By Robby Putzeys, Global Head of Software Testing Practice

AI-generated code is no longer a distant concept — it’s a reality that’s reshaping the software development landscape. Businesses that embrace this emerging technology can unlock faster, more efficient coding, but they also need to carefully manage the significant risks it introduces.

During a recent webinar, Robby Putzeys, our Global Head of Software Testing Practice, shared his insights on how organisations can safely and effectively navigate this transformative era.

Through audience polls and a deep analysis of the industry, Robby explored the rapid adoption of AI-generated code, the associated risks and the practices organisations must implement to strike a balance between innovation and security.

How AI adoption is transforming businesses today

The audience poll about AI-generated code adoption was one of the most revealing aspects of the webinar, with the results revealing a fascinating picture of where the industry stands today. Nearly half of the participants (45%) reported using AI-generated code in test environments, while a significant 25% had already advanced to production use. Another 25% indicated they were considering adoption within the next year, while only 5% said AI-generated code wasn’t even on their radar.

These figures underscore the fact that AI-generated code is no longer experimental — it’s fast becoming an integral part of the software development toolkit. Despite this rapid progress, the trend also raises critical questions about the risks and challenges these businesses face when integrating AI into their workflows.

When participants were asked to rank the types of risks they saw as the most pressing, security vulnerabilities in code emerged as the top concern. Copyright issues and functional risks followed closely behind, with performance and maintenance challenges rounding out the list. These concerns highlight the complexity of AI adoption and the need for a structured approach to mitigate potential pitfalls.

The opportunities and challenges of AI-generated code

According to Robby, this shift toward AI-driven development has profound implications for the software industry. On the one hand, AI-generated code is accelerating development cycles and allowing teams to deliver new features and products faster than ever before.

By automating repetitive coding tasks, it frees developers to focus on high-level activities like architecture design and problem-solving. This productivity boost lowers entry barriers to software development, enabling a broader range of individuals and organisations to participate in building innovative solutions.

However, on the other hand, this promising future is not without its challenges. Security vulnerabilities are a significant threat, with AI-generated code sometimes introducing flaws that human developers might overlook. Functional risks such as compromised end-to-end functionality and performance issues tied to non-optimised code can derail projects if left unchecked.

Additionally, maintenance challenges arise when development teams struggle to understand or adapt the code AI systems produce. Together, these risks emphasise the importance of human oversight and robust testing frameworks to ensure AI-generated code meets the highest quality standards.

Preparing for the future: evolving practices for AI adoption

To navigate the complexities of AI adoption, Robby stressed the importance of evolving both technical and business practices. From a technical standpoint, organisations must prioritise human oversight. While AI tools can generate code at an unprecedented speed, human engineers are essential to review and validate the output to ensure it’s correct, secure and adheres to industry standards. Without this layer of scrutiny, there’s an increased risk of compromised systems and vulnerabilities.

Testing and quality assurance practices also need to adapt. Traditional testing approaches are insufficient for the unique challenges posed by AI-generated code. Rigorous strategies, such as security testing, integration testing and system testing must be implemented to identify and resolve potential issues early in the development process. Continuous learning is also key, and teams should stay informed about the latest AI advancements to maximise their effectiveness.

On the business side, companies must consider the broader implications of AI adoption. Intellectual property rights for AI-generated code remain a grey area, and organisations should work closely with legal experts to ensure compliance with copyright laws. Developing risk management strategies is also vital to address security vulnerabilities, functional issues and potential liability concerns. At the same time, investing in talent is critical – companies need to upskill their existing workforce to work with AI tools effectively and attract professionals with expertise in AI and machine learning.

A new era of software development

Robby concluded the webinar with a clear message: AI-generated code is ushering in a new era of software development. To fully harness its potential, organisations must review and update their practices so they’re equipped to manage the challenges and opportunities this technology brings.

This moment is a pivotal one for businesses. By carefully balancing the benefits of AI with the risks, companies can drive innovation, improve efficiency and maintain a competitive edge. The future of software development is here and those who adapt now will be best positioned to thrive long into the future.

If you’re ready to explore how AI can transform your development processes, don’t hesitate to reach out to Resillion. Learn more about the transformative potential of AI-generated code by accessing our AI whitepaper or watching the webinar replay.