By Bharath Kumar, Head of Marketing and Customer Experience—Zoho Creator
Generative AI (GenAI) is revolutionising the way companies run by fundamentally transforming numerous business operations and processes. One such area that’s seeing impact at scale is application development. With large language models (LLMs) trained on billions of coding parameters, GenAI helps developers by generating quick fixes for repetitive tasks such as boilerplate codes, database operations, and standard UI elements, allowing for a faster and better application development experience.
However, GenAI isn’t the first contender in the space to offer a simplified development environment. Low-code and no-code development platforms have been doing the same for far longer to help businesses adopt agility in software development life cycles and go-to-market strategies. So, given the scope and potential of GenAI and the way its usage levels skyrocketed across the world, it wasn’t a surprise when industry discussions turned to whether the former can replace low-code platforms for good. The answer is no.
Generative AI will not replace low-code platforms
GenAI without human cognition can’t replace low-code platforms, but it can greatly improve the value delivered by the latter. For example, GenAI can easily generate a specific block of code in a specific programming language for a particular functionality, based on your prompt. However, what AI can’t do seamlessly is tell you where this snippet should be plugged in, what would change if you tweak a certain component, or if the generated snippet has the scope for optimisation in view of the desired result.
Low-code/no-code platforms and generative AI will coexist in the future
There are certain limits to how much a low-code platform, on its own, can balance abstraction and control. For instance, a sales lead with no background in app development but wants to build an automated system to monitor deals in the pipeline may feel lost even with a low-code platform at their disposal. One way to close this gap is to provide extensive onboarding interventions, but that defeats the goal of democratisation, an essential objective of low-code platforms.
This is the sweet spot where low-code and GenAI will coexist and push the boundaries in delivering a simpler and faster app-building experience. Let’s go back to our example above. The user will now be able to open the low-code platform and type in a simple text-based prompt to define their need. The system will then dip into the code base and suggest the starting point and recommend data models, connections, and everything else that a non-developer might not even think of in normal circumstances. Furthermore, the platform can go on to auto-generate the application shell for the developer to customise and launch.
Similarly, GenAI within low-code platforms can enable developers of different personas from business users to techno-functional consultants and pro-coders to prompt their business requirements as an application or as individual components. Users can also leverage prompt engineering and create logical blocks of codes in a specific language and push it to a low-code platform. The internal guardrails of the platform can then validate the code based on the overall context and quality to create a win-win scenario.
The catch here is choosing the right low-code/no-code platform, which has an in-depth integration with GenAI and strong LLM capabilities. A loosely-coupled low-code/AI approach might lead to problems like poorly built applications, compliance issues, and technical debt.
Things to keep in mind while using low-code platforms with AI capabilities
Regardless of whether the low-code platform provides GenAI capabilities via an external LLM integration or an in-house LLM, it’s important to vet the offering against the following factors.
Platform maturity: Be careful not to opt for a low-code platform solely for its AI capabilities because the tech itself is still fairly wet behind the ears. Instead, focus on choosing a mature low-code platform with a good breadth and depth of features to support different personas to build scalable custom applications.
Privacy and security: Apps built on low-code platforms typically interact with all kinds of data residing within and outside an organisation, depending on the use case. Questions regarding how the data will be used in the context of LLM should be addressed upfront. A platform that has prioritised data privacy and security at its core will focus on extending the same for all of its provisions beyond LLM capabilities.
Compliance: Non-compliance is probably the fastest way to bankruptcy. On the one hand, heavy fines are a reality. On the other, it can (and will) drive away customers and prospects, making survival difficult. It’s critical to choose platform vendors that comply with all major regulations from the regions they function in. For example, the platform should actively track the application progress and flag for GDPR non-compliance if the application is designed to be consumed in the EU.
Governance: With different developer personas leveraging the platform to solve real-time business problems, having adequate governance measures at the platform level is important. Adding LLM-based capabilities to the mix further compounds the governance issue because users will inevitably bring in foreign code blocks to build on top of the existing code base.
Approach for adoption
In the near future, we will see all long-term low-code platforms that are worth their salt train and launch their proprietary LLMs to have better control over the outputs. In addition, we will see contextual domain-centric LLMs deployed with low-code platforms to build industry/use-case-specific applications at scale.
As with the adoption of any new technology, jumping in head-first because of the hype or for the sake of it is not advisable. Instead, a conscious effort towards starting small, evaluating progress, and scaling should be the approach.