No-code platforms empower business users, also known as citizen developers, to go beyond spreadsheets and other confines of emails while transitioning from manual tasks into tools and automations that can be deployed across many departments. However, low-code platforms are commonly targeted at IT staff and they offer means of software building with little or no coding effort.

On top of applications and automation, providers of low-code and no-code platforms have expanded their investments into other emerging spaces. Last year I wrote about how low code is enabling machine learning. Since then there has been an increase in the number of tools and platforms that provide AI development capabilities.

There are various ways in which AI application development can be accelerated with low-code and no code tools. Here are a few suggestions::

  1. Choose the right low-code or no-code platform: In the market, there are many platforms that provide low-code and no-code solutions. However, for you to choose the appropriate platform which meets your specific needs, technical expertise, and price range it is fundamental.

  2. Get started with pre-existing AI models: To start with, most of them possess the conveniently packaged pre-built AI models that address common AI issues such as image recognition, sentiment analysis or speech to text translation.

  3. Use drag-and-drop interfaces: This means that you can create an AI model by simply dragging and dropping items on a canvas such as datasets, algorithms and data visualization tools. In this way coding skills are not required which saves time.

  4. Simplify workflows: Low code and no code platforms have integrated automation features which help automate business processes thus streamlining workflow. With these instruments they can automate boring operations like preprocessing data or dealing with it while focusing on building more sophisticated AI models instead of them.

  5. Work together with a community: Several low-code and no-code platforms have growing communities of developers and professionals that can assist you during your development process. With their help, you will be able to make use of their experience, receive advice on your work, as well as contribute to the completion of projects which speed up the pace of development.

Embedding Artificial Intelligence  abilities SaaS tools
This is how artificial intelligence tools such as machine learning, computer vision, natural language processing, and predictive analytics are added into software as a service (SaaS) platforms. Data can be analyzed automatically with these functionalities provided by artificial intelligence; thus it can learn patterns and trends in the data and also give insights and recommendations to users.

For instance, an AI-based customer relationship management (CRM) SaaS tool may employ AI technology to evaluate its customer dataset with the aim of predicting customers who may churn or will make a purchase. AI-enabled marketing automation software may study how users interact with content on websites and recommend personalized materials or deals aimed at increasing conversion rates. An AI-driven project management solution may assess team performance and forecast the time it will take for the project to end or suggest appropriate allocation strategies regarding resources.

In all, SaaS tools with AI capabilities have become a key factor in improving efficiency, accuracy and scalability of different business operations empowering organizations to make data-driven decisions, automate workflows as well as improve customer experiences.

IoT and Artificial Intelligence using low-code platforms
Companies have started looking into the possibility of having AI and machine learning (ML) functionalities within SaaS, business and technology platforms via low code/no code tools.

For instance, developers can connect their search apps for customers and employees by use of low-code AI search. Technology companies utilize low code platforms to speed up AI use cases; for example Salesforce achieved 90% success self-help rate through its AI-enhanced search while Dell improved employee satisfaction scores three times.

Previously a demanding engineering exercise was to link multiple sensors on IoT platforms which also required integration with machine learning abilities. However, this process is now simplified by the use of low-code platforms allowing more businesses to capitalize on IoT as well as AI technologies in different applications such as smart buildings manufacturing agriculture etc. Many CRM CMS e-commerce among others are some of the existing SaaS platforms that come with these options.

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