Ryan McCue on Ryan McCue - AI Delivery Systems & Engineering Leadership

The engineer says they need to get better at AI.

The PR lands at 3:42.

Generated code is not the hard part anymore.

AI dashboards are getting better.

Every serious AI coding workflow eventually teaches you two things about the little approve button.

AI does not make software delivery simple.

Most companies are still treating AI adoption like a tooling rollout.

AI did not just change how code gets written.

A pull request used to carry its own credibility.

If you ask for weekly proof that your AI transformation is working, you are setting your team up to manufacture theater.

I used to write terrible tickets.

I wrote recently about the 5 stages of AI tooling adoption that engineering teams go through, from curiosity to orchestrated autonomy.

When vendors start inventing new engineering management frameworks, it’s usually because the old assumptions stopped fitting reality.

A few months ago, I watched Claude Code implement a feature I’d been planning to build that afternoon.

Ninety-five percent of developers now use AI tools at least weekly, with seventy percent juggling two to four tools at once.

Somewhere in the last two years, the industry settled on a mental model for AI coding tools: “It’s like having a junior developer.

I wrote recently about the 5 stages of AI tooling adoption that engineering teams go through.

Every engineering team I talk to tells me they’re “using AI.”

Over 91% of developers now use AI coding tools.

Invited by the University of Victoria’s Digital Scholarship Commons, I presented a live, hands-on session exploring how senior developers actually use agentic AI coding tools in their day-to-day workflow.

The Problem Voice dictation tools either produce garbage transcripts or lock you into a single platform.

The Problem AI coding assistants are powerful, but deploying them at scale is a mess.

Overview A personal AI operations platform that bridges conversational interfaces with development tooling.