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Take the following steps to build your AI SaaS product:
1. Prevent disruptions to your existing SaaS business
In order to stay ahead, you will need to add new capabilities to your SaaS product by introducing AI and ML. The primary question do you even know whether you’re adding value to your product?
Well, the name of the game is to constantly be ensuring the best possible customer experience and retention. To do this, you need to ensure your product delivers the maximum possible value and a great all-around user experience.
Companies are already leveraging AI technologies by using them to automate traditional human intervention customer services such as call centers with chatbots that can help with queries instantly.
The addition of this single functionality has greatly improved customer satisfaction as well as reducing company operating costs, making it a win-win technology.
So where do you start when it comes to implementation?
You can try to find and adopt an existing open-source product or build your own from scratch. Whichever route you choose, you will need to undertake the following steps.
You need to launch a “Minimum Viable Product” first, subsequently, you can assess the market feedback. Depending on the feedback, you can enhance the SaaS product.
How would you create an AI/ML-powered MVP without disrupting your existing SaaS business? Do the following:
- Ensure that you have sufficient knowledgeable people in your team to run your existing SaaS business effectively.
- Avoid any adverse impact on existing IT infrastructure and computational resources so that your current SaaS product functions effectively.
- Onboard new people with the required skills and competencies to develop your AI/ML-powered MVP.
- Plan for adequate infrastructure and computational resources for the MVP.
- Plan to secure your AI/ML MVP so that you can avoid any information security incidents. This is important to protect the reputation of your existing SaaS business.
Wondering how you can prevent disruptions to your existing SaaS business? Check out our guide “How to build an enterprise MVP without disrupting your core business”.
2. Decide on the AI/ML-powered features to offer in your SaaS product
Now that you are incorporating AI and ML, which new features should you offer in your SaaS product? To analyze this, first onboard a competent project manager (PM), an experienced software architect, and a team of competent business analysts (BAs).
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Conduct a sufficient number of brainstorming sessions with your business stakeholders to identify which features to offer. Take the following steps:
- Analyze how each feature will address the specific pain points of your customers. Document the impact of each feature using tools like the “pain and gain map”.
- You also need to prioritize the features for your MVP. Use tools like the “prioritization matrix” to do this.
Do you need help deciding on the features and prioritize them for your MVP? Read our guide on creating an MVP for more information.
3. Project planning for adding AI and machine learning to your SaaS product
Adding AI and ML to your SaaS product is an involved software development project, and you need to plan meticulously to achieve success. How do you plan such a project?
Take the following steps:
- Determine where you will introduce AI-powered “intelligent automation”. Also determine which AI capabilities you will use, e.g., “Natural Language Processing” (NLP), image recognition, etc. You can read our guide to AI development lifecycle for more information.
- Identify datasets to “train” your proposed AI/ML modules. Keep in mind that the data quality and volume of your historical data influence the functioning of AI/ML systems.
- Plan to use cloud computing so that you don’t spend too much time on infrastructure management. Considering that you are transforming your SaaS product with the help of AI/ML, using cloud fits in with your long-term strategy too.
- Analyze the steps you should take to secure your AI/ML-powered SaaS app.
- Choose the technology stack you should use. This could involve using an AI development platform, alternatively, you could choose to develop your AI/ML modules entirely from scratch. Since you already have a SaaS product, your choice of technology stack should align with your overall technology strategy.
- Plan to onboard a competent development team.
- Keep the SaaS UI design best practices in mind when introducing AI and ML.
- Decide on your “verification & validation” actions, which would involve reviews and testing.
Need help with this planning exercise? You can read our guide to best app development approaches for more information.
4. Estimate your project to add AI and ML to your SaaS product
How do you get all the necessary organizational approvals to execute your software development project? An important step is to estimate its cost, therefore, we will now see how you can do that.
You need to take the following steps:
- Estimate the cost of using a cloud computing platform;
- Assess the costs of AI and ML development tools;
- Calculate the estimated costs for development manpower;
- Estimate other costs for hiring and administrative activities.
If you need help with this exercise, then you can read our guide “How much does it Cost to develop an AI solution for your company?”.
5. Find a cloud platform for development
How do you take care of your infrastructure requirements while developing AI and ML modules for your SaaS product? As a SaaS company, you are probably using cloud computing already.
You need to align with your cloud strategy. Since SaaS products are web apps, you are probably using a “Platform-as-a-Service” (PaaS) platform already. I recommend that you use it to develop the AI and ML modules too.
PaaS platforms like Amazon’s AWS Elastic Beanstalk offer plenty of advantages, e.g.:
- They manage the cloud infrastructure, networking, databases, operating system, middleware, and runtime environment. As a result, you can concentrate on development.
- You will find it easy to integrate APIs if you use a PaaS platform.
- Reputed PaaS platforms offer excellent DevOps tools and robust auto-scaling solutions. Read our guide to estimating AI development for help with this exercise.
6. Decide on the technology stack for your AI and ML project
Which technologies should you use in this project to add AI and ML to your SaaS product? Consider the following aspects when you make the decision:
- You need to align with the technology stack used in the existing SaaS product. E.g., if you have used Node.js to code the web app, you should stick to it.
- You can use AI development tools to create AI and ML modules since this approach can expedite your project.
Read how we can help you with code review.
“Microsoft Azure AI Platform” and “Google Cloud AI Platform” are a few examples of such tools, and you can read about them in our guide to AI software development tools.
- Alternatively, you might decide to code your AI and ML programs from scratch. If you choose to do so, then you should use a powerful programming language like Python. It has excellent libraries that make coding AI and ML programs easier. Read “Julia vs Python: can this new programming language unseat the king?” to learn more about it.
- You would likely integrate your AI and ML modules with your existing SaaS product front-end using APIs. I recommend you to develop RESTful APIs since “REST” (Representational State Transfer) is the de-facto standard for API development.
7. Onboard a competent development team
You can’t underestimate the importance of a competent development team. Such a team needs the following roles:
- UI designers;
- AI/ML developers;
- Web developers;
- DevOps engineers.
You need team members with excellent technical skills, including those in data science, however, they should also have industry knowledge and a good level of experience.
While you will surely test your AI and ML algorithms, APIs, etc. thoroughly, testing can’t detect all errors. You need to implement a thorough review process, which should cover all of the following:
- Business requirements;
- Technical design;
- Test plan and test cases;
- UI design;
How do you find experienced reviewers? Once again, we at DevTeam.Space can help! Read our guide “Why choosing DevTeam.Space to review your code can ensure your software product is a success” for insights.
8. Secure your SaaS product while you introduce AI and ML
You might have secured your SaaS product when you launched it. Now that you are adding new features or capabilities with the help of AI and ML, you need to do that again.
How can you secure your app? You might need to focus on the following:
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- Mitigating key application security vulnerabilities like injection, XML external entities (XXE), cross-site scripting (XSS), broken authentication, etc.;
- Using tools and techniques like multi-factor authentication (MFA), encryption, next-generation firewall, antivirus solution, and real-time threat intelligence capabilities;
- Incorporating security and compliance testing in CI/CD testing instead of treating them as low-priority tasks;
- Securing your APIs.
Need help securing your SaaS app? Our guide to securing a finance app can provide insights.
9. Keep the important SaaS UI design principles in mind
You must have designed your SaaS product UI by following appropriate guidelines, however, now you are enhancing it. Ensure that you continue to follow the SaaS design best practices.
I recommend the following:
- Offer user-friendly navigation options.
- Allow frictionless sign-up, e.g., don’t make your users fill up big forms when they sign-up.
- Study your target audience carefully and focus on them. Use the “buyer persona” you had developed.
- Enable easy user onboarding.
- Keep the design simple.
- Present data with visual aids and allow dynamic sorting.
- Design an elegant UI.
- Prominently display customer support, FAQ, product guides, and knowledgebase.
Our guide to securing a finance app can provide insights.
You also need to review your SaaS app UI before you launch the enhanced version. This will help you to offer a user-friendly UI, as we have explained in our guide to checking UI before launching apps.
10. Develop APIs to integrate AI and ML modules into your SaaS product
You are likely developing APIs so that the front-end of your enhanced SaaS app can access the new AI and ML deep learning modules. Developing APIs will also enable you to scale your development process. Good optimization of your application is key to its success.
How do you develop APIs? You need to take the following steps:
- Use a tool like Postman to develop APIs, moreover, use a tool like Swagger to document them.
- You need to use your cloud hosting account to host your APIs.
- Use modern databases like PostgreSQL and MongoDB for API development.
- Secure your APIs using techniques like encryption, digital signature, authentication token, quotas, throttling, and secure gateways.
- Create effective rules for API requests and responses, moreover, design the API endpoint URLs smartly.
Looking for help developing APIs? You might find our guide to building RESTful APIs useful.
11. Manage your project
How do you manage this project? First of all, you need to form a cohesive team.
Most PMs that lead SaaS and AI/ML development projects use the Agile methodology since it suits such projects. In this methodology, you need to work closely with your customers and deliver tangible value quickly.
This requires an environment that fosters collaboration. I recommend that you use the “Scrum” technique and form “Scrum teams”. Developers and testers work together in such teams, and they collaborate closely with the business stakeholders and marketers, etc. Read “How to build a Scrum development team?” to learn more about this technique.
You should also use a real-time dashboard to manage the project workflows effectively. Wondering how it can help? Reach out to us at DevTeam.Space! We are uniquely positioned to help you, thanks to our community of top developers and data-driven processes.
Mitigating the key risks while developing SaaS artificial intelligence products
SaaS companies developing AI solutions need to mitigate a few key risks. These risks and corresponding mitigation measures are as follows:
A. AI and machine learning development teams might choose to develop everything on their own
SaaS businesses might choose to code everything about artificial intelligence and machine learning on their own. You would normally expect that from a software company.
However, AI and machine learning development involve plenty of work. SaaS companies might end up spending a lot of time if they develop everything from scratch. This can delay the project.
We recommend SaaS AI companies reuse well-established frameworks, libraries, and tools. Take the example of libraries like Scikit-learn in Python. Popular Python libraries like Scikit-learn already include standard implementation of important machine learning algorithms.
Top SaaS companies consciously utilize these resources while developing AI systems. Therefore, they can significantly expedite their project.
B. SaaS businesses might hire a development team without sufficient experience
Some SaaS companies might think that it’s easy to develop AI solutions. They might hire inexperienced developers.
While it might be easy to create AI chatbots, many AI projects can be highly complex. Inexperienced developers might not successfully deliver such projects.
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If you are developing a SaaS AI product, hire experienced AI engineers. You need developers that know how to use enterprise AI platforms, AI/ML development libraries, etc.
C. SaaS AI companies might choose to manage cloud infrastructure on their own
Senior managers and infrastructure engineers in some digital businesses might choose to manage the entire IT infrastructure on their own. There can be various reasons for this preference. E.g., the company might have already invested considerably in a modern data center.
This approach can certainly work for some companies. However, many companies find it counter-productive. IT infrastructure management distracts them from their core business.
Take the case of the hospitality management industry or e-Commerce businesses. They need to develop software systems that cater to their core business. They would rather use cloud computing technologies and cloud-based services to expedite their project. Such companies will certainly not want to get bogged down in IT infrastructure management.
This is the case with the SaaS industry too. The SaaS market is competitive. Unless you deliver effective SaaS solutions to cater to customer demands, other SaaS businesses will dominate the market.
To compete in this industry, you would want to focus on developing the core products. You might leverage AI in those products. Focus on developing the required AI solutions and not IT infrastructure management! Use cloud computing technologies and cloud-based services to your advantage.
D. SaaS companies might accord a lower priority to data governance
Data is the key to the success of artificial intelligence and machine learning. SaaS companies building AI solutions need high-quality data sets. They might not get that readily. Without data governance, they might not be able to gain value from data.
AI development teams need to analyze the quality of data sets, furthermore, they need to take many steps to improve data quality.
They can only do that if they have a data governance policy. SaaS companies need to implement the appropriate data governance processes, methods, and tools.
E. SaaS companies might not utilize capabilities like machine learning and deep learning
SaaS products can make great use of AI and ML. However, they often don’t utilize them sufficiently. The mitigation of this risk involves a thorough understanding of artificial intelligence and its use cases.
SaaS businesses should analyze how data scientists use ML to gather insights from business data. Customer feedback and customer behavior indicators are largely unstructured data points. ML helps organizations process this data.
Many companies use ML to gain valuable business intelligence. Subsequently, they use the insights to design marketing campaigns.
Banks and financial services institutions use ML to improve the security of financial systems. They use it to analyze usage and transaction patterns. E.g., ML helps them to identify suspicious usage patterns of banking apps on mobile devices.
SaaS companies should strategize on using AI and ML optimally.
Frequently Asked Questions
SaaS stands for Software as a Service. SaaS AI is an extension of this term where AI resources are offered to third-party users.
AI as a Service is based on the Software as a Service concept whereby third-party companies make their products available to third-party users for a subscription fee. In the case of AI as a Service, companies make their service available to users so that they can take advantage of the technology.
Machine Learning as a Service is essentially the same as AI as a Service. It makes ML programs available to users so that they can access their big data predictive analytics and other such services.