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Research Paper | Information Technology | Volume 15 Issue 6, June 2026 | Pages: 405 - 408 | India
AI Engineering Effectiveness: A Predictive Framework for Measuring AI-Assisted Software Delivery Velocity
Abstract: This paper presents the AI Engineering Effectiveness Framework (AEEF), a structured model for assessing and predicting AI-assisted software delivery performance. The framework argues that delivery velocity is influenced by multiple interacting dimensions rather than AI capability alone. Seven factor groups are incorporated: Human, Technical, Process, Organizational, AI, Domain, and Environmental factors. The model uses weighted scoring to estimate delivery effectiveness during early adoption stages and evolves toward regression-based prediction as historical data become available. The framework provides a practical mechanism for identifying delivery bottlenecks, comparing teams, and improving decision-making through explainable metrics. The approach supports evidence-based AI adoption and offers a pathway from anecdotal productivity claims to measurable delivery outcomes.
Keywords: AI engineering effectiveness, software delivery velocity, AI-assisted development, engineering productivity, software engineering metrics
How to Cite?: Kunal Kumar, "AI Engineering Effectiveness: A Predictive Framework for Measuring AI-Assisted Software Delivery Velocity", Volume 15 Issue 6, June 2026, International Journal of Science and Research (IJSR), Pages: 405-408, https://www.ijsr.net/getabstract.php?paperid=SR26607160505, DOI: https://dx.doi.org/10.21275/SR26607160505