Department

Outcomes & Hiring

20 engineers · 3 teams

Operating lift

1.08/ 2

-0.00 QoQ

AI adoption

87%

of engineers

$ / seat · mo

$335

Trained

60%

12/20 engineers

Top outcome

20% fewer bugs reaching prod since the trained cohort ramped.

Teams in this departmentClick a team

Outcomes InsightsLead · Elena Vazquez

1.1689%

Interview SystemsLead · Nadia Petrova

1.1188%

Hiring WorkspaceLead · Jamal Whitfield

0.9784%

Recommended actions

Outcomes & HiringCoach cohort

This department needs coaching — review checkpoints lag and one squad trails on training.

Outcomes InsightsMeasure first

Premium spend rose without outcome movement; measure before expanding tool budgets.

All recommendations

How Outcomes & Hiring uses AI

Full tasks

AI completes multi-step work end-to-end

39%

Code suggestions

inline help while engineers type

35%

Q&A

asking the AI questions

18%

Review help

AI checks code before it ships

Training liftJan → Jun 2026

PRs using AI agents59% · was 32%

Reviews with AI notes57% · was 37%

Runs with context summary53% · was 34%

AI PRs with eval hooks48% · was 24%

SpendSpend page

$ / issue

$149

$ / seat · mo

$335

$ / 1k lines

$26

Hours / issue

7.7

Budget used · month68% of $16.0k