Undeniably, Artificial Intelligence and Machine Learning have now become the central marketable assets for SaaS businesses. Many SaaS businesses have started leveraging the power of AI platforms to scale their revenue by offering better and personalized consumer experiences and services. And as stated in Adobe’s Digital Intelligence Briefing, many leading companies, many business giants are more likely to adopt AI for marketing to deliver excellent customer experiences.
We all have witnessed the intense push from enterprise to the Software-as-a-service in the past few years has enabled the users to step aside the hurdles associated with software maintenance and implementation. The key areas include ease of installation and upgradation, streamlined testing, as well as minimizing large upfront costs. Artificial Intelligence aggregates a large amount of customer data and distills it into automatic processes, all these tasks, that were essentially done by humans.
As the decision-maker of any SaaS company would agree, how much time, effort and manpower it takes to keep customers engaged with the products. But AI enables the companies to automate many of these customer-related processes like marketing upsells, ongoing customer services, etc. And not only this but almost every industrial sector today leveraging the power of AI.
Let’s know now, how could we implement AI into the SaaS product to increase the revenue!
1. Prevention of Distribution into the existing SaaS business
AI and ML integration add new power and potential to your SaaS products. But how would you know you are adding value to your product?
Think to launch an MVP, as this would help you to measure the customer behavior to your products and you can simply enhance your SaaS product. To put it simply, MVP is the best possible way to improve your SaaS product. As it’s a product with enough features to satisfy customers and would provide you with feedback for future product development. MVP has enough core features to effectively deploy the product and no more.
Remember this particular strategy targets avoiding creating the products that the customers do not need and seek to maximize the customer information with the least money spent. But how could you create a successful AI/ML-powered MVP? Delivering AI/ML-powered MVP models to the real-world requires an appropriate real-time infrastructure. So you need to think in a way that there are no adverse impacts on existing IT infrastructure and computational resources and your current SaaS product functions effectively. All you have to do is to appoint such a knowledgeable team that can run your AI/ML-powered MVP products without disrupting your current SaaS business. Consider onboarding new people who are skilled enough and by using various techniques, can deliver AI-Powered predictions, scorings, recognized patterns, complete business tasks and finalize your MVP development journey. You need to plan in such a way that wastage of computational and infrastructural resources should be avoided and your AI/ML-powered MVP is secured to avoid any unnecessary security related issues. This is the best way to protect your company’s goodwill.
2. Decide on the AI/ML-Powered Features, Your SaaS Product Should Have
So, once you have decided how you would prevent your existing SaaS product without any disruption into the existing business, it’s time now to make up your mind about, what AI/ML-powered features your SaaS product should contain. To decide, you need to hire competent manpower resources like PMs, IT architects, and proficient business analysts.
Take daily standups with your business stakeholders and start to brainstorm ideas about the features to be added. Think and plan about the features that would represent the pain point of your customers, the ways how AI can bring the option of hyper-personalization to your SaaS. All this is basically to work out a process to define what to prioritize. Having a clear picture for channelizing your tasks, and deciding which of those you should prioritize, can save you from wasting your time deliberating on less essential things. This is going to provide stakeholders with a reliable process for resolving disagreements, and deciding on which proposals to focus on for your AI/ML-powered MVP.
3. Project Brainstorming for AI/ML integration into your SaaS Product
How do you think you would plan a project while integrating the AI/ML into your SaaS to achieve success? There are many SaaS providers who begin this journey with a lengthy list of possible projects. But this is actually not the right way.
You need to go step by step while planning and brainstorming the project. First and the foremost, you need to plan where you would use “personalization and Automation” for instance in SaaS, the NLP (natural language processing) and AI to learn from previous users interaction can help you configure user interfaces and as a result, you can cater to the need of the individual. Or the use of Chatbots, that provide users with answers to their basic to complex questions. These also help in cost reduction and keeping the customer engaged.
Think, plan and analyze where you would implement and use cloud computing so that you can avoid the upfront costs and complexities of maintaining your IT infrastructure and simply pay for what you use and when you use it. The cloud computing strategy is beneficial in the long run as well you can provide the services to a wide range of audiences.
Next is the Cloud security issues, which are always a hot topic among SaaS, and traditional security measures. Make sure you take precautionary measures to keep your AI/ML-powered SaaS product secure. Select the technology stack that you are going to use while developing the AI platform and in case you are going to build AI/ML modules from scratch, you need to make sure that the technology stack you chose should match with your overall development strategy. After the techno stack, decide what competent team you want.
Make sure you practice on UI design, as this point is crucial if your ML product has to interact with operators or even be replaced by them. The design should have less complexity and add the minimum necessary amount of stress to the users of the system. Like the, chatbots are often machine learning-based but they can even be seamlessly operated by a human.
Make sure you do proper verification that also involves the review and testing.
4. Estimate your project to add AI/ML to your SaaS product
After deciding on the features, techno-stack, and team, it’s now time to estimate how much it would cost to develop AI/ML modules for your SaaS project. Many project managers and owners can easily estimate the time and money requires to develop a standard project. But however, estimating the time of the delivery and internal cost of the AI/Ml based project might be tricky. So to make it easy you can work on the following like estimating the cost of the cloud computing platform, development tools, cost of hiring and development if the manpower and other administrative activities.
5. Look for Cloud Platform for Development
The next is to use the cloud computing technique. While developing the AI/ML modules of your SaaS product you need to take care of your It infrastructures as well. Like other firms, you can also adopt cloud-based services and leverage the SaaS (Software as a Service) & PaaS to execute and deploy AI-infused cloud results. Cloud Machine Learning (CML) platforms, for instance, AWS ML, Azure ML, TensorFlow (Google Cloud ML) powers the creation of artificial intelligence and machine learning models. It is advisable to use PaaS while developing the AI/ML modules as well. The PaaS service is designed to provide access and functionality for a cloud environment. This implies the integration with databases, messaging systems, portals as well as storage components. Also, PaaS makes the integration of APIs easy.
6. Decide on the Technology Stack for the AI/ML-based Project
Your machine Learning team can use python, as it makes programming a lot easier. Also, it is when you integrate your AI/ML modules with your existing SaaS product front-end. It is advisable to use APIs, that too RESTful APIs for standard development.
7. Hire Competent Development Team
A good team means working with one another and working towards a shared goal. Teamwork has always been a fundamental part of an organization. The team should have the industry domain knowledge and should be skilled and competent enough which could help the organization to achieve triumph. The AI/ML-powered SaS software development team needs the following professionals:
- Project manager
- AI/ML developers
- UI/UX developers
- Web developers
- DevOps engineers
- Back-end developers
- Quality analysts experts
- Delivery person
Since all the errors can not be rectified through testing, there is always a need for the proper execution of the review process which includes the following steps:
- Organizational needs
- Technical design and proper test plans,
- UI designs.
Hence we can say that the combination of a competent team and a proper pre-meditation can help the business can increase profitability and success.
8. Secure Your SaaS Product While You Introduce AI and ML
App security isn’t a feature or an advantage-it’s a bare necessity. One breach could only cost you the millions of dollars but also a lifetime of trust. That is why security has to be a priority from the moment you start writing the code. Because while managing a website it’s literally crucial to stay on top of the most critical security risks and vulnerabilities. So you need to think of the ways you can secure your app.
For Instance, you can make use of such common security vulnerabilities, like
- Broken Authentication
- Sensitive Data Exposure
- XML External Entities (XXE)
- Broken Access control
- Security misconfigurations
- Cross-Site Scripting (XSS)
These would also show risks, impacts, and countermeasures. However, there are others like, Admins can install antivirus software, raise the firewall, deploy many encryption technologies, and even can run vulnerability tests periodically. But the sobering reality, if multi-factor authentication (MFA) is not there in place, these other security measures can be bypassed. The best practice for IT managers is to categorize their systems to identify the ones that contain access to business-crucial data, and then add MFA on top of them. MFA has low complexity, which makes it an easy addition. Additionally, It can be rolled out quickly without busting the budget.
9. Keep The Important SaaS UI Design Principles In Mind
Creating appealing and attracting software products is important for gaining usership. There are plenty of great software products in the market, but there are only a few that really rise to the challenge of creating user-friendly products possible. Nowhere is optimizing the user experience more essential than it is with SaaS products. Web design principles have heavily influenced SaaS products because they are delivered via the Internet. Usability, attractiveness, and functionality have become crucial in order to provide end-user experiences. Hence, all SaaS products must also be imbued with modern web design practices. Following are the recommendations to be kept in mind:
- User-friendly navigation and UIs
- Simplified sign ups and registration process
- Get acquainted with your target audience and know about them
- Use onboarding to clarify functionality for users.
- Simplified design architecture
- Streamline information architecture to make it intuitive and minimal.
- Dynamic data sorting
- Elegant UI design
- Engage with users and inform them about KPIs with dashboards.
- Ensure that SaaS product support is useful and available round the clock.
10. Develop APIs to Integrate AI and ML Modules into your SaaS Product
The APIs are the big waves and are of vital importance needed by all businesses and industries as they allow computer programs to be used by another, which establishes communication between two well-distinguished programs. Basically the function of APIs is to channelize your development process.
But have you given it a thought how you would build API? Below mentioned are the tools you could use:
There are currently several leading options like Postman and Swagger.
Modern databases like PostgreSQL, and MongoDB.
Securing APIs with encryption, digital signature, the authentication token, gateways, etc.
A software development task is not easy to perform, especially when it comes to the inclusion of AI and ML. you need to engage with the company having ample experience in delivering such projects.
You need to look for the company with the right capabilities and competitiveness. Matellio is an eminent software engineering company with the right capabilities.
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