The ROI of AI in Engineering: Measure Value Beyond Vanity Me
Blog post from Harness
The text explores the challenges and potential benefits of integrating AI into engineering systems, emphasizing that while AI can accelerate code production, it often creates bottlenecks in delivery due to insufficiently adapted processes, pipelines, and governance frameworks. It highlights the common mistake of measuring AI's success solely by increased code output rather than actual system outcomes that deliver customer value efficiently and safely. The document outlines a three-layer AI ROI measurement model that includes utilization, impact, and cost, urging organizations to focus on system-wide improvements rather than individual productivity gains. Moreover, the importance of establishing robust governance and standardized pipelines is stressed to ensure that AI-enhanced development leads to faster, safer, and cost-effective delivery. The text also provides insights into measuring AI ROI through metrics that reflect delivery speed, quality, cost efficiency, and system resilience, advocating for a shift from vanity metrics to those that truly indicate business value.