OKRs (Objectives and Key Results)
OKRs (Objectives and Key Results) is a goal-setting framework that pairs a qualitative, ambitious objective with a small set of measurable key results used to track progress toward it.
Definition
OKRs (Objectives and Key Results) is a goal-setting framework that pairs a qualitative, ambitious objective with a small set of measurable key results used to track progress toward it.
Overview
An OKR has two parts: the Objective is a short, qualitative statement of what a team wants to achieve — ambitious and motivating rather than a number — and the Key Results are typically two to five measurable outcomes that indicate whether the objective was met. A well-formed key result is a specific, verifiable metric, not a task; "launch the new onboarding flow" is a task, while "increase onboarding completion rate from 60% to 80%" is a key result. The framework was developed at Intel under Andy Grove and later popularized broadly after Google adopted it in the late 1990s, and it has since become a common goal-setting system across the tech industry. OKRs are usually set on a quarterly cadence and cascade loosely from company-level objectives down to team and individual objectives, though many organizations deliberately keep some slack between levels rather than forcing rigid alignment. OKRs are often used alongside other planning structures — a company running SAFe or Scrum might still set quarterly OKRs to define the "why" behind sprint-level work, and product managers frequently use OKRs to translate strategy into measurable team priorities distinct from a raw feature backlog.
Key Features
- Objective: a short, qualitative, ambitious statement of intent
- Key Results: two to five specific, measurable outcomes tied to the objective
- Typically set on a quarterly cadence with periodic check-ins
- Loose cascading from company to team to individual objectives
- Deliberately ambitious targets, sometimes scored below 100% completion by design
- Separation of outcome metrics (key results) from task lists or backlogs
- Regular scoring and review to track progress and inform the next cycle
Use Cases
Frequently Asked Questions
From the Blog
Prompt Engineering: Get Better Results from Any LLM
The difference between a mediocre AI output and an excellent one is usually the prompt. This guide covers the techniques that consistently produce better results: clarity, context, examples, chain-of-thought, system prompts, and output formatting — with real before/after examples.
Read More AI & TechnologySemantic Search Explained: Beyond Keywords
Semantic search finds results by meaning rather than exact words, using vector embeddings so a query and a relevant document match even with no shared terms.
Read More AI & TechnologyWhat Is Semantic Search and How Does It Work?
Semantic search finds results by meaning, not keywords, using embeddings to represent text as vectors and matching queries to the closest ones in vector space.
Read More Data SciencePandas GroupBy Explained With Examples
Pandas GroupBy splits a DataFrame into groups, applies an aggregation, and combines the results. Learn the split-apply-combine pattern with clear examples.
Read More