AI is experiencing a massive boom in popularity. 2024 has clearly shown that this technology is here to stay. Covering a variety of industries, AI-powered SaaS is, perhaps, one of the most promising directions for artificial intelligence to take. It provides extra functionality and potential to an already thriving niche, enhancing usability and making SaaS platforms more attractive.
In today’s guide, we’ll use JetBase’s experience to illustrate what makes AI and SaaS a match made in heaven and discuss the best way to introduce AI into your solutions. We’ll talk about the development steps for an AI SaaS platform and ways to manage its cost. By the end, you should have a firm grasp of the process and feel ready to launch your own project.
So, without further ado, let’s discuss mixing AI and SaaS with great results.
Why AI and SaaS Together Are a Powerful Solution
It’s no secret that the interest in AI spiked in 2024, and some people may be wary of hopping on this trend. However, the pairing of AI and SaaS is more than just a fad, and this section will prove that conclusively. Here are just some of the reasons why AI-powered SaaS works well.
Automation in SaaS with AI
Automating is a major draw in enterprise software, as it cuts expenses, streamlines operations, and speeds up internal processes. By freeing up employees to focus on more complex tasks, automation directly affects a business from top to bottom. And it’s no secret that AI and automation go together perfectly.
By implementing AI-powered features in your SaaS solution, you open it to learn your workflow and augment human input with automated protocols and algorithms. That way, it can handle scheduling, running analytics, or marketing efforts.
AI-Driven Personalization Features
For a bit of social impact, a SaaS AI platform can reach people more effectively by discovering and adhering to their preferences. It improves marketing and customer relations, as well as makes social media and personalized apps more immersive.
This can also greatly impact advertisement revenue, as AI can help determine the optimal way to run campaigns. As a result, they bring in more with less spending, reaching new audiences and growing user bases.
Predictive Analytics for Better Decision-Making
AI is changing the landscape of analytics and data analysis with deeper insights and better forecasting. This leads to well-informed business decisions, which in turn leads directly to growth and more ambitious moves. As the AI SaaS platform reduces risk, it encourages bold choices by guaranteeing they will pay off.
Enhancing Security with AI Technologies
AI can analyze many possible attack vectors and simulate theoretical situations to test an enterprise’s security. This helps find gaps and patch them up before they become an actual issue. Running these tests protects companies from data leaks and internal breaches that could seriously disrupt their operations.
Key Approaches to Integrating AI into SaaS Products
Now that you know why AI-powered SaaS works, let’s start discussing ways to combine the two. We’ll start off by talking about the AI side of things, which has two core approaches. Each has its strengths, so this section will give an overview to help you make the choice.
Pre-built AI Solutions: Quick and Efficient Options
A pre-built AI may be the way to go if you’re looking to hit the ground running. There are plenty of options on the market that provide powerful features and high quality. Plus, these may require a lower initial investment compared to a custom-built solution.
The drawback is that you might have issues integrating them with your platform, and they could lack the exact features you seek. With pre-builts, you always have less control over the solution and may need to work around certain restrictions.
Custom AI Development: Building from the Ground Up
On the flip side of pre-builts, we have the approach of creating an AI SaaS platform. This gives you tons of flexibility and the option to customize the solution over time. Thanks to that, you will have a system that fits your products perfectly and keeps up with your growth.
It is, however, slower to start and more expensive as you’ll have to develop it from scratch. Besides, it will likely require hiring outside help unless you have an in-house AI department. Thankfully, you already know who to turn to for that, and JetBase is happy to help.
A Step-by-Step Approach to Creating an AI-Driven SaaS Platform
So, how do you actually structure development to create an AI-powered SaaS solution? We’ll break down the process into stages to illustrate their importance.
1. Defining Your Vision: Planning and Ideation
Take your time to lay out your ideas for the product, listing features and ranking them to establish the essentials and the “good-to-haves.” The aim is to understand what the final solution will be like, the likely cost, and what you’ll be competing with. By gaining a full understanding of your own product, you can determine whether it’s worth it in the current market.
2. The Discovery Phase: Researching and Understanding Needs
Research is key for any project, especially one as technologically complex as a SaaS solution with AI. Once you’ve defined the scope, look at competing solutions, assess the current trends, and determine what you need to succeed with your AI SaaS platform.
3. Selecting the Right Tech Stack for Your AI SaaS Solution
Picking the optimal stack can be crucial as it determines how optimized your platform will be, what it can achieve in the future, and how long development will take. Consult professionals on the best tools for the job, meaning ones that deliver quality but won’t balloon the cost. That way, you can guarantee that your AI solution will rival the best ones out there.
4. Assembling a Skilled Development Team
The success of your product depends on the people making it, so it’s paramount that you pick the right team for the job. Whether choosing people in-house or outsourcing the work, verify that their experience matches your plans and that the team has enough workforce to meet deadlines. Management is equally important, as it will control the process and ensure it runs smoothly.
5. Developing a Minimum Viable Product (MVP)
One of the reasons why we stressed the value of ranking features is that creating an MVP is an essential step in building a full-fledged AI-powered SaaS solution. It gives you an idea of how well the final product will work and provides a chance to adjust the performance and feature set.
6. Scaling Your AI SaaS Product for Growth
AI can learn and improve as time passes, so keep the team available for further development. Post-launch updates will help refine the algorithm, add new functions, and handle maintenance to boost performance.
| Step | Value |
|---|---|
| Defining vision | A clear scope for the project |
| Researching the market | An understanding of valuable trends |
| Selecting the tech stack | Optimized solution performance |
| Assembling a team | Smooth development process |
| Developing MVP | Vision for the future |
| Scaling | Continued growth and relevance |
Understanding the Costs of Developing an AI-Powered SaaS Product
Money may not be everything, but any company knows that sinking too much into development is impractical. So, let’s break down the expenses associated with making an AI SaaS platform and what kind of price level you can expect.
| Aspect | Cost |
|---|---|
| Market research | $8000 |
| Design | $15000 |
| QA/Testing | $18000 |
| Algorithm training | $25000 |
| Development | $45000 |
| Post-launch updates and maintenance | $15000 |
As you can see, the cost isn’t the same for each aspect. While the actual development process is obviously pricey, it’s also the most time-consuming and work-intensive part of creating the platform.
Similarly, training the algorithm is another major cost, which is why we previously mentioned using a pre-built solution. However, remember that this initial investment will pay off later with a more effective AI SaaS platform and the possibility of marketing your own solution.
Factors Affecting the Cost of AI SaaS Product Development
We saw the approximate cost of creating an AI SaaS solution, so let’s discuss some of the factors that shape it. These are all integral parts of the development process and can sometimes be altered to reduce the final cost and make it more palatable.
The Role of Features and Product Complexity
The more intricate your AI-powered SaaS solution is, the more you’ll have to invest in it, as each additional feature increases the cost. Therefore, it’s important to balance rich functionality with a manageable budget.
How Platform Selection Impacts Costs
Deploying an AI SaaS platform on various platforms like mobile or desktop requires different skill sets and builds. As a result, if you’re targeting several at once, your expenses will rise. Between mobile and desktop, though, the former tends to be less costly to work with.
The Importance of Technological Architecture Choices
Quality architecture is essential and your choices about it will impact both the solution’s performance and cost. The cloud you choose, the microservices structure, and your data storage options will all factor into this. So, as you pick the tech stack, brief your team on what your priorities are for the solution and get their opinion on how to construct the SaaS platform right.
UI/UX Design and Its Cost Impact
Finding the right design for a complex SaaS product can be tricky, requiring many iterations to perfect it. This leads to higher expenses as you spend more time and effort refining your UI/UX. In addition to establishing a vision early on, testing the design with the target audience can make the process more efficient. They can give invaluable feedback to speed it up.
The Industry of Choice
An AI SaaS platform can be useful for just about any field, and depending on its intended use, the cost changes based on the industry. For example, healthcare solutions tend to be rather pricey, as they have to account for regulations and privacy laws, requiring extra work. Similarly, fintech solutions are traditionally quite complex, so if your SaaS is targeting the finance sector - prepare to spend a lot.
The Influence of Development Team Location on Costs
It’s no secret that the median salary differs greatly by region with North America and Western Europe having the highest ones. However, don’t be tempted by the allure of the lowest possible rates, as those often come with the downside of inexperienced teams. Instead, opt for a middle ground, finding experienced developers in regions like Eastern Europe.
Maintenance and Updates: Ongoing Investment
Work on your AI SaaS platform isn’t done once initial development ends. To ensure its longevity and usefulness for years to come, it’s important to push post-release updates. These can include bug fixes and new features using new advances in AI. Plus, maintenance that ensures stability is just as important.
Successful AI SaaS Platform Examples to Inspire Your Project
In order to highlight just how transformative AI can be, let’s take a look at some real-world examples of it. These are a couple of companies that gained all-new benefits by integrating artificial intelligence into their business.
Facebook: Revolutionizing Social Media with AI
One could think that Facebook was already as successful as it could be, making billions of dollars a year. However, the company started relying on AI quite a while ago, and the generative AI boom has put that in the spotlight.
For example, Facebook reported using AI to analyze and proactively address hate speech and other inappropriate content on the platform. These tools have helped the company improve content moderation efforts and gather insights into user behavior. AI also has contributed to identifying abusive behavior and enforcing community standards.
Salesforce: AI in CRM Solutions
Another giant relying on SaaS artificial intelligence, Salesforce, focuses on AI as a helper. The company uses AI to help businesses engage better with their customers by personalizing interactions, automating everyday tasks, and delivering detailed insights on user engagement. The company packages all of that into its offerings, bringing classic software into the modern era with the help of AI.
Common Challenges in AI SaaS Platform Development
Just like any other type of development, creating an AI SaaS platform can come with some roadblocks. However, none of them are insurmountable, so let’s talk about navigating them.
Ensuring Data Privacy and Security
While AI itself should help boost your ecosystem’s security, it’s important to understand that AI works with tons and tons of data, some of it private. That means you need to secure the solution itself through methods such as encryption and authorization management. Without them, your AI-powered SaaS product could introduce vulnerabilities.
Overcoming Scalability Issues in AI SaaS Platforms
Using a suboptimal tech stack increases the risk of bottlenecks when your AI solution becomes overloaded. After all, AI is rather power-intensive, and the more ambitious your features, the more optimization is required. Consult your team to ensure the planned uses match the product’s internal architecture.
Navigating Integration Complexities
If you opt to use a pre-built AI-powered SaaS or already have a complex ecosystem for your business, it may take some time to iron out integration issues. Connecting data streams and ensuring proper access can be tricky within sprawling systems. However, experienced developers with integration-specific expertise will solve the problem.
Managing Ethical Concerns in AI Development
To address ethical concerns, use data sourced from verifiable sources with user consent, ensure transparency in AI interactions, and actively mitigate biases in AI models. It’s also important to disclose your own use of AI in your solution so that any user interactions with it are conscious and informed.
The Future of AI SaaS Development: What’s on the Horizon?
Though even a modern SaaS AI platform can be a game-changer for a business, these solutions still have room to grow. This leaves the future of AI in SaaS looking quite exciting. Here are some of the trends we expect to make a splash.
Firstly, expect significant improvements in personalization as natural language processing and machine learning develop new methods and approaches. This can enhance user-facing experiences and make AI-powered SaaS a great choice for CRM systems.
Next, the market is likely to see an increase in pre-built options, along with efforts to simplify integration into company ecosystems. As a result, companies without development resources will rely extensively on the market’s offerings. This will give an advantage to those running their own unique solutions with more analytical power and flexibility, but the growing sophistication of pre-built options could narrow this gap.
Why Choose JetBase for AI SaaS Product Development?
Making an AI SaaS platform from scratch is no easy feat, but the payoff is worth it. It will deliver better customer relations, stronger security, and cutting-edge automation. To reap all of those benefits, though, you need to have a firm grasp of the process. JetBase’s helpful guide gave you the know-how to do it, but another approach eliminates all development risks.
By partnering with JetBase, you get a team of veteran developers who have been creating custom solutions for more than a decade, covering different industries and technologies. We always prioritize producing a polished product that surpasses the client’s expectations. If you want to ensure that your AI-powered SaaS solution beats the competition, you know who to turn to.
Get in touch today to start off with a free consultation.
