OKRs (Objectives and Key Results)

OKRs (Objectives and Key Results)

Overview

OKRs separate ambition from measurement: an Objective is qualitative and aspirational; Key Results (3-5) are quantitative outcomes proving the objective was reached. KRs must be outcomes, not activities. Calibration rule: 70% achievement is success — routine 100% means goals were sandbagged. Developed by Andy Grove at Intel (1971); introduced to Google by John Doerr (1999).

Composes with north-star-metric, first-principles, metacognition, mece.

When to Use

• Team goals are vague, unmeasurable, or just "complete project X" lists
• Teams hitting all goals but the business isn't moving (sandbagging signal)
• Cross-team work failing due to private, conflicting goals; or a new company needs goal infrastructure
• Setting OKRs on AI features and the draft KR is "ship AI" / "increase AI usage" (vanity/Goodhart metric — replace with outcome KRs)

Not when: under ~10 people with sufficient informal alignment; inherently uncertain output (research labs); leadership will punish 70% achievement.

Coaching Novices (Adaptive Front Door)

• Engine mode: user has a concrete case → run The Process directly.
• Coach mode: user unfamiliar or no concrete case → guide step by step.

In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.

1. OKRs separate the inspirational what we're trying to be (Objective) from the measurable how we'll know (3-5 Key Results) — aiming for 70% so goals stretch beyond what's safe.
2. Check fit: under 10 people / uncertain output / leadership punishes 70% → not the right time.
3. Elicit the actual goal the team is trying to set.

[WAIT — do not advance until user responds]

1. Ask one question at a time: what's the outcome? What would prove it happened? Activity or outcome? Would 70% be a real win?

[WAIT — do not advance until user responds]

1. Close: one well-formed Objective and 3 Key Results, plus the cadence to revisit them.

[WAIT — do not advance until user responds]

The Process

Step 1 — Write the Objective. Qualitative, aspirational, time-bounded, one sentence. Test: "If achieved, would this objectively matter to the business?"

Step 2 — Write 3-5 Key Results. Quantitative outcomes (not activities), time-bounded, 70%-success-calibrated. "Ship v2.0" = activity (wrong). "Reach 100k WAU on v2.0 within 30 days" = outcome (right). If all KRs happen and the Objective is not achieved, the KRs are wrong — iterate.

Step 3 — Calibrate ambition. >85% probability per KR → sandbagged, stretch it. <30% → unreasonable, compress. Target: 50-70% probability.

Step 4 — Cascade and align. Company OKRs first → Team OKRs derived from company → Individual OKRs (optional). Cross-team dependencies must appear explicitly in both teams' OKRs.

Step 5 — Run the quarterly cycle. Week 1: set. Mid-quarter: score 0.0–1.0. Final week: scoring + retrospective. Carryover: carry, drop, or rewrite.

Step 6 — Score honestly. Each KR scored 0.0–1.0, reported transparently. Scoring is NOT performance evaluation. Sandbagged 1.0s are worse than stretched 0.6s.

Output: Quarterly OKR Sheet

# OKR: Q[X] [Year] — [Team]
## Objective 1: 
- KR1: [start] → [target] (actual: [x], score: [0.0-1.0])
- KR2: ...  KR3: ...
## Dependencies: depends on / others depend on us for
## Confidence: start / mid-quarter / end actual

→ Method in Action: Andy Grove at Intel, 1971; John Doerr at Google, 1999 · Bono's ONE Campaign

→ 2026 lens: OKRs in an AI-native org (2024–2026)

Pack: OKR Patterns

Applying It Well

Limit ruthlessly: ≤2 Objectives per team, ≤5 KRs each. Celebrate 70% explicitly — punishing it destroys stretch. Decouple scores from comp/performance reviews. Require cross-team OKRs to reference each other.

→ Primary sources: references/sources.md

Common Rationalizations

[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.

Red Flags

• KRs that are activities ("ship," "launch," "hire," "complete") — 100% achievement every quarter
• More than 5 Objectives or 5 KRs per Objective — OKRs invisible across teams
• OKR scores tied to compensation — no quarterly retrospective

Verification

• Each Objective: qualitative, ambitious, one sentence
• Each KR: quantitative, outcome-focused, time-bounded, genuinely stretching (70% target)
• Cross-team dependencies explicit; OKRs visible org-wide
• Quarterly cadence established; scoring honest and decoupled from performance reviews

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Part of deciqAI Knowledge Skills — 227 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/c/okr-goal-setting · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/okr-goal-setting.json