Turbocharging development with generative AI and low-code

In today’s rapidly evolving technological landscape, businesses are actively seeking innovative solutions to increase efficiency and streamline the application development process. One breakthrough that holds immense potential is the integration of AI-based code generation with low-code and no-code platforms.

AI-based code generation is the talk of the town, bridging more development gaps than ever before thanks to advanced auto-completion and AI coding assistants like ChatGPT.

While low-code technology already plays a vital role in helping businesses build their software applications, its integration with generative AI leads the way to a new class of software development tools.

The next step for enterprise application development is to merge the power of generative AI with existing low-code and no-code tools. This can revolutionise software development by further lowering barriers for enterprises to easily develop applications. Combining the power of low-code/no-code with generative AI empowers businesses to create sophisticated applications without requiring extensive coding experience.

Supercharging the use of low-code systems to speed up development cycles

Companies are under pressure to adapt to rapidly shifting external environmental factors such as evolving market trends, regulatory changes and technological advancements. At the same time, they must also cater to growing employee expectations, as staff have become accustomed to the seamless experiences offered by the latest consumer apps they use on a daily basis.

However, certain barriers can hinder companies from building custom models or applications using traditional legacy software — namely high development and maintenance costs, a lack of internal expertise, evolving technologies and integration challenges. By leveraging generative AI and low- or no-code platforms, businesses can overcome these barriers and launch new digital products at the speed of business.

With generative AI, businesses can improve the functionality and intelligence of applications built using low-code tools. Generative AI can be used to prompt best practices and automate repetitive tasks, helping boost developer productivity. Integrating generative AI with intuitive and user-friendly low-code/no-code features such as drag-and-drop applications helps to streamline the app creation process, enhancing output quality and efficiency for developers. This ultimately speeds up the overall development cycle as a whole.

Assisting developers in the app creation process

There is currently a severe global shortage in skilled software developers due to businesses’ increasing reliance on technology and digital transformation. A recent Australian industry report identified tech job vacancy rates 60% higher than the national average, and predicted Australia will need to employ an additional 653,000 technology workers to meet the growing demand.

This shortage of software developer talent has had a significant impact hindering companies’ abilities to compete and innovate in the digital space. However, there is a potential solution to address this issue by integrating generative AI into low-code and no-code platforms.

By lowering barriers to adoption across enterprises, non-technical individuals can utilise this generative AI with low-code tools to build applications by expressing their requirements in everyday language. This eliminates the need to code, reducing the learning curve associated with application development.

Professional developers can also benefit from this integration as they can create custom AI solutions tailored to specific business needs. They can also easily integrate AI component libraries into applications, such as chatbots and predictive analytics. This proves particularly advantageous for developers working on larger projects as it offers valuable shortcuts. This advancement in app development is expected to provide significant benefits for businesses.

Considerations of using generative AI

While integrating generative AI with low-code and no-code systems offers significant advantages for businesses, it also comes with its share of considerations. While using natural language prompts offers a more intuitive and conversational way to interact, it takes practice to achieve desired outcomes. Drag-and-drop methods in low-code and no-code environments may be more suitable for users with less expertise. The integration of AI is particularly beneficial for complex tasks that require detailed instructions or contextual understanding.

Additionally, generative AI may give rise to legal and security issues as models such as GPT may unknowingly infringe upon data lineage and intellectual property rights. This is because some GPTs are not aware of copyrighted or patented processes as they autocomplete responses, necessitating careful code checking to avoid any legal repercussions. This limitation may hinder the potential of platforms to reduce development time.

Another crucial aspect to consider is data security. To take full advantage of generative AI capabilities, businesses would need to train systems using their own in-house datasets. However, this raises concerns as platforms may require access to enterprise datasets, potentially leading to privacy and security risks. One recent incident involved Samsung employees who inadvertently shared confidential data with OpenAI’s ChatGPT while trying to resolve a code error related to semiconductor equipment measurement. This shared data included sensitive software source code and excerpts from confidential corporate meetings. The repercussions of this incident serve as a reminder to business leaders of the critical need to safeguard sensitive information and mitigate data breaches. These concerns are further underscored by Apple’s decision to restrict ChatGPT use by its employees due to fears of potential data leaks.

Although Australia has no specific laws regulating AI at this time, governments and regulatory bodies are considering regulations on the use of artificial intelligence applications. The Italian Data Protection Authority has already imposed a temporary ban on the use of ChatGPT and initiated an investigation into its compliance with the EU’s General Data Protection Regulation (GDPR) due to privacy concerns stemming from data breaches, which exposed addresses and credit card numbers. In response to these growing concerns, the UK is set to host the first global summit to review the use of AI in relation to data protection and safety in October this year. The summit will bring together policymakers, experts and industry leaders to address these issues and develop guidelines and regulations to ensure responsible and secure AI usage.

To address data security concerns, enterprises may need to implement more sophisticated solutions. Open-source and commercially available solutions like Databricks’ Dolly 2.0 are being developed to allow organisations to build their own AI language models in-house. This approach helps protect a company’s intellectual property without the need for API access or sharing data with third parties. Additionally, employee awareness programs focused on data privacy and the potential risks associated with sharing confidential information are paramount. By adopting a proactive approach to data privacy and security, business owners can strike a balance between technological advancements and the protection of valuable information. This allows them to effectively navigate the intricate landscape of an increasingly data-driven world.

Overall, the integration of generative AI into low-code and no-code platforms empowers both professional and non-technical developers, leading to faster and more customisable application development. Addressing data security concerns and implementing appropriate safeguards is essential for businesses to successfully integrate generative AI into their low-code and no-code platforms while maintaining the privacy and security of valuable data assets.

Image credit: iStock.com/Natalia Darmoroz