AI has deeply rooted in SaaS products, becoming nearly unavoidable. Around 82% of cloud companies have embraced AI, seamlessly integrating it into their SaaS offerings. For these providers, it's not merely a choice but a vital strategy to remain relevant in the ever-evolving market.
Take Microsoft. By acquiring 49% of OpenAI and making substantial investments, the tech giant was looking to outpace major competitors like Google. This collaboration resulted in the creation of powerful tools such as the Azure OpenAI Service, empowering developers to add OpenAI's advanced models into their applications, and elevating their software with cutting-edge AI features.
Google has also ventured into creating AI SaaS products, like Bard, but their journey hasn't been without setbacks. A notable incident on February 8, 2023, saw Google's parent company, Alphabet, lose a staggering $100 billion in stock market value due to an AI tool mishap during a demonstration.
Yet, some companies incorporate AI features for the hype surrounding them rather than a clear-cut need. In the case of Windows, some users on platforms like Reddit express skepticism, deeming AI features unnecessary.
Here at Flyaps, with our AI-focused decade of experience in software development, we recognize that, despite diverse motivations, companies universally encounter mounting pressure to incorporate AI capabilities into their SaaS products. In this article, we'll delve into seven steps of creating an AI SaaS platform. And we’re going to start with exploring the best way to decide on the AI features for your product. Keep reading - there is more.
Selecting AI SaaS features
Imagine a company, providing smart solutions for financial organizations. Suppose they recognize the potential of AI SaaS in their field, so they decide to add some AI features to their popular product - let it be a financial automating platform. Let's follow the company on its first AI SaaS strategy step - deciding on the AI features. These features must not only align with their business goals but also effectively address customer pain points.
Understanding customer pain points
To understand the challenges and opportunities that AI can address in the financial sector, the company conducts comprehensive market research, surveys, and insightful interviews with financial professionals. This process reveals a nuanced understanding of the intricacies and pain points that their clients face in their day-to-day operations.
The company uses tools like the “pain and gain map” to document customer pain points and potential gains from AI features. This visual representation guides the company's AI strategy in alignment with customer needs.
Evaluating each feature's impact
After understanding what bothers their customers, the company carefully looks at each new AI feature they want to add. They think about how these features can help solve the customer problems. They consider factors like efficiency improvements, cost reduction, and the overall enhancement of the user experience. The main goal isn't just to add features for the sake of innovation but to make a real and positive difference in the daily operations of financial professionals.
To figure out how much of a difference these new features can make, the company uses measurements. They set up metrics for each feature whether in terms of revenue increase, improved customer satisfaction scores, or better operational efficiency. This data-driven approach ensures that the chosen AI features bring clear and measurable benefits to people using the product.
Prioritizing features with the prioritization matrix
Now that the company knows what problems customers have and how each new feature can help, they need to decide which features are the most important. To do this, they carefully set up a plan called the “prioritization matrix.” This plan includes specific criteria like how much customers will like the feature if it's technically doable, and if there's a demand for it in the market.
The prioritization matrix is the main tool in this process. It helps the company rank each new feature based on the criteria they set. This careful way of deciding ensures that the features they choose aren't just great ideas but are also the ones that will have the biggest positive impact on the company's overall goals.
Developing a minimum viable product (MVP)
Now the company can create a minimum viable product (MVP). This isn't an advanced AI-powered SaaS product, but a basic version with only the most important features. It's like a quick way to get their enhanced financial platform out to the market.
The MVP for the company is a way to hear what users think in the real world. So, they give the MVP to financial professionals and collect feedback. It's a flexible approach that ensures AI features change and improve according to what people want.
Planning a smooth AI integration
To make a minimum viable product and, further, the final AI SaaS product, the company should focus on seven key aspects that we are going to reveal.
The user interface (UI) is the first thing through which the user interacts with the platform. It must simplify the process of users adopting the product and it’s crucial for user satisfaction. Here are the five most important aspects our imaginary company should take care of:
- Clarity and simplicity: A simple design reduces cognitive load, making it easier for users to navigate and understand the interface. It's better to prioritize essential information to streamline the user experience. For instance, a banking app that prioritizes actions like checking balances and making transactions might choose to add them on the main screen. It would minimize complexity, making way for a smoother user experience.
- Consistency: Maintaining a uniform look and feel throughout the interface ensures that users can predict how elements will behave. Take media platforms. Their navigation menus remain in the same position across various screens, creating a seamless and predictable user experience.
- Responsive design: Design for various devices and screen sizes to accommodate users accessing an AI-powered SaaS interface from different platforms. A responsive design results in a seamless experience regardless of the device users access your product from. For example, an AI-driven collaboration tool adjusts its layout seamlessly, whether accessed from a desktop, tablet, or mobile device, ensuring users can collaborate efficiently no matter the case.
- Accessibility: To ensure this aspect, our fintech company should prioritize accessibility to make their AI SaaS interface usable by a diverse audience. They should also focus on readable fonts, color contrast, and alternative text for images to ensure inclusivity. For instance, they can incorporate high color contrast and provide screen reader compatibility, making the app accessible to users with visual impairments.
Right after the design phase, it’s time for our fintech provider to choose an AI tech stack.
AI technology stack selection
Selecting the correct technology stack is a critical step when seamlessly incorporating AI features into a SaaS product. For successful implementation, SaaS providers take into account key factors such as scalability, compatibility with existing systems, and robust support for AI development frameworks.
Businesses have two main variants when integrating AI and ML into a SaaS product:
- If the product is built using a specific language or framework, such as Node.js, it may be best to stick with the existing setup. This will ensure compatibility and a smoother integration process.
- An alternative option is to code AI programs from scratch, especially if the current tech stack isn't optimized for AI. Python, a powerful language with excellent libraries and frameworks for AI and ML, is emerging as a popular choice. Adopting Python for AI development allows you to leverage its extensive ecosystem and community support.
To learn more on this topic, check out our detailed article for a complete guide on picking the best AI tech tools.
Cloud computing integration
As cloud computing is a core element of any SaaS, our fintech provider is considering the best way of integrating a managed cloud services platform.
Suppose the company's platform experiences a surge in user interactions during market fluctuations. In this scenario, integrated cloud services seamlessly scale resources, ensuring that real-time data processing and AI-driven insights remain swift and uninterrupted. This capability fosters a resilient and responsive financial advisory system.
Here's how the company can effectively integrate AI SaaS with cloud services:
1. Before integration, they conduct a thorough assessment of the infrastructure requirements and understand the computational and storage needs of the AI SaaS application.
2. They chose a cloud service provider among Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). The provider aligns with the company's business goals, offering essential tools and resources for AI development.
3. The company's tech team considered incorporating a Platform as a Service (PaaS) solution like Amazon's AWS Elastic Beanstalk. PaaS platforms simplify the management of various cloud components, encompassing infrastructure, networking, databases, and operating systems. For instance, AWS Elastic Beanstalk abstracts away the complexity of infrastructure management, enabling developers to focus on application development.
Leveraging cloud services provides scalability, flexibility, and cost-effectiveness. It allows seamless integration of AI functionalities without the need for extensive hardware investments.
Assembling a skilled development team
Bringing an experienced development team on board is key to seamlessly integrating AI into SaaS products. The team should include different roles, such as UI designers, AI/ML developers, web developers, QA testers, and DevOps engineers. They must grasp the intricacies of AI products and possess relevant expertise.
Speaking of a winning team with all types of tech specialists. At Flyaps, we've been assisting businesses globally in adopting the latest technologies, including AI. Our successful AI projects, such as the GlossaryTech Chrome extension used by industry giants like Amazon, Disney, Tesla, and Cisco, or the CV Compiler, a cutting-edge resume-parsing app, stand as testimony to the promising potential of AI.
Safeguarding the wealth of user data entrusted is the top priority for our imaginary fintech provider. Therefore, their tech team initiated the process by addressing application security issues and fixing vulnerabilities like injection, cross-site scripting (XSS), broken authentication, and XML external entity (XXE).
To make sure only authorized users have access to their AI SaaS, the company adopted a multi-layered security approach. The tech team implemented robust authentication methods, such as multi-factor authentication and strict access controls. Sensitive data is protected through encryption – a digital vault with algorithms like TLS, ensuring its security both in storage and during transit. Additional digital security measures include a firewall to prevent unauthorized access and antivirus tools to scan for threats. They also rely on threat intelligence to identify potential hazards in the digital landscape.
The seamless integration of security and compliance testing is crucial, ensuring it is part of their overall testing process.
The last crucial step the company took here was working on the security of their application programming interfaces (APIs) that connect the project with external systems. To safeguard against unauthorized access and potential data breaches, the tech team implements secure API design and authentication mechanisms, such as API keys or tokens.
Comprehensive testing and review
Before deploying AI into a SaaS product, the team has to test and review it comprehensively. This involves subjecting the integrated AI components, data inputs, and the overall system to rigorous testing protocols. The goal is to identify and rectify any potential issues that might arise during or after the integration process.
During this step, our fintech provider meticulously estimates the costs associated with implementing AI features into their SaaS platform. This involves assessing the expenses of utilizing cloud computing platforms, acquiring advanced AI and ML development tools, calculating labor costs for the development team, and considering additional costs for hiring and administrative activities. This comprehensive cost estimation allows the company to align its budget with the project goals.
As you wrap up your decision-making process, whether you're starting from scratch to build an AI-powered SaaS platform or just adding some AI features to your existing product, having a clear plan for AI adoption is crucial. Equally important is finding a skilled tech team with expertise in AI. The combination of these two strategic moves ensures a smoother journey into the world of AI, whether you're creating something entirely new or enhancing what you already have.
Still have questions about creating an AI-powered SaaS platform or adding AI features to your existing SaaS products? We're here to help! Just reach out to us, share the details of your project, and we'll come up with a solution tailored to your specific needs. Let's chat!