No-code platforms empower business users, also known as citizen developers, to move beyond spreadsheets, collaborate more effectively beyond emails, and transition from manual task execution to using tools and automations across various departments. In contrast, low-code platforms are typically geared towards technologists and offer ways to build and maintain software with minimal or no coding effort.

Providers of low-code and no-code platforms are expanding their investments beyond applications and automation, venturing into other emerging fields. In the past year, I covered how low-code is making machine learning accessible. Since then, a growing range of tools and platforms have emerged, providing capabilities for AI development.

There are several ways to accelerate the development of AI applications using low-code and no-code tools. Here are some suggestions:

  1. Choose the right low-code or no-code platform: There are several platforms available in the market that offer low-code and no-code solutions. It's essential to choose the right platform that aligns with your needs, budget, and technical expertise.

  2. Leverage pre-built AI models: Many low-code and no-code platforms come with pre-built AI models that you can use to jump-start your development process. These models are designed to solve common AI problems such as image recognition, sentiment analysis, and speech-to-text translation.

  3. Use drag-and-drop interfaces: With drag-and-drop interfaces, you can create AI models by dragging and dropping components such as data sets, algorithms, and data visualization tools onto a canvas. This saves time and eliminates the need for coding expertise.

  4. Automate workflows: Many low-code and no-code platforms come with built-in automation tools that can help you streamline your workflow. You can use these tools to automate repetitive tasks, such as data cleaning and processing, and focus on building more complex AI models.

  5. Collaborate with a community: Many low-code and no-code platforms have a thriving community of developers and experts who can help you with your development process. You can leverage their expertise, get feedback on your work, and collaborate on projects to accelerate your development process.

Artificial Intelligence capabilities integrated into SaaS tools
This refer to the integration of artificial intelligence technologies such as machine learning, natural language processing, computer vision, and predictive analytics into software as a service (SaaS) platforms. These AI capabilities enable SaaS tools to automatically analyze and interpret data, learn from patterns and trends, and provide insights and recommendations to users.

For example, a SaaS tool for customer relationship management (CRM) may use AI to analyze customer data and predict which customers are most likely to churn or make a purchase. An AI-powered marketing automation tool may analyze user behavior on a website and recommend personalized content or promotions to increase engagement and conversions. An AI-powered project management tool may analyze team performance and predict project completion times or recommend resource allocation strategies.

Overall, AI capabilities in SaaS tools enhance the efficiency, accuracy, and scalability of various business processes, enabling organizations to make data-driven decisions, automate workflows, and improve customer experiences.

AI-enabled search and IoT using low-code platforms
Companies are discovering possibilities to develop AI and machine learning (ML) functionalities within SaaS, business, and technology platforms using low-code and no-code tools.

For instance, low-code AI search aids developers in integrating data sources, constructing search apps for customers and employees, and harnessing AI and ML features. Even technology firms employ low-code platforms to hasten AI use cases. For example, Salesforce leveraged AI search and achieved a 90% self-help success rate, while Dell improved its employee satisfaction score three-fold.

In the past, connecting numerous sensors to IoT platforms and integrating them with machine learning capabilities was a challenging engineering task. However, low-code platforms are simplifying this process, enabling more businesses to leverage IoT and AI technologies for various applications such as smart buildings, manufacturing, and agriculture. Many SaaS platforms, including CRM, CMS, and e-commerce, provide AI and low-code development options.