AI workflow

A bounded operational process where AI handles defined steps end-to-end with measurable KPIs.

An AI workflow is the unit we ship: a defined process (e.g. classify support tickets, draft outbound replies, triage compliance alerts) where AI handles repeatable work, humans handle judgment, and KPIs are measured weekly against baseline. Workflows are bounded, governed, and operated — not vague AI initiatives.

When it matters

When framing the engagement scope. The 'AI workflow' is what we ship — not 'AI features', not 'AI models', not 'AI tools'. The workflow is the unit of value and accountability.

Real example

A scoped AI workflow: 'Triage inbound fraud alerts at Acme Bank'. Inputs (alert payloads), processing (retrieval, scoring, evidence assembly), outputs (reviewer queue tickets), KPIs (false-positive rate, time-to-clearance), owner (Head of Fraud Ops). One workflow, one SoW, one team.

KPIs to watch

Workflow KPI vs baseline (the primary outcome metric), workflow uptime (>99.5%), workflow cost per case (instrumented, reported weekly).

Related terms

AI-native

A delivery model where AI is the operating layer of the workflow, not a feature added on top.

Evaluation harness

A test framework that scores model or prompt output against a labelled set of expected outputs.

Audit log

Tamper-evident record of every model input, output, version, and reviewer action.

Thin slice

A narrow, end-to-end production deployment that proves an AI workflow on real data and edge cases.

See it in action

We use this every week

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