Measuring AI productivity: Why your numbers are flat and rework is up

AI is in your engineering workflow. The token spend shows it, but the throughput doesn't. Most software teams can't prove whether AI is actually helping, because the metrics they watch reward motion over delivery.

This is a context problem. The gains disappear between the feature branch and main because your agent doesn't fully understand how your system works. It pushes code that breaks in production, and you spend the next sprint dealing with the thrash instead of working the review backlog or shipping the next feature.

In this session, we cover how to actually track AI adoption in your organization, how context maturity affects where teams get stuck, and what it takes to make the most of your agents.

You'll leave with:

• A way to measure the gap - The four metrics that don't lie (and the two rules that keep them honest), so you can see exactly where AI's gains leak out before production.
• Characteristics of the AI maturity stages - Which of the 8 stages of context maturity your team is at, and the specific wall that's capping your metrics right now.
• The way to uplevel - Why more tools, connectors, and context window won't move you to the next level, and what actually does.

For: Engineering leaders, senior and staff engineers, and platform teams who want a straight, measurable answer on whether their AI tools are working.

Includes a free assessment to find your team's level on the AI maturity curve.