How Engineering is Changing the Way
We are Using AI

Stephen Barrett

Co-Founder & CTO
Milestone

Abstract

Artificial intelligence is rapidly reshaping how software is built. Across the industry, engineers are adopting AI to write code faster, explore solutions more broadly, and reduce friction in everyday development work.

For CTOs and engineering leaders, this presents a dual challenge: accelerating adoption to remain competitive, while ensuring that the speed and scale introduced by AI do not undermine engineering safety, quality, or organizational control.

In large-scale, mission-critical engineering environments, the risks are not hypothetical. AI changes the shape of engineering work: how decisions are made, how errors propagate, how reviews function, and how responsibility is distributed across teams and tools. While AI systems continue to improve in capability, the industry still lacks a clear, operational safety model for their use in production engineering systems. As a result, leadership is left to manage unfamiliar failure modes using governance structures designed for a pre-AI world.

This talk explores how engineering is changing under AI, not at the level of individual productivity gains, but at the level that matters most to enterprises: system reliability, quality assurance, and organizational risk. We examine emerging patterns of AI usage across teams, the new forms of engineering risk they introduce, and why traditional qualitative oversight is no longer sufficient at scale.

The core argument is that software engineering analytics are becoming a critical control surface for AI-augmented engineering. By making engineering activity observable, measurable, and explainable, analytics provide management with the clarity needed to guide adoption safely, detect early warning signals, and make informed trade-offs between speed and assurance. In an era where AI accelerates both progress and risk, visibility is no longer optional—it is foundational.