How Generative AI can help Low code platforms to build applications faster?
Building at Lightning Speed: The Impact of Generative AI on Rapid Application Development in Low-Code Platforms
Low-code platforms have emerged as powerful tools for businesses seeking to optimize operations, develop applications faster, and reduce costs. According to a Gartner survey, these platforms are projected to create over 65% of all applications by 2024, highlighting their transformative potential across industries. When coupled with advanced generative AI, low-code platforms can redefine business applications’ landscape, propelling a new era of digital transformation. Let’s dig into how these two technologies can revolutionize various industries with examples.
The Rise of Low-Code Platforms
Low-Code platforms are rapidly becoming a vital part of the technology ecosystem. This trend is driven by number of factors such as accelerated application development, increased agility, cost-effectiveness, and the democratization of application development. McKinsey reported a 90% reduction in development time using a low-code development platform, significantly reducing development costs.
Low-code platforms accelerate application development by providing ready-made, highly customizable components. These platforms translate complex coding needs into intuitive, visual interfaces, democratizing the process of application development.
Industries such as finance have effectively leveraged these platforms for automating internal processes, while healthcare organizations have used them for patient portals, telemedicine applications, and improving the accuracy of medical records management systems.
However, using a low-code platform involves understanding its visual tool and detailed understanding of its base components, a process that can be challenging for some. This is where there is a good role Large Language models can play.
Revolutionizing Low-Code with AI
While low-code platforms provide a visual interface for application development, users still need to break down their desired tasks into logical pieces and model them within the platform’s visual editors to connect various components together. This necessitates the need for clear understanding of the components and its underlying behaviors.
This is where Generative AI could come into play, offering a logical translator that maps user intent to specific components, and even building new components when required. Imagine an user simply describing their goal in plain English, and the AI agents translates it into specific tasks. By identifying necessary subtasks, an AI agent can logically map these to available tools / components and executes them, opening doors for personalized workflows and unique solutions.
Lets take an example from ChatGPT to illustrate this point. Users provide a task, and ChatGPT identifies relevant plugins needed to accomplish the task. For instance, if a user wants to find a vegan restaurant, ChatGPT uses OpenTable plugin to provide the recommendation and links to make a reservation. While the user focuses on their goal, ChatGPT translates this into specific subtasks, executing them accordingly.
In a business context, generative AI can use available tools for achieving its goals. If a task is given to a generative AI model, using its logical inference capabilities, it breaks the task into subtasks and maps them to the necessary tools. This enables users to focus on their objectives while leaving the implementation details to the AI.
The Integration of AI and Low-Code Platforms
Applying this AI-driven approach to low-code platforms brings about a paradigm shift. Instead of understanding each component’s functionality and assembling them manually, users can express their goals, and AI can determine how to use the available plugins or tools (exposed by the Low code platform for the LLMs to use) to accomplish those goals. I will explain this using two examples.
Example 1: Healthcare claim clinical validation
Let’s consider a healthcare workflow: validating adequate clinical evidence from notes for a particular treatment in an insurance claim. This involves someone reading information mentioned in the claim and validating for evidence to be present in the clinical notes.
If there are separate components to read ICD codes from the claim, component to understand the patient’s symptoms from clinical notes, and a component to verify there is a context sensitive match between what was extracted from the claim and the notes, these components could be exposed as plugins for large language models to use them in a workflow realtime based on user intent. These are the trained components Low code platform can expose as tools for the LLMs to use to create the workflow.
Instead of an user opening a visual tool and putting these workflows together manually, all they have to do is to describe what they want to achieve. The AI will understand, interpret and create subtasks to achieve their goals. This approach could ensure swift, accurate, and fraud-free processing of insurance claims.
Example 2: Preparing for subscription renewal
Lets take another example from Finance. Imagine a scenario where a finance product company is sending a renewal notice to a customer. When it’s time for a customer’s subscription renewal, its important to highlight on the positive experience and the benefits the customer had with the platform. The AI system need to analyze previous interactions, understanding the challenges the customer has faced and the feedback they have provided. Based on this knowledge, the system need to highlight new features that have been added since the last renewal, addressing the specific requirements of the customer. Moreover, the AI system should also lookup any references to discounts customer has requested or referenced in the recent conversations and also any competitive references made. Taking this into account, the system needs to apply the discount to the renewal offer, summarizing the value customer got from the product thereby demonstrating its attentiveness to the customer’s needs and fostering a sense of loyalty.
To build such a powerful workflow, you need foundation components that can extract challenges customer faced from the support data, components that can extract any references to discount / pricing / competitive references from email conversations, components that can extract key features added to the product and components that can logically map customer requests to key features added. Once these components are exposed as tools, an AI system based on user intent can automatically put together the desired workflow Realtime.
Through this intelligent integration, the finance company can deliver a renewal notice that is tailored, efficient, and demonstrates a deep understanding of the customer’s preferences and history.
Exposing reusable components
The key is exposing the pre made components / integrations Low Code platforms already possess as plugins for AI models, thereby empowering AI to connect them together. In doing so, new and unique solutions can be put together quickly using these foundational components, offering a unique blend of efficiency and innovation. Visual editor low code platform has still can be potentially used to test and tweak the base structure put together by the AI model. This improves the speed at which solutions can be put together by customers.
The important part here is the out of the box fully trained reusable components. If the customer has to build and train those components the value is lost. Low code platforms typically have rich set of integrations with business eco system. By exposing those rich integrations Low code platforms already has in to logical components or tools for the AI system to leverage to put together a logical workflow would help users experiment with many business workflows in their enterprise.
If you are a product manager at a Low Code platform company, you should consider the types of Out-Of-The-Box (OOB) domain-specific components that can be built, packaged, and exposed for Large Language Models (LLM) to consume. These components would enable LLMs to logically and easily assemble workflows. The most valuable elements are the trained OOB components / tools. In light of this, it raises the question of whether we could establish a library of tools available in a standardized format from many Low code solution providers to work in harmony? Technologies like Langchain can facilitate the consumption of these tools
The Road Ahead
The winning low-code platforms will be those offering useful and highly relevant industry-specific components as tools. A healthcare-focused platform, for instance, could benefit from components that extract symptoms, treatments, medications, or digital biomarkers from clinical notes. These foundational components / tools will help bring together a variety of workflows using a combination of AI + visual editor for further tweaking.
This innovative approach will promote experimentation, enabling companies to trial new workflows swiftly, refine the successful ones, and deploy them on a larger scale. However, it will also necessitate stringent change management and governance to ensure the innovations are deployed responsibly.
In conclusion, the integration of Generative AI and Low-Code platforms presents a powerful paradigm shift towards efficient, flexible, and personalized business application development. The future of business innovation lies in the successful synergy of these technologies, which will streamline workflows and foster creativity while preserving the core principles of effective business management.
