Measuring Acceleration in GenAI

Stephen Barrett

Co-Founder & CTO
Milestone

Abstract

AI-assisted and agentic development models are changing the structure of engineering work. As code generation, refactoring, and cross-system orchestration increasingly shift from direct human authorship to AI-mediated workflows, traditional productivity metrics lose interpretive reliability. Throughput can rise while review depth thins, rework concentrates, and governance capacity drifts out of alignment with delivery velocity. Adoption metrics, meanwhile, signal tool usage rather than value creation.

This paper examines the emerging assessment problem facing engineering leadership: how to measure performance when output-centric proxies no longer map cleanly to understanding, stability, or risk. Drawing on the investigative governance framework introduced in Managing Software Quality and Risk in the Era of AI-Assisted Development, we argue that engineering performance must be reframed as controlled acceleration: the ability to increase delivery velocity without degrading stability or governance integrity.

We introduce the Engineer Automation Leverage Index (EALI), a composite framework that formalizes leverage across three dimensions: delivery acceleration, stability under change, and risk discipline across control surfaces. Grounded in observable repository and workflow telemetry and requiring no prompt-level surveillance, EALI serves as a north star index for agentic engineering; restoring interpretability to performance assessment as development shifts from human-centric implementation to spec-led and AI-mediated orchestration.