# Outputs vs Outcomes

Distinguish produced work from changed reality and escape the feature-factory trap.

Duration: 10 min

An output is what a team produces. An outcome is the change that work creates. This lesson makes that distinction usable across software, healthcare, marketing, and operations.

## fmap-m3-l1-v1

The approved narration and storyboard are present in the FMAP v1.3.1 source pack. This placeholder remains excluded from public rendering until a reviewed provider asset and human-reviewed captions replace the pending ID.

## fmap-m3-l1-digest

### Outputs, outcomes, and the feature factory

An **output** is what you produce: a feature shipped, a form deployed, a report written, or a ticket closed. Outputs are visible, countable, and mostly within the team's control. An **outcome** is the change the work creates for a customer or the organization: a problem solved, behavior changed, or meaningful measure moved. Outcomes arrive later and are influenced rather than controlled.

A **feature factory** measures itself by how much it ships while the results that matter remain flat. It often emerges because visible activity is easier to reward than delayed change. The remedy is to expose the hypothesis inside each backlog item: **we believe shipping X will move Y**. Delivery then becomes the start of the evidence cycle, not its finish.

The chain has three links. A clinic deploys a scheduling form—an output. Patient waiting time falls—an outcome. Satisfaction rises and more people can be served—an organizational impact. Control fades along that chain, but meaning increases. Outcome goals sit where influence and value overlap.

Durable teams matter because someone remains after release to see whether Y moved. A temporary team can disappear at the output boundary; a product team can inspect evidence and decide whether to continue, revise, or stop.

Keep three levels of “done” separate: produced, usable, and valuable. A completed output is a bet. The outcome is the evidence that decides what the team should do next.

## fmap-m3-l1-i1

The deliberately nuanced training card distinguishes an output from a leading indicator without dismissing its possible predictive value.