Tempo Board — Engineering QBR

⚡

AI adoption is accelerating across the org — but every squad is hitting the same wall: faster code, slower review. Flow is the constraint that will determine whether Q3 goals land.

Q2 2026 · 6 squads · avg cycle time 5d 2h (up 28% Q/Q) · org-wide PR review is now 44% of cycle time · 3 of 4 strategic goals behind target

See squad breakdown ↓

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Engineering Health Score · Q2 2026

Headcount-weighted rollup of squad goal-fit scores across Lift, Promise, Flow, and Spark. Reflects how well the current org composition and operating rhythm serves your four declared strategic goals. Reviewed quarterly.

✓ Mobile relaunch on track ✓ Reliability bench growing ⚡ AI Adoption at 42% — target 60% ⚡ Flow degraded across 5 of 6 squads ⚠ AI Pioneers: new squad, predictability low

L · Lift

AI & tooling leverage

42%

↑ +14pp Q/Q · org avg

42% of all PRs had AI assist. AI Pioneers (81%) and Data Platform (68%) leading; Trust & Safety (19%) lagging.

2 squads ≥60% 3 squads 30–59% 1 squad <30%

⏱ Reviewed weekly

P · Promise

Sprint predictability

81%

→ −3pp Q/Q · org avg

Planning accuracy 81%; capacity accuracy 84%. AI Pioneers dragging the average (61%). Other squads 83–92%.

4 squads ≥85% 1 squad 70–84% 1 squad <70%

⏱ Reviewed per sprint

F · Flow

End-to-end cycle time

5d 2h

↑ +28% Q/Q — org-wide bottleneck

Review wait is 44% of cycle time org-wide. Change failure rate: 4.8%. Data Platform worst at 7d 3h.

1 squad <2d 3 squads 2–5d 2 squads >5d

⏱ Reviewed monthly

S · Spark

Developer experience

71/100

↓ −5pp Q/Q · org avg

Q2 survey: 71/100 across 34 engineers. Review friction and unclear AI policies are the top two friction sources.

2 squads ≥80 3 squads 65–79 1 squad <65

⏱ Surveyed quarterly

Squad portfolio · Q2 2026

6 squads · click any row to drill down

Squad

L · P · F · S

Goal fit

Q/Q

Top risk

Platform

5 engineers · APIs & infra

L P F S

73%

↑ +2

Single Pathfinder; review queue widening

→ View squad

Growth

7 engineers · acquisition & activation

L P F S

61%

↓ −4

No AI/ML capability; Lift pillar at risk

→ View squad

Mobile Guild

6 engineers · iOS & Android

L P F S

58%

↓ −8

Reliability single-point; low Spark — burnout risk

→ View squad

Data Platform

4 engineers · pipelines & analytics

L P F S

79%

↑ +6

7d+ cycle time; small squad (bus factor 2)

→ View squad

Trust & Safety

5 engineers · compliance & abuse

L P F S

66%

→ 0

AI policy ambiguity blocking Lift; shallow bench

→ View squad

AI Pioneers

7 engineers · agents & tooling · new Q1

L P F S

54%

↑ +11

New squad — low Promise; no Anchor at Chorus yet

→ View squad

Archetype legend:

Bridge Builder

Pathfinder

Anchor

Craftsman

Connector

Strategic goals · Q2 2026

4 org-level goals

Agent Platform Launch

Ship schedulable agent layer + MCP server by end Q2

68%

Platform (lead) AI Pioneers Data Platform ⚠ Flow blocker: review

Hit DORA Elite Threshold

Cycle time <1d, CFR <2%, deploy frequency daily per squad

44%

Mobile (best: 1d 20h) T&S (2d 8h) ⚠ Data Platform 7d+ ⚠ Platform 4d+

AI Adoption ≥ 60% of PRs

Org-wide AI-assisted PR rate sustained for two consecutive sprints

42%

AI Pioneers (81%) ✓ Data Platform (68%) ⚠ T&S blocked by policy (19%) ⚠ Growth lagging (31%)

Mobile App v3 Relaunch

Full redesign shipped to App Store & Play Store by 30 Jun

82%

Mobile Guild (lead) ✓ Growth (activation) ⚠ Spark risk: burnout signals in Mobile

Pillar coverage by goal

L · P · F · S

Goal

Agent Platform

●

DORA Elite

●

AI Adoption

●

Mobile Relaunch

●

● On track ● Watch ● At risk

Org capability bench depth

Critical capabilities × squads · ● single point of failure

Capability	Platform	Growth	Mobile	Data Plat.	T&S	AI Pioneers	Org risk
Reliability / SRE							Medium
AI / ML Engineering							High
Integration / APIs							Low
Mobile (iOS / Android)							Medium
Data / Analytics							Low
Security / Auth							High

Deep bench (3 ICs) Adequate (2 ICs) Single-point (highlighted) Not present Partial / developing

Recommended moves · Q3 2026

3 structural actions in priority order

#1 priority

🔴 Invest

↗ Lift

Grow the AI Pioneers squad to 10 ICs

AI Pioneers is the only squad above the 60% Lift threshold. With 7 engineers and low Promise scores (61%), the squad needs senior anchors before it can become an org-wide enabler. Hire 2 senior ICs and embed 1 from Platform to carry architectural norms. This directly closes the AI Adoption goal gap and unblocks T&S / Growth squads who lack AI capability entirely.

AI Adoption goal from 42% → 60%+ by end Q3. Closes org-wide AI/ML capability gap.

#2 priority

🟡 Policy

↗ Flow

Org-wide PR review SLA + AI review guidelines

Review is 44% of cycle time across every squad. This is not a headcount problem — it is a norm and tooling problem. Mandate a 2-hour pickup SLA with automated Slack nudges (already feasible in LinearB). Separately: T&S is at 19% AI adoption because there is no approved AI policy for compliance-sensitive code. Publish one. Both actions are zero-headcount, high-leverage.

Cycle time down 25–30% across all squads. Unblocks T&S Lift pillar immediately.

#3 priority

🟢 Reorg

↗ Spark

Promote 2 Anchor-stage engineers to Staff

Security/Auth has single-point-of-failure coverage in 3 squads, and AI/ML bench is thin outside AI Pioneers. Two engineers — one in Platform, one in T&S — are already operating at Staff scope but carry IC4 titles. Promoting them formally expands decision-making authority, reduces review bottlenecks (Staff-level PRs get faster approvals), and improves Spark scores by aligning compensation with contribution.

Closes Security bench risk across 3 squads. Improves Spark in Platform and T&S by end Q3.

🔐

The Vault

The full evidence pack — per-squad skill receipts, individual traceability tables, level-advancement scores for all 34 engineers, and the quarter-over-quarter rhythm narrative for each team. Never generated by default. Available on request for board preparation, compliance conversations, or internal process reviews.

📋Per-squad skill receipts with supporting evidence (6 squads)🔒

🔗Work-item traceability table (all 34 engineers)🔒

📊Individual goal-fit scores with level-advancement criteria🔒

📈Quarter-over-quarter LPFS trend per squad (Q1 → Q2)🔒

The Vault is opt-in only. It is never generated by default, and is never shown in engineer-facing views.

Data sources — what feeds this view

DevLakeLift — pull_requests across all 6 squad repos; AI-assisted PRs detected via co-author commit pattern & label convention. Aggregated to org level.

DevLakePromise — issues + sprint boards across all squads; planned vs delivered story points per squad per sprint. Org avg is headcount-weighted.

DevLakeFlow — pull_requests + cicd_deployment_commits; per-squad cycle time breakdown and change failure rate.

Pulse surveySpark — quarterly developer satisfaction survey sent to all 34 engineers. 29 of 34 responded (85% response rate). Not yet wired to DevLake.

Coverage gaps

gapPer-engineer AI tool breakdown requires a DevLake custom field or side-car log from each squad's AI coding tool.

gapT&S and Growth squads do not yet have consistent AI commit labelling — Lift figures for those squads may be undercounted.

gapCapability bench matrix is based on Tempo Card persona data, not live GitHub activity — update each quarter.