The How-To Guide On Implementing AI into a Saas Product
Nowadays, AI software can dramatically boost the profits of organizations with its integration in SaaS products. AI (Artificial Intelligence) works on machine learning principles. The current innovation in computing ability and accessibility of massive data makes things easy. Undoubtedly, the SaaS-based global market is growing consistently. Along with profit, innovation in the industry can impact the lives of people positively. Here are some examples of the implementation of AI into SaaS products.
Stop Undesirable Disruptions
The introduction of ML and AI will be beneficial for your business because you can improve SaaS products’ capabilities. Moreover, you will be able to understand how to add value to different products. It is essential to launch minimum viable products and access feedback from the market. Based on this feedback, it will become easy to improve the quality and benefits of SaaS products.
By following these points, you can create an ML-AI-powered MVP while avoiding disruption to your current SaaS business:
- Make sure to appoint knowledgeable individuals for your team to run your SaaS businesses effectively.
- Prevent possible harmful impacts on current computational resources and IT infrastructure to increase your present SaaS products’ functionality.
- Hire new onboard people with essential competencies and skills to develop ML/AI-powered MVP.
- Planning for sufficient computational resources and IT infrastructure for MVP.
- Having an appropriate plan for the security of ML/AI MVP to avoid incidents related to information security. It is essential for the protection of the reputation of the current SaaS business.
Figure What AI Features You Need
After incorporating ML and AI, you have to think about new features to offer in SaaS products. To evaluate essential features, it is crucial to onboard a qualified PM (project manager), a team containing qualified business analysts, and experienced IT architecture.
For the best decision, conduct several brainstorming sessions and invite stakeholders of your business in these sessions. It will help you to identify the right features for your products. Here are some essential steps:
- Evaluate how every feature may address the particular problems and pain points of customers. Make sure to document the effect of every feature with tools, such as gain and pain map.
- It is essential to prioritize valuable features for MVP. Several tools are available, such as a prioritization matrix.
Project Planning with AI and SaaS
If you aim to add ML and AI to SaaS products, it is essential to realize how software development projects play an indispensable role. You have to obtain success with the help of meticulous planning. Here are some critical steps to plan these projects:
- In the first step, you have to determine the way to introduce intelligent automation powered by AI. Moreover, find out the right AI capabilities to use, such as image recognition, NLP (natural language processing), etc.
- Recognize datasets for the training of proposed ML/AI modules. Remember, the volume and quality of data can influence the functioning of ML/AI systems.
- Plan the use of cloud computing to avoid spending extra time on the management of IT infrastructure. For the transformation of the SaaS products with ML/AI, the cloud can fit in long-term strategies.
- Evaluate the stages you have to follow for the security of the ML/AI-powered SaaS app.
Select the critical technology stack, and it might involve the use of a platform for AI development. Undoubtedly, several options are available to develop ML/AI modules from scratch. If you are ready to design SaaS products, the technology stack must alight with your technology strategy.
- Focus on onboarding a proficient development team.
- It is essential to consider the best practices for the UI SaaS design while introducing ML and AI.
- Pay attention to your validation and verification actions that would require testing and reviews.
Estimates Matter When Merging AI and SaaS
You have to determine how to get critical organizational approvals for the execution of a development project. It is essential to estimate the cost of the software development project. Here are some essential steps to follow:
- Estimate the price of using a computing platform.
- Evaluate the charges of ML and AI development tools.
- Estimate the charges for workforce development.
- Calculate several other costs for administrative activities and hiring.
What Is The Best Cloud Platform For You?
Highlight your method to manage requirements of IT infrastructure for developing ML and AI modules for SaaS products. Remember, SaaS companies may already use cloud computing. It is essential to alight with cloud strategy. The SaaS products are different apps, and the company may use a PaaS (platform-as-a-service). This platform is useful for the development of the ML and AI modules. PaaS platforms, such as Elastic Beanstalk AWS offer several benefits, such as:
- They maintain the networking, cloud infrastructure, operating system and databases, runtime environment, and middleware. It helps you to focus on development.
- It will be easy for you to assimilate APIs with the use of PaaS platforms.
- Famous PaaS platform offers unique DevOps tools and robust auto-scaling solutions.
- Decide on the Technology Stack for Your ML and AI Project
- The use of the right technology in these projects is necessary to add ML and AI to SaaS products. Here are essential aspects to consider to make the right decision:
- In this phase, you have to line up with technology stacked utilized in the current SaaS products. For instance, if you are using Node.js for coding web apps, you must stick to this code.
- Feel free to use AI development tools for creating ML and AI modules because this approach may expedite each project. Google AI Cloud Platform and AI Microsoft Azure Platform are some examples of these tools.
- You may want to code ML and AI programs and apps from scratch. If you’re going to use codes, you will need a powerful programming language, such as Python. It comes with excellent libraries to make coding ML and AI programs easy.
You may want to integrate your ML and AI modules with the current SaaS product using front-end APIs. Feel free to develop APIs RESTful because Representational State Transfer (REST) is a de-facto standard for API development.
Bring On A Experienced Dev Team
Avoid underestimating the significance of a team with competent developers. Your team needs these roles:
- ML/AI developers
- UI designers
- Web developers
- DevOps engineers
Your team members should have outstanding technical skills, and they must have a breadth of knowledge and experience related to the industry. Businesses may use testing to detect errors in ML, APIs, and AI apps. Make sure to implement a comprehensive review procedure that should cover these points:
- UI design
- Technical design
- Requirements of business
Test, Test, Test
It is possible to obtain personalization with the integration of AI and SaaS products. Modern businesses may not cater to your customers with a general approach. Each customer has unique preferences and needs; therefore, your business should make them feel special. Personalization is becoming critical, and SaaS businesses require this touch as well.
You have to find out the method to introduce personalization. Undoubtedly, it isn’t easy to evaluate the behavior and interactions of customers. A business may find it complicated to commit several resources.
After evaluating the behavior of your customers, it will be easy for you to offer more features. Remember, new features may clutter only UI. You can utilize artificial intelligence to SaaS to evaluate the maximum data collected through customers’ interaction with different products. In this way, you can find the preferences of individual customers. Moreover, categorize your consumers into meaningful segments.
Augment Your Team With Chatbots
In your business, you have to prioritize your customers and offer them the best customer service. Customers need more features and the best products; therefore, SaaS businesses also need specialized personnel to chat with customers. For these factors, customer service is becoming complex.
The SaaS business may see extra complexity through customer support. These businesses work remotely because of the latest technology. It is easy to serve customers from different parts of the world. If you want to grow a SaaS business, understand the needs of several customers. It will be difficult for a consistently growing customer base to manage a large team of customer support.
Time-zone and volume differences can stretch the team of customer support. For your convenience, AI-powered chatbots are available. For training of chatbots, the database of customer support incidents will be useful—for example, requests to reset the password. A chatbot may quickly help users based on available knowledge. Remember, chatbots will be 24/7 available; therefore, customers will not notice any delay.
You have to train chatbots to route the most complicated queries to your business’s experienced customer support team. In this way, you can keep customers happy and achieve the best use of the support team. It is a significant benefit of artificial intelligence for SaaS.