Glossary
AI Adoption Glossary
Clear, citable definitions for the concepts that matter in AI adoption measurement.
Written by Christine Barnett, founder of the Autonomy Index™. Last reviewed .
- AI adoption
- AI adoption is the process of embedding AI tools into repeatable workflows so they change how work is completed, measured and governed.
- AI usage
- AI usage is activity inside AI tools, measured through logins, seats and prompts. Usage does not prove workflow change or business value.
- AI impact measurement
- AI impact measurement is the process of assessing whether AI tools have created measurable workflow change, credible output quality and evidenced business value.
- AI workflow adoption
- AI workflow adoption is the point at which AI is consistently used inside a defined workflow and reliably owns part or all of that workflow.
- Workflow autonomy
- Workflow autonomy is the degree to which AI reliably owns work across a four level maturity scale, from assisted tasks to trusted end to end autonomy.
- AI theatre
- AI theatre is the appearance of AI transformation without evidence of meaningful workflow change, operational maturity or measurable business impact.
- AI debt
- AI debt is the accumulated risk, quality drift and rework created when AI is deployed into workflows faster than governance, quality and measurement can keep up.
- AI maturity
- AI maturity is the overall level at which an organisation adopts, governs, measures and scales AI across its workflows.
- AI governance
- AI governance is the set of policies, controls and review practices that ensure AI systems operate within acceptable risk, quality and compliance boundaries.
- Agentic AI readiness
- Agentic AI readiness is the extent to which an organisation can safely deploy autonomous AI agents into real workflows, with monitoring, escalation and human oversight.
- Human in the loop
- Human in the loop is a workflow design pattern where humans review, correct or approve AI outputs at defined points, keeping accountability with people.
- Output quality
- Output quality is the reliability, accuracy and fitness for purpose of AI generated work, assessed against workflow specific criteria.
- Board ready AI reporting
- Board ready AI reporting is executive level reporting that connects AI adoption to workflow change, risk and measurable business value, in language leadership can act on.
- AI value realisation
- AI value realisation is the point at which AI adoption produces measurable, evidenced business outcomes tied to workflow change.
- AI transformation
- AI transformation is the organisational programme of change through which workflows are redesigned so AI can reliably own more of the work.
- Feature depth
- Feature depth is the extent to which users employ a tool's advanced capabilities, integrations and repeatable processes rather than basic assistance.
- Workflow ownership
- Workflow ownership is how much of an end to end workflow AI reliably completes, expressed as a four stage scale from individual assistance to trusted end to end autonomy.
- Trusted workflow autonomy
- Trusted workflow autonomy is the Autonomy Quality Matrix™ quadrant where workflow ownership is high and output quality remains consistently acceptable.
- Risk at scale
- Risk at scale is the Autonomy Quality Matrix™ quadrant where workflow ownership is increasing without sufficient quality, control or reliability.
- AI adoption audit
- An AI adoption audit is a structured review of a defined workflow that evidences how much AI owns, where human scaffolding remains and whether the workflow is governed well enough to scale.
- Human oversight in AI
- Human oversight in AI is the defined set of review, override and escalation points that keep accountability with people as AI takes on more of a workflow.
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