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Where is low-code development heading towards? This is a common question that we encounter when meeting with clients. Custom software development is not new and has been around since the inception of computing. However, much has changed in recent years.
To set the stage, low-code development is defined as a software development approach that uses graphical user interfaces, drag-and-drop tools, and pre-built templates to create applications with minimal or no coding. Low-code emerged due to the increasing business demand for robust software solutions, the need for agility, and increasing costs. According to Forrester (2024), the global market for low-code platforms is expected to grow from USD 13.2 billion in end-2023 to USD 30 billion by 2028.
In this article, we will explore key trends in low-code development that are impacting enterprises and the application of Artificial Intelligence (AI) with low-code development.
Five key trends in low-code development
The low-code industry is constantly evolving and innovating to meet the needs of businesses and customers. Here are some of the trends that we can expect to see in the coming years:
1. Low-code for everyone (a.k.a citizen development): Low-code platforms are becoming more accessible and user-friendly, enabling anyone to create applications without coding skills. While it is not expected for business users to take on full development responsibilities, they will have more control in making rapid and personalised adjustments.
2. Low-code for everything (templatised solutions): Low-code platforms are expanding their scope and functionality, allowing developers to create applications for various domains and purposes. Be it web, mobile, desktop, cloud, IoT, or blockchain, low-code platforms can support different types of applications and integrations. Platforms also allow for templatised solutions to be shared, further improving the variety that the market has access to.
3. Low-code for excellence: Low-code platforms are improving their quality and performance (fewer bugs and more efficient code production), ensuring that the applications they create are reliable, secure, scalable, and maintainable (lower cloud services cost). In addition, low-code development platforms come with security frameworks to reduce cybersecurity risks.
4. Low-code for iterative transformation: Low-code platforms help enterprises digitise and automate their workflows, improve their customer experience, increase their productivity and efficiency, and reduce their costs and risks. Enterprises can take on a phased approach and progressively transform their business processes, models, and strategies with minimal retrofitting.
5. Low-code for innovation: Low-code platforms are incorporating advanced technologies such as AI, machine learning, natural language processing, and computer vision to enhance the capabilities and intelligence of the applications they create. For example, generative AI can provide the ability to use natural language to retrieve enterprise-wide reports and data.
Low-code development with AI integration
One of the most exciting and promising aspects of low-code development is its integration with AI. Recent developments of Large Language Models (Generative AI) have opened up opportunities for innovation. Low-code development is a natural complement to applying AI. A simple analogy is to think of the AI model as the car engine and the low code development as the car chassis /body that enables the practical application within a larger user experience.
Here are some examples of how low-code development can benefit from AI integration:
· AI-powered features: Low-code platforms can provide developers with pre-built AI components or easily integrate with external services, such as chatbots, voice assistants, image recognition, sentiment analysis, or recommendation systems, that they can easily utilise in their applications. This allows developers to add AI-powered capabilities without heavily investing in coding or training the models.
· AI-assisted development: Advanced low-code platforms today already use AI to improve developers’ productivity by reducing effort during the development process, such as generating code, suggesting features, or detecting errors. Low-code platforms can use natural language processing to translate the developer's intent into the development or use machine learning to recommend the best templates, components, or integrations for the developer's needs.
· AI-enabled optimisation: Low-code platforms can use AI to optimize the performance and quality of applications, such as automating the testing and debugging of the applications or monitoring and analysing user data to improve the user experience and satisfaction.
At LanciaConsult, we observe that many enterprises are not ready at this point to invest in the development of their own AI capability or proprietary Large Language Models (LLMs). Instead, they prefer taking a more practical approach of leveraging tried-and-tested AI capabilities in the existing technology products as a starting point.
We advise enterprises to use this opportunity to define beneficial AI use cases. Using a low-code platform and working hand in hand with available AI models (ranging from recommendation engines, and chatbots to deciphering large volumes of natural language), enterprises could gain an advantageous head start over their competition.
One example is in the domain of enterprise finance where AI is incorporated within low-code to be used for fraud detection. It uses multiple machine learning models to detect anomalies in transaction patterns and highlights them for human intervention.
Conclusion
In conclusion, low-code development is a rapidly evolving industry transforming how enterprises develop, customise, and deploy software solutions. As the low-code industry matures, its capabilities are growing, providing enterprises with faster and cheaper delivery, higher quality and performance, greater innovation and creativity, and improved customer satisfaction and trust.
We have also observed that low-code platforms naturally and easily incorporate AI for practical uses. Ultimately, enterprises that can identify viable AI use cases alongside low-code development can drive their digital transformation and gain a distinct competitive advantage in the coming years.
