How To Create an AI SaaS Product by Aran Davies

According to a CueReport study, the global SaaS market is expected to grow at a CAGR of 22.6% by 2025, reaching $36.72 billion by 2025.

The report also notes that the introduction of AI in SaaS products will not only enable SaaS enterprises to achieve better growth, but also have a positive impact on people’s productivity.

So how to build a SaaS+AI product? We have the following 10 suggestions

1. Avoid damage to existing services

To stay ahead of the curve, you need to empower your SaaS offerings by introducing ARTIFICIAL intelligence and machine learning technologies to ensure that your offerings deliver a more valuable experience to your customers.

Some companies are already using AI technology, such as SaaS products such as intelligent customer service, to automate traditional human intervention in customer service. Let chat robot, assist customer service inquiries, complete instant processing. The addition of this single function greatly improves the customer satisfaction of the enterprise, but also reduces the operating cost of the company, which is a win-win technology.

  • How do you build an AI-based MVP on top of your existing SaaS business?

  • Make sure you have enough talent on your team to sustain the business;

  • Avoid any adverse impact on the existing architecture;

  • Recruit new people with relevant skills to develop your AI/ ML-driven MVP;

  • Plan the required architecture and computing resources for the MVP;

  • Protect your product data to avoid information security incidents.

2. Build an AI scenario into your product

To find the right AI scenario, you need a highly competent project manager, an experienced architect, and a strong team of business analysts to complete the following steps:

  • Analyze how each feature addresses a customer’s specific pain point, calculate impact and map return on investment for each feature.

  • Use tools like priority Matrix to prioritize your MVP scenarios.

3. Made project planning for AI/ML development

Adding AI and machine learning capabilities to your SaaS offerings is a process that involves software development projects, and you need to plan carefully to achieve your goals.

  • Once you have identified the features that need to be introduced into the AI scene, you need to first determine which specific AI technologies will be used, such as natural language processing or image recognition;

  • Identify a data set to train the AI/ML module. Keep in mind that the quality and quantity of data affect the performance of an AI/ML system;

  • Use cloud computing services so you don’t have to spend too much time on operations; Analyze required product development steps;

  • Choose the right technology stack. This might involve choosing an AI model, or developing your AI/ML modules completely from scratch. It is important to note that your technology stack selection should be consistent with the overall technology strategy of the original product line;

  • In the AI scenario, try to keep the UI design consistent with the original product and have a good user experience; Continuous development and continuous integration are essential.

4. Estimate r&d costs

R&d cost has always been an important basis for enterprise decision making. To get an accurate estimate, you need to evaluate four areas:

The cost of cloud computing and related services

Evaluate the cost of AI and ML development tools

The human cost of the r&d team

Other costs such as administration and events

5. Select a technology stack

The following aspects can be considered in selecting the right technology stack to introduce more ARTIFICIAL intelligence and machine learning into SaaS products:

  • Alignment with technology stacks used in existing SaaS products;

  • Use AI development tools to create AI and ML modules to speed up your project.

  • If you need to code ai and ML programs from scratch, a powerful programming language like Python is recommended;

  • Front-end integration of AI/ML modules with existing SaaS products using apis. Develop RESTful APIs because Representational State Transfer (REST) is the standard for API development.

6. Recruit an excellent R&D team

It is important to have a capable development team, usually composed of at least the following members:

UI designer AI/ML developer Web developer tester DevOps engineer

Team members should have excellent data science skills, industry knowledge, and a good level of experience.

Just testing the AI and ML algorithms and apis will not find all bugs. Additional testing of business requirements, technical solutions, test plans and use cases, UI design, etc.

7. Information security comes first

When introducing new features or functions of SaaS products, the following aspects need to be implemented to ensure application security:

Eliminate critical application security vulnerabilities such as injection attacks, XML external entity injection (XXE), cross-site scripting (XSS), destructive authentication, and more; Use superior security tools and technologies such as multifactor authentication (MFA), encryption, next-generation firewalls, antivirus solutions, and intelligence capabilities to respond to real-time threats; Incorporate safety and compliance testing into CI/CD testing rather than making it a low-priority task; Ensure API security.

8. Keep SaaS user interface design principles in mind

You already have a SaaS UI solution in place, but how do you follow best practices for SaaS UI design?

The suggestions are as follows:

  • Provide user-friendly navigation options;

  • Choose a simpler and more accessible registration method;

  • Study your target audience carefully and pay attention to them;

  • Keep the user interface design simple and elegant;

  • Visualize data and allow dynamic sorting;

  • Highlight customer support, FAQs, product guides, and knowledge base.

9. Module integration through API

Use tools like Postman to aid in developing apis, and use tools like Swagger to generate documentation;

Use a cloud host to host the API;

Use modern databases such as PostgreSQL and MongoDB for API development;

Protect apis with encryption, digital signatures, authentication tokens, degrade, limiting traffic, and security gateways;

Design valid rules for API requests and responses according to the specification, and, in addition, cleverly design the URL path of the API.

10. Project management ability

To manage the project well, you first need to build a cohesive team.

Most SaaS and AI/ML development projects practice the “Agile R&D” approach, Scrum, because it fits so well. In the Scrum methodology, you need to work closely with your customers to deliver tangible value quickly.

To use Agile and build a Scrum team, you will need a collaboration platform focused on agile development.

Ideally, it should be professional, intelligent, flexible, with a professional understanding of agile development, and can provide PaaS level capabilities tailored to your own business needs… At the same time, the project collaboration platform itself should also be an excellent SaaS+AI product, enabling your development team to provide a cleaner workflow of collaboration and project management experience through intelligent scenarios.

An AD

LigaAI’s new-generation intelligent R&D management platform is committed to providing professional, intelligent and flexible SaaS+AI project collaboration experience for developers

Follow us to learn more about LigaAI products!

Read more “Industry Think” lessons shared and best practices… Follow the Nuggets at @ligaai

To learn more about our products, please visit LigaAI – a new generation of intelligent R&D management platform