Methodology
How the AI Revenue Workflow Dataset is built
The dataset maps workflows into a buyer-facing operating model: business problem, baseline metric, AI role, human review point, risk boundary, pilot design, and claim level.
1
Workflow Boundary
Each record must name a distinct operating workflow and why it is not a duplicate of nearby workflows.
2
Metric Design
Each workflow gets a baseline metric, source system, collection method, leading indicators, and lagging indicators.
3
Pilot Shape
Each workflow defines owner, sample size, duration, readiness blockers, and success threshold.
4
Claim Boundary
Records are labeled as directional or pilot-shaped unless real field evidence validates outcomes.
Scoring
Deployability readiness is not an outcome claim
Business Value
Whether the workflow is attached to revenue, conversion, retention, margin, speed, capacity, or customer experience.
Evidence Clarity
Whether trigger, required inputs, source systems, baseline metric, and pilot sample are defined.
AI Fit
Whether AI prepares, classifies, drafts, summarizes, routes, or scores bounded work.
Human Review
Whether a named owner reviews exceptions, customer-visible output, commitments, or decisions.
Risk Containment
Whether failure modes, stop rules, not-ready states, and human gates are explicit.
Measurement Quality
Whether baseline metric, collection method, leading indicators, and lagging indicators are defined.
Claim Levels
What the dataset does not claim
Directional
The workflow is operationally plausible, but source support is indirect or company policy must define key rules.
Pilot-shaped
The workflow has defined trigger, metric, owner, evidence, review point, risk boundary, and pilot design.
Validated
Reserved for future records with field evidence. The current public dataset should not be read as proof that any workflow improves a business outcome.
Use and Citation
How to cite the asset
Cite the dataset as a workflow taxonomy and pilot-readiness reference. Do not cite it as benchmark evidence for conversion lift, churn reduction, forecast accuracy, or revenue improvement.
AI Revenue Workflow Dataset: 165 workflows mapped by business metric, AI role, human review point, risk boundary, and pilot readiness. AI Deployment Authority, 2026.