By Gregory H Bergmann
The need to capitalize on the most in-demand emerging technologies and the growing need for faster creation of enterprise applications have increased the adoption of low-code development. Low-code has been garnering a lot of attention among business leaders. Studies estimate that atleast 70-75% of business applications will be developed in low-code/no-code platforms by 2025. The main reason is that low-code allows developers to create code and feature sets including third-party integrations into reusable code snippets or templates. It also allows for user-friendly interfaces and design models from pre-existing templates or features to speed up development. Plus, it empowers citizen developers or users with very minimal programming knowledge to rapidly create, enhance, deploy, and maintain applications.
Compared to traditional development models, low-code development is far less complex, has a faster time to market and allows organizations more agility as well. This helps create more applications with less duplication, at a much faster rate.
Low-code: The new catalyst of artificial intelligence (AI) adoption?
Low-code provides a more accessible way for businesses to adopt AI technologies, through pre-built AI models, without requiring a complete overhaul of the system or extensive coding. Businesses can tap into the potential of this powerful combination of low-code and AI to launch intelligent applications at rapid speed. For instance, a business could use a low-code platform to add natural language processing (NLP) capabilities to an existing customer service application, enabling it to respond to customer enquiries automatically in natural human language.
AI-Low code use cases
There are several use cases for AI and low-code working together, from simple image and speech recognition and chatbots to more sophisticated ones like using AI to analyze data and make predictions about future outcomes for retail, finance, and healthcare; fraud detection in banks and financial institutions and building applications that use AI to provide personalized recommendations and experiences to users based on their interests and preferences.
Integrating AI models
Low-code platforms provide prebuilt AI components that can be easily added to applications. These components can be customized and trained with specific data to meet the needs of the business. Low-code platforms can connect with various data sources, including databases, APIs, and third-party services to retrieve data and integrate it into the AI models. Users can select an AI model from the prebuilt components on low-code and customize it by specifying the input and output data, training parameters, and performance metrics to meet the business requirements. Low-code can connect with AI model development tools as well to help import and use pre-trained models, thus saving time and reducing resources. Once the model is trained, it can be automatically deployed into the application.
Training AI models on low-code
In addition to integrating pre-built AI models, low-code platforms can also be used to build, train, and deploy custom AI models. Interestingly, the latest wave of AI models has shown increased efficiency with natural language processing. Some of these new models allow for robust language processing with little input. These AI models can be trained to learn to develop boilerplate or general templates on low-code. Such templates could act as an underlying framework for an application. This could accelerate the development process further and reduce the already decreased time to market!
It is to be noted that non-technical individuals may become more adept at creating more complex applications, potentially with third-party integrations by directing the AI to assist and create the underlying components. While we may be still very far off, the plausibility exists. However, this would by no means replace the need for custom development but may help increase creativity for business teams through more advanced prototypes or reduce the time to market for applications by allowing for more robust application frameworks derived from their written needs.
Final thoughts
Low-code and AI can work together to democratize AI, making it accessible to businesses of all sizes. By integrating AI with low-code platforms, businesses can realize many benefits, including digital transformation, business resilience and scalability. Also, AI-powered low-code can be integrated with other emerging technologies, such as blockchain and the Internet of Things to create innovative applications. Overall, the future looks very exciting!
The author is vice president, delivery and operations, WNS-Vuram
