Enterprise AI Measurement Guide
Reliability
Highest by Department (Pages)
Which engineers are receiving the most incident pages, and how does that rank compare across departments?
What it shows
The Highest by Department (Pages) view ranks engineers by the number of incident pages they received in the selected period, surfacing the most on-call-burdened individuals across the org and within each department. It answers the distribution question that aggregate reliability metrics cannot: is incident load spread across the team, or concentrated in a small number of engineers?
Why it matters
Page concentration is both a retention risk and a governance signal. An engineer receiving 3x the org-average number of pages is almost certainly the engineer who owns the most critical or most fragile systems, and is also the engineer most likely to leave if that burden isn't addressed. In AI-assisted engineering environments, the engineers who own the most complex systems are often also the heaviest AI tool users, because they're the ones with the most to gain from AI coding assistance. When high AI usage and high on-call burden stack onto the same people, the org is concentrating both its highest-value output and its highest operational risk in a small population.
The Larridin angle
Larridin cross-references page concentration with the AI spend and output data in the same platform, enabling the specific finding that the most-paged engineer is also the highest AI spender or the highest CAV performer. That cross-signal connection converts a page ranking from a reliability observation into an actionable workload balance conversation.
Related Reliability Metrics
Common questions
What does the Highest by Department (Pages) view show?
It ranks engineers by the number of incident pages they receive, highlighting those most burdened by on-call duties in the organization.
Why is page concentration important for the C-suite?
Page concentration indicates retention risks and governance issues, as engineers with high page loads may manage critical systems and are more likely to leave if overburdened.
How does AI usage relate to on-call burdens?
Engineers managing complex systems often use AI tools heavily, and when combined with high on-call burdens, it concentrates value and risk in a few individuals.
How does Larridin's platform help address workload balance?
Larridin cross-references page concentration with AI data to identify engineers who are both highly paged and high AI users, facilitating discussions on workload balance.