question. 

Why is Investing in AI Systems a Good Idea for SaaS?

It’s no secret that AI is the next big thing, with 92% of product managers believing it will have a long-term impact. Clearly, people are embracing this technology nowadays, which is great. But it’s equally important to understand just what it brings to the table.

In our mind, there’s no doubt that AI SaaS solutions are the future. Even now, 47% of SaaS companies report discovering new revenue streams thanks to AI. Couple that with forecasts about customer interactions slowly becoming fully AI-based, and you can see the market’s direction. 

Why is Investing in AI Systems a Good Idea for SaaS.webp

Investing in AI SaaS now would mean being ahead of the pack. While others adopt AI and use it to improve or redesign their corporate structure, you can start offering AI-powered solutions. Getting on the trend early can help capture a large chunk of the market, as well as establish yourself as a forward-thinking company.

Besides, you can shape AI however you see fit, adapting it to your needs and vision. While the technology is still young, your project can become the one that shapes its future use. For that, of course, you need to launch an AI in SaaS product that’s influential enough. Doing so requires a good idea and realization. We can help with both of those, and we’ll begin by giving you some inspiration.

Top Five AI in SaaS Ideas

While we’ve been talking about AI in SaaS projects in general, it has plenty of uses. AI can help with data analysis, personalization in marketing, task automation, and many more services. The question is only what appeals to you most and what your staff can tackle reliably.

In this section, we’ll present some of the most exciting applications for AI, showing how they transform traditional features. However, the ultimate choice is up to you. In fact, we hope these will simply inspire you to come up with a fresh take on AI SaaS solutions. JetBase is always on the lookout for innovative ideas and we will gladly help you create something revolutionary.

For now, though, here are the best ideas for AI-powered projects to inspire you.

Top Five AI in SaaS Ideas.webp

Customer Relationship Management (CRM) with AI Automation and Analysis

An AI SaaS solution can process customer feedback, respond to queries, and compile data to assess your success. Moreover, it will analyze how a business can raise customer satisfaction and provide actionable advice to implement better policies. As a result, companies will grow their sales and establish a returning customer base, all off the back of your solution. 

Project Management Solutions with AI Course Correction

AI can do a cold estimate of a team’s progress, assess a reasonable deadline, calculate the optimal task distribution, and point out the weakest links. This way, a project manager can base their decisions on unbiased data. Besides, you can add AI-based automation for time logging and sprint reports. It will cut down on bureaucratic tasks, letting teams focus on the work at hand.

HR Software with AI Analysis

Using AI SaaS to estimate an employee’s efficiency, output, and growth can be an immense time-saver for big corporations. It can also be “taught” to handle the accounting side of things, processing invoices and payouts every month. What you’ll be selling, first and foremost, is the streamlining of an HR team’s duties, which opens them up to get more in-depth with employees who need it.

Better Cybersecurity Through AI

One of the core aspects of securing an enterprise ecosystem is external testing. Regularly hiring firms specializing in this is expensive, which is why an AI SaaS can be a lifesaver here. Companies can use your solution to test their own systems, find weak points, and get data-based analysis on ways to improve them.

Similarly, AI can work as a smart identity verification tool. It will provide access on a flexible basis, guaranteeing that data is only available to the appropriate roles. The algorithm can even be set up to assign roles itself, as well as customize permissions based on an employee’s projects.

Marketing Solutions with Auto-Generated Content and Personalization

Any brand that wants to stay relevant has to invest a lot into marketing, time- and finance-wise. However, you can offer them an AI SaaS alternative: using an algorithmic approach to content generation and outreach. With enough data, a company will be able to launch marketing campaigns with just a few clicks, tailoring them to a specific audience.

Six Steps to Build an AI SaaS Product

Now, let’s discuss how you’ll turn these ideas into reality. The process of creating an AI SaaS solution isn’t that complicated; you just have to take it step by step.

Six steps to build an AI SaaS product

Step 1. Analyze the Market and Lay the Groundwork

Coming up with the right idea for an AI SaaS project is just the start. It’s even more important to make sure that the product will land well in a market that’s already quite saturated. Plus, you need to assess the outsourcing teams available to you and determine their skills. It’s crucial to have a team that can match your ambitions and, perhaps, help with the market analysis too.

Step 2. Design the Product and Establish the Infrastructure

The early stages of development give you a chance to cement the look and feel of your product, creating a unique visual identity. It’s also the time when you are to choose your cloud provider. You might be thinking of handling the infrastructure yourself. Yet that is a risk—and we’ll explore why below. Generally, it’s recommended to use an established platform like Azure or AWS for AI SaaS.

Step 3. Implement AI and Iterate It

Picking your AI model will be the project's star step and linchpin. Make sure you’ve consulted the devs extensively to pick out the optimal model. Then comes the technical part, where your team will train the model and run different iterations, refining it. In the end, it will be completely unique, tailored to your AI SaaS product.

Training the model takes a lot of data, as well as time, but it’s an essential process that determines what AI will be able to do for you. A lack of training means analysis and forecasts won’t be as precise, and the algorithm’s communication ability will suffer. Investing heavily into making your AI “smarter” will always pay off.

Step 4. Run Tests and Gather Feedback

It’s vital that your product ships without major bugs or errors, but that’s not all that you need to look out for. As the SaaS model requires a long-term commitment from customers to work, you need to incorporate their feedback into the solution. Whether it’s design tweaks or some last-minute updates to the AI model, your AI SaaS software can and should undergo some changes for the better.

Step 5. Market the Solution and Launch It

No matter how unique your product is, it won’t make a big splash without proper marketing. Emphasize your selling points, especially the AI-powered features, and show why you’re ahead of whatever else the market can offer. Ideally, your launch day should be a major event with plenty of excitement and pre-sales. 

Step 6. Assess Sales and Conduct Maintenance

The data on your project’s success reflects what you did right and how to proceed because the work doesn’t stop there. Regular updates and maintenance will guarantee you stay competitive and that your AI SaaS product won’t feel outdated as the technology evolves. The post-launch period is easiest to handle with the same team that ran the actual development.

Common Risks While Developing AI SaaS Products

Even though the actual steps of the development process are straightforward, there are still some challenges to tackle. As you work on your AI SaaS solution, keep these in mind and navigate them with our tips. You shouldn’t have any major issues, as preparation helps mitigate most risks.

Common Risks While Developing AI SaaS Products.webp

Hiring a Team with Not Enough Experience

Even though AI in SaaS is treated as a hot new trend, the technology has been evolving for quite a while. This means that you should be able to find truly experienced developers if you look in the right place. You want a team that’s worked with AI and machine learning and projects of various scopes. Variety of experience is just as important as longevity in the market, in fact.

Since AI is constantly changing and getting more complex, you should be on the lookout for teams that work with it regularly. Merging AI with other fresh technologies shows both tech prowess and an ability to adapt to new standards.

Most importantly, verify any team’s lofty promises and claims of experience. An extensive portfolio and favorable reviews are the only way to confirm you’re working with true experts. Don’t be hesitant to ask in-depth questions about your own project, too, especially technical ones. Before you begin the work, you should know how the team plans to implement AI in SaaS solutions.

Trying to Create Everything from Scratch

Even if you have assembled the best team possible, don’t do more than necessary. There are plenty of machine learning and AI frameworks and libraries to be your foundation. This will not only speed up development and cut down on the amount of work. Such libraries also lower the chance of errors and directly diminish risks during the project.

Choosing existing frameworks for AI SaaS products doesn’t mean you will have less flexibility or lose the ability to tailor them to your own needs. It simply creates a base layer, which your team can then configure and build upon. This doesn’t mean you can’t create your own implementations and protocols. Just do that work to supplement existing solutions, saving time and money.

Building Your Own Infrastructure

It’s not uncommon for companies to weigh their options and think about whether a third-party cloud provider is actually the right choice. This is usually a result of wanting to retain full control over the product and data. However, the risk is not worth the benefits unless you have a big department of engineers to build, maintain, and improve infrastructure.

Cloud providers for AI SaaS solutions are juggernauts in the industry. With them, you’re guaranteed stability, extensive security practices, and data safety. Sure, you don’t have as much flexibility in building the environment. But the trade-off is high-quality infrastructure that a smaller company simply will not match.

Focusing Too Hard on AI

Don’t get us wrong, AI absolutely can be the selling point of an entire product. But you want people who pay for it to discover that you have much more to offer. Centering all of your development around AI and not making other, more diverse features is a huge mistake. Prepare an array of functions that can keep customers coming back for more.

Don’t build the whole solution around AI. Instead, consider how AI will enhance the software and which areas you can apply it to. Treat it as a way to upgrade a product that was already excellent, not as the foundation. This way, you get more flexibility and longevity for your product.

Need to Build an AI Saas Product?

Whether you want to reinforce your team with dedicated developers or outsource the entire project, JetBase is here to help. We have more than a decade of experience in software development. Our team always keeps a hand on the pulse of technology, and AI is one of our favorite new things to work with.

For example, we have experience with SaaS projects like Grapevine, an award-winning collaboration platform. Using AssemblyAI, this team software supports asynchronous audio- and video-chatting, as well as transcription. The latter is used both for the sake of convenience and inclusivity, letting any team member participate in the conversation.

Another AI SaaS project of ours covers the healthcare industry. This web solution uses predictive algorithms to assess a patient’s health and forecast its changes. It flexibly adapts to each new case and bases its predictions on hard data. This is a clear example of AI being an indispensable tool for this specific type of solution.

As you can see, we have quite varied cases in our portfolio. But JetBase never stops learning and seeking new skills. That is what makes us the perfect team to handle your AI SaaS development, from conception to post-launch maintenance. Get in touch today to receive a consultation and start our collaboration.

FAQ

  • How difficult is it to implement AI in SaaS?

  • How to decide which idea for a SaaS product is the best one?

  • Should I worry about my SaaS solution’s security?

  • What’s the approximate price of an AI SaaS project?

Tags:
  • SaaS

Table of Contents

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