The adoption of low-code no-code (LCNC) platforms has gained momentum in recent years, enabling companies to develop applications without the need for extensive internal coding.
It’s a rapidly evolving landscape with the advent of Generative Artificial Intelligence (GenAI). LCNC uses intuitive visual interfaces, pre-designed templates, and drag-and-drop capabilities to dramatically accelerate the application development lifecycle. This has a major impact on organizations streamlining their processes, reducing costs and closing the software skills gap.
Adoption has been such that Gartner predicts that up to 70% of new applications will come from these tools and platforms by 2025. Forrester estimates this market at $13.2 billion, with strong annual growth of 21% from 2019. Adoption is widespread, with why 87% of enterprise developers now use such tools or platforms.
GenAI will accelerate this trend, bringing the market value to $50 billion in the next four years.
While GenAI is reshaping the software landscape by enabling systems to evolve and adapt autonomously, LCNC platforms and Large Language Model (LLM) code generators/co-drivers are democratizing software development and driving digital transformation at an ever-faster pace. The adoption of GenAI is also driving major changes in LCNC platforms.
GenAI generates content, including code, when requested. It is also used for documentation, test case generation, test automation, code optimization and refactoring. For software developers, it offers new features and tools to improve workflow and offers a new way to develop quality software.
With a conversational interface, GenAI enables developers to tap into vast amounts of knowledge, easily access code snippets, and think more collaboratively, enabling code development in days instead of weeks. Generation AI shines in quality assurance and verification processes. By enabling code review and early detection of problems, it improves code quality and accelerates test cycles. Automation features further reduce testing time, while anomaly detection capabilities help identify hidden defects and potential problems early in the development process.
Risks and challenges
While both systems have advantages, they also carry risks that require compliance with relevant laws and policies.
Ethics, mitigating algorithm bias, and data privacy are paramount here. The rise of automation and interconnectedness also leads to increased cyber risks, which is why safeguards are crucial.
Organizations must also comply with regulations, such as the General Data Protection Regulation and the California Consumer Privacy Act, to mitigate legal risks associated with data processing and the use of artificial intelligence, as well as the increasing regulatory requirements surrounding GenAI.
Risk mitigation
Businesses must clearly understand their “data protection obligations” and address data protection “by design and by default”.
One solution is to limit direct access to the application programming interface (API) and replace it with a front-end internally hosted service to clean up personal data, report misuse and abuse, and enable internal auditing. Here, users will call an internal API that validates the request. and transfers it to the LLM. The front-end API should be monitored and users authenticated via single sign-on or authentication tools.
Another option is to host your own (open source) LLM or GenAI solution to prevent introducing vulnerabilities and help models adapt to organizational data. High performance open source models such as Llama, Mistral and DBRX can be trained (quantized) cheaply. prices and offer greater transparency, control and flexibility. Future small open source models can be trained on a single graphics processing unit (GPU). Locally hosted open source models will significantly reduce the risk of leaks, including personal data.
Industry trends
Industries are leveraging GenAI for rapid experimentation and innovation—with human oversight and expertise—for both open source LLMs and proprietary models.
Low cost, flexibility and transparency make open source attractive to developers looking to improve niche application models beyond coding to content development and knowledge management to improve efficiency while reducing risk. Feedback from the pilot shows that GenAI has been instrumental in simplifying code generation, debugging and knowledge management. ensure code consistency. GenAI can revolutionize software development and, when integrated with LCNC tools, can create tangible benefits.
Conclusion
The convergence of GenAI and LCNC heralds unprecedented opportunities for innovation and efficiency. They enable ordinary users to become app creators. As software development becomes more accessible, people from different backgrounds, regardless of technical expertise, can create their own digital solutions and tools.
Zak Mourad
Zak Murad is Director of Technology and Information at CRISIL Ltd.