Discussions around the potential impacts of AI and Generative AI have boomed in the last few years. Outside of chatbots and GPT, the technology promises to make software development processes more efficient for organisations. Theoretically, this means drastically reducing the time between ideation and commercialisation.

Startups, with their agility and innovative spirit, are particularly well-positioned to leverage AI-driven tools, fundamentally altering their coding processes and accelerating their paths to market. By leveraging AI-powered tools, startups can rapidly prototype, deploy, and scale.

Against pressures like advancing digitalisation and a tech skill shortage, AI-assisted coding's promise and potential are grand. However, startups should carefully consider their adoption of AI-assisted coding to pair with the right secure tools to maintain agility and continue innovation.

The role of AI and Generative AI in modern coding

Traditionally, developing software from ideation to finding product market fit takes months in the best-case scenario and years in the realistic scenario. A successful application goes through a rigorous process of planning and design before teams begin to code, test, and debug.

After deployment, constant maintenance is still required to ensure that the software runs smoothly, and updates and performance are meeting growing demands. The reality is technical experts handle most of these processes, creating a backlog and inefficiencies as organisations try to keep up with demand.

However, the industry is changing. AI, and more specifically, Generative AI, has the potential to streamline the coding process, enabling developers to write and refine code faster and with fewer errors. These technologies can automate repetitive tasks, suggest code improvements, and even generate new code snippets, significantly reducing the time and effort required to develop software.

Rapid developments in AI mean that certain tasks like documenting code functionality for maintainability and writing new code, according to McKinsey, may only take half the time. In the near future, Gartner predicts AI-assisted coding can improve human coder productivity by 10 times. For startups deploying AI-assisted coding, this means faster development cycles and less time spend on maintenance. However, this must be balanced with a thought-through approach to coding that can help startups stay agile in an increasingly complex market without compromising on security.

Pairing the power of low-code with AI

Each time ChatGPT releases a new update, you may see opinions that this version will kill traditional development and allow everyone to be a developer.

In reality, AI-assisted coding does not replace the need for technical skills. While the use of AI during coding has great potential to improve speed, the sporadic inaccuracies from AI hallucinations require technical hand-holding to maintain the quality of the code and avoid vulnerabilities.

Publicly available AI models are trained on publicly available codebases – and a significant part of them, by nature, have errors. In other words, the quality of data that has been fed to the generative models, and the resulting models themselves, are far from perfect. Therefore, to innovate with speed and stay agile, organisations need to pair AI adoption with solutions that ensure solid guardrails and governance to protect the resulting code from vulnerabilities and errors. It is key that people from the business can interact with AI generated software in order to determine if it is what they really need from a business or product point of view.

Moreover, putting AI aside, the prevailing tech talent shortage means IT teams cannot cope with growing tasks in an increasingly complex landscape. This means that outside of using AI to improve coding efficiency, organisations need to find ways to upskill through technologies and involve wider teams in the ideation and creation of new technologies.

For both of these pains, low-code emerges as a critical remedy allowing any organisation to develop secure and robust applications by democratising software development. In practice, this creates cross-functional fusion teams, and non-technical team members are able to collaborate with technical talents throughout the innovation and software development processes without deep technical knowledge.

From a development perspective, this also helps increase the quality of software developed as future users are involved from the beginning. Additionally, this breaks down silos within the traditional software development processes and facilitates the exchange of knowledge and feedback, which streamlines development and eliminates bottlenecks.

Low-code, enhanced by AI, is a great solution as it helps leaders foster collaboration across technical and non-technical members. It alleviates strain on IT teams and also fosters an environment that encourages innovation at speed and scale, which are in high demand for any startup.

Impact on innovation cycles and competitive advantage 

The integration of AI with low-code platforms improves innovation cycles by allowing for rapid prototyping, testing, and scaling, all of which are essential for startup success. By decreasing the time and expense of development, companies can concentrate on creativity and strategic planning, promoting a culture of constant innovation.

Combining low-code and AI levels the playing field, allowing startups to compete with other bigger and more mature organisations by leveraging their agility and empowering teams to quickly adapt to market changes and customer needs.

Enterprise-grade low-code platforms with embedded AI assistance can streamline complex development tasks by automating routine processes, providing real-time insights, and generating code suggestions. This efficiency enables startups to explore new business models without the traditional overhead of extensive manual coding.

Using AI in coding marks a significant evolution in the tech landscape, especially for startups. It can catalyse innovation, enabling faster, more efficient, and inclusive software development. Still, organisations must understand its limitations and approach AI-augmented coding with governance and security in mind. For many startups in their quest for rapid growth, this balanced approach will be a defining element for success.