Standard : Experiment-to-Adoption Ratio

Description

Experiment-to-Adoption Ratio measures how many improvement or innovation experiments result in full or partial adoption. It reflects an organisation’s ability to learn from change and to integrate validated improvements into standard practice.

A healthy ratio indicates a learning culture that balances innovation with practicality and is capable of translating exploration into impact.

How to Use

What to Measure

  • Count the total number of experiments run (e.g. new tooling trials, delivery process changes, observability improvements).
  • Track how many of those experiments result in full or partial adoption into team or organisational practices.

Formula

Adoption Ratio = (Number of Adopted Experiments / Total Experiments Run) × 100

Segment by:

  • Team, value stream, or engineering capability
  • Type of experiment (tech, process, culture, tooling)
  • Adoption level (full rollout, partial use, abandoned)

Instrumentation Tips

  • Use lightweight tracking (e.g. internal wiki, Jira tags, Miro boards) to record experiment goals and outcomes.
  • Record "Decision Logs" at the end of each experiment cycle.
  • Capture rationale for adoption or abandonment to support shared learning.

Why It Matters

  • Closes the learning loop: Helps ensure experiments lead to outcomes.
  • Builds momentum: Shows that improvements don’t stop at discovery.
  • Encourages rigour: Validates that teams define success and follow through.
  • Supports scale: Highlights what works and is ready for broader adoption.

Best Practices

  • Make adoption decisions part of sprint reviews or quarterly planning.
  • Link experiments to clear hypotheses and measurable outcomes.
  • Encourage teams to pilot, learn, and share results widely.
  • Celebrate both successful and abandoned experiments if learning was achieved.

Common Pitfalls

  • Experiments run without clear intent or follow-through.
  • No documentation, making outcomes invisible or hard to replicate.
  • Teams over-index on exploration without converging on stable practices.
  • Success defined too loosely, leading to premature or uncritical adoption.

Signals of Success

  • A meaningful percentage of experiments lead to improved ways of working.
  • Lessons learned from abandoned experiments are shared and valued.
  • Teams have confidence in their ability to shape and improve their delivery environment.
  • Standard ways of working are regularly updated based on validated learnings.
  • [[Number of Learning Experiments per Quarter]]
  • [[CoE/Lean/Measures/Continuous Learning & Experimentation/Time Allocated to Improvement Work]]
  • [[Engineering Learning Hours per Person]]
  • [[Retrospective Action Completion Rate]]
  • [[Adoption Time for New Practices]]