Auto-tech series: Zoho - practical uses of automation in low-code

This is a guest post for the Computer Weekly Developer Network written by Tejas Gadhia of Zoho.

Based in Austin, Texas, Gadhia has held various roles over the yeats spanning sales engineering, strategic partnerships, developer relations and product management. 

Currently, he is the lead evangelist for Zoho’s Developer Platform, made up of a variety of tools designed to allow users of all skill levels to build applications, integrations and analytics while using Zoho’s deep tech stack.

Gadhia writes in full as follows…

Over the past decade, the significance of IT in an organisation’s growth strategy has vastly expanded as [the technology department has] transformed from being a ‘mere’ support and service function to becoming a critical enabler that fosters innovative business models, diverse revenue streams and organisational efficiencies.

However, there is a massive mismatch between the supply and demand for quality skilled developers worldwide, resulting in a significant rise in hiring and retention costs for IT teams. Maximising the productivity of the current developer force has become a top priority for CIOs – and automation will play a key role.

Developers perform a mix of operational and strategic tasks on a daily basis. To improve developer productivity, it’s important to find ways to automate and optimise this mix. To achieve this, organisations are heavily investing in tools and technologies like low-code development platforms.

The evolution of low-code

Low-code platforms were originally designed to democratise software development i.e. to help non-developers build simple solutions for simple use cases quickly. But over time, it has extended much beyond simple use cases. Currently, most mature low-code platforms are capable of providing efficiencies across both operational and strategic IT mandates.

On the development side, these platforms offer significant advantages with pre-built components and rapid application development capabilities in critical areas such as user experience, integrations, analytics and maintenance. This allows IT teams to collaborate effectively and build high-quality contextual business solutions that have a direct impact on how the business performs.

The next phase: AI low-code

AI and ML advancements in combination with low-code application development are set to fuel the next wave of software development automation. AI-led low-code platforms are already making application development more accessible to individuals with varying developer skill sets by simplifying the development process and reducing the need for specialised technical expertise. Tools such as ChatGPT can fully build code snippets and websites for individuals with minimal coding knowledge, demonstrating the accessibility and automation capabilities of AI-led low-code platforms.

We anticipate that AI’s scope and impact on the low-code spectrum will continue to expand across different aspects of application development, including enhancing developer experience, application reliability and security and self-training platforms.

AI/ML capabilities are and will keep having a huge impact on curating developer experiences across different developer personas. Some of the key capabilities around these are:

  • Intelligent code suggestions – based on the context of the application and the developer’s past behaviour. This includes suggesting code snippets, highlighting potential errors and recommending code changes, enabling developers to write code more efficiently with fewer errors.
  • Intelligent workflows – that guide developers through the development process and help them complete tasks more efficiently. For example, automating the process of building user interfaces.
  • Natural Language Processing, which enables developers to interact with the low-code platform using conversational language. This allows developers to ask questions or give commands in plain English, which are then understood and executed by the low-code platform.
  • Predictive analytics at the builder level enables developers to anticipate and address potential issues before they occur. For example, offering UX recommendations based on user behaviour during the design phase.

Application reliability & security

AI can also play a big role in providing a consistent experience for developers of low-code platforms and their end-users.

These can be categorised across:

  • Automated quality assurance by automating functional, performance and regression testing. This saves time and ensures that the application is thoroughly tested before it’s deployed, reducing the risk of bugs and errors in production.
  • Real-time detection of errors, security vulnerabilities and performance issues enables developers to quickly address any issues before they escalate into more significant problems.
  • Automated security testing by continuously scanning the code for vulnerabilities and providing recommendations for fixing them. This helps in identifying potential security issues and addressing them proactively, making the application more secure.

Zoho’s Tejas Gadhia: Let’s automate functional, performance & regression testing for better lives.

Low-code platforms can leverage AI to automate user behaviour analysis. Data insights from this can feed into a system that proactively sets up guardrails and guidelines based on how different developer personas interact with the platform. In addition, platforms can also suggest optimal workflows and data models for developers on why they should design their apps a certain way to ultimately reduce professional developer interventions even during citizen developer-led initiatives.

We live in interesting times

We are entering interesting times. Massive macroeconomic pressures are pushing initiatives that drive frugal innovation to the forefront and AI-led low-code development will play a critical role in it.

However, the big caution is for people not to become over-reliant on a solution on a plate. Understanding the foundations of application development, like data models and workflow logic, is important in building high-performance, scalable applications.

Organisations building this into their operational DNA will thrive and excel over the next decade.

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