Evaluating the risks of AI coding assistance
Despite strong adoption and business benefits, some leaders highlight the risks of AI code assistance. Organizations adopting AI for devops and software development should define non-negotiables, train teams on safe utilization, identify practices to validate the quality of AI results, and capture metrics that reveal AI-delivered business value.
“AI poses risks to code quality and security that can’t be ignored, making code reviews and analysis still a critical part of the development process,” says Andrea Malagodi, CIO of Sonar. “Without proper checks and reviews, AI-generated code may lead to poor software quality and increased tech debt. To maximize AI’s productivity benefits, developers must have accountability for code quality and adopt a ‘trust and verify’ approach, ensuring all code—AI-generated or human-written—meets quality and security requirements, and the user experience is not disrupted.”
Bogdan Raduta, head of AI at FlowX.AI, raises questions about quality and innovation when businesses rely too heavily on generic user experiences and AI defaults to patterns and conventions. “While faster development reduces costs, businesses may deliver functional but uninspired products, opening opportunities for competitors to stand out with bespoke, human-driven designs,” Raduta says.