Log In
GET A DEMO

What We Researched

Three core questions about AI measurement in engineering organizations:

np_two-arrow_7262308_3B5B76

Are leaders measuring the right AI outcomes?

The gap between systems thinking for engineering effectiveness and individual metrics for AI impact

np_lightning_4730659_3B5B76

Where do AI priorities conflict?

How velocity pressure from leadership clashes with engineering concerns about security, quality, and technical debt

np_risk_6908842_3B5B76

How do AI measurement gaps create risk?

Where do misaligned metrics undermine AI initiatives and create long-term organizational dysfunction?

Key Insights

key-insight-1

SYSTEMS THINKING VS. INDIVIDUAL METRICS

63% of leaders apply systems thinking to engineering effectiveness, but most revert to individual metrics for AI.

Engineering leaders identify cross-team coordination and technical debt as delivery constraints, then measure AI success through developer productivity rather than organizational flow.

BUSINESS ASPIRATIONS VS. MEASUREMENT

50% of leaders want business outcomes from AI but don't measure business impact for engineering performance.

Half aspire to business value from AI. Only 3% use business metrics for engineering evaluation. This measurement disconnect creates incentives for technical outputs over value delivery.

key-insight-2
key-insight-3

LACK OF AI STRATEGY AS TOP RISK 

22% of leaders identify missing AI strategy as their biggest organizational risk while 88% simultaneously rate themselves as highly prepared for implementation. 

This strategic vacuum forces engineering teams to measure AI impact using familiar individual metrics rather than developing approaches that align with business goals.