Requirements Engineering: The Key to Crossing the AI Productivity Divide
Wavemaker's Praveen Kumar Cherukuri argues that AI coding tools amplify existing processes, making clear specifications the primary driver of success.
The success of AI-driven software development depends less on the tools themselves and more on the quality of the requirements engineering preceding them. As generative AI and agentic coding tools become mainstream, a productivity divide has emerged between teams achieving exponential gains and those struggling with unmaintainable code.
Writing in Communications of the ACM, Praveen Kumar Cherukuri, head of professional services at Wavemaker, argues that clear specifications are strongly correlated with the successful deployment of AI coding tools. Cherukuri posits that AI acts as an amplifier for existing organizational processes. When paired with rigorous requirements engineering, AI delivers exponential speed; however, when applied to poor specifications, it merely accelerates the accumulation of technical debt.
The Process Gap
This shift comes at a time when many agile environments have streamlined or bypassed the 'front end' of software development. The resulting gap in requirements engineering has created a scenario where AI can generate vast amounts of code that may be buggy or misaligned with business needs. According to Cherukuri, the competitive advantage in the current era will not belong to the companies that generate the most code, but to those that establish the best processes, including measurement frameworks, architectural literacy, and strict specifications.
Shifting the Engineer's Value
The implications for the industry are significant, as the primary value proposition of the software engineer is shifting from the act of writing code to the act of defining requirements. If AI can handle the bulk of the implementation, the critical bottleneck in the development pipeline becomes the ability to precisely describe what needs to be built. This transition suggests that the role of the developer must evolve toward a focus on formal requirements gathering to prevent systemic technical debt.
The Path Forward
To bridge the productivity divide, corporate workflows and developer education may need to pivot back toward the fundamentals of requirements engineering. The focus is moving toward building guardrails that ensure AI output is grounded in verified business logic. While the tools continue to evolve, the industry must now determine how to standardize these specifications to ensure that AI-generated code remains scalable and maintainable in the long term.