01
Adoption visibility
Who uses AI repeatedly, where, and for which kinds of work.
PromptLeash Methodology
Measure transformation without overstating the evidence.
The Adoption Score approach is designed to make adoption and organisational change visible. It distinguishes indicative signals from a verified baseline supported by organisational evidence.
The evidence dimensions
Evidence is organised across five dimensions. No single activity metric is treated as proof of transformation, and each dimension is read in the context of the roles and workflows it describes.
02
Workforce capability
Whether people can use AI effectively and improve over time.
03
Workflow integration
Whether important work changes rather than simply gaining an optional tool.
04
Governance
Whether responsible-use controls, ownership, and feedback keep pace with adoption.
05
Impact evidence
Whether observed change connects to decisions and relevant business outcomes.
What the Adoption Score measures
The product's displayed indicators are measured from approved AI activity in the customer's environment, such as adoption breadth, prompt and output quality, cost efficiency and compliance. They are produced from observed activity, not from self-reported answers.
Coverage depends on the sources approved and connected in the customer's deployment, so the indicators describe the activity in scope rather than every interaction in the organisation.
Indicative signals versus a verified baseline
An early view built from partial sources is directional. It helps identify where to look next; it is not an independent audit or a verified baseline.
Comparisons in the product use the customer's own context and role potential (how effectively AI is used for the work a role requires) rather than a cross-customer or peer dataset. A role-potential or within-customer comparison is indicative, not a verified peer dataset or benchmark.
A verified baseline should use agreed definitions and relevant organisational evidence, review conflicting signals, document missing data, and make the interpretation available for challenge.
Interpretation principles
- Access and activity do not, by themselves, demonstrate adoption or impact.
- Evidence is interpreted in its organisational and workflow context.
- Missing or weak evidence is made visible rather than converted into false precision.
- Results should guide the next decision and be reviewed as the organisation changes.
Current limitations
- PromptLeash does not publish a cross-customer or peer benchmark dataset; product comparisons use the customer's own context and role potential.
- Transformation outcomes vary by task, organisation, workforce, controls, and implementation quality.
- Adoption Score evidence should complement, not replace, legal, security, risk, finance, or workforce expertise.
Bring your next AI investment question.
See how PromptLeash interprets approved AI activity in the context of your roles and workflows, and what evidence it can bring to your next business review.