The deeper Thinking.

If your team hits 100% of its OKRs every quarter, that's not the win it looks like. This post walks through the score you should actually be aiming for, what each band from 0.9 down to 0.4 tells you about the goal, not the team, and what to ask when a key result comes in low.

Your OKRs are already tied to pay and teams play it safe. A practical path to decouple OKRs from compensation and rebuild the trust to stretch again.

Link OKRs to pay and teams stop stretching. Here is why tying OKRs to compensation quietly kills ambition, and what to do instead.

Blocking AI-enabled citizen development backfires. Learn the blast radius framework for governing it by risk, not blanket restriction.

AI coding agents architecture drift happens because agents can't ask for missing context. Here's why it occurs and three ways to stop it.

AI tools give inconsistent output when context layers go stale. Here is the layer-aware checklist for keeping context current across your team.

AI agents don't flag stale context, they just use it. Here's why the slow layer of your context (architecture, boundaries, conventions) is the real risk, and what a second control loop looks like.

Most teams are still tweaking prompts. The ones getting consistent results from AI have moved on to something more fundamental - engineering the context around every request, not just the request itself.

AI agents don't browse wikis or ask colleagues — they use the context you give them. Here's why context management is now a core delivery discipline.

Most SDLCs weren't built for AI-generated code. Here are the 4 structural shifts engineering leaders need to make - and the order to make them in.

Why isn't AI improving software delivery? Velocity is up, but the bottlenecks remain. Here's what the 21% who get real returns do differently.

Every control in your SDLC has a story. AI generates code that carries none of that history — and it generates it faster than your team can review it.