← Workflow Examples

Workflow Example — Reporting & Analytics Automation

function

Finance & Operations

workflow pattern

data collection → validation → report generation → narrative summary → distribution

reliability focus

repeatable reporting logic, controlled human review, auditable delivery

Audit Logging
Inputs
Data
Outputs
Data
Distribute to Stakeholders
Inputs
Data
Outputs
Data
Human Review Checkpoint
Inputs
Data
Outputs
Data
Generate Narrative Summary
Inputs
Data
Outputs
Data
Generate Report Artifacts
Inputs
Data
Outputs
Data
Route Anomalies for Human Review
Inputs
Data
Outputs
Data
Validate Data and Detect Aniomalies
Inputs
Data
Outputs
Data
Normalize and Map Definitions
Inputs
Data
Outputs
Data
Collect Data From Source
Inputs
Data
Outputs
Data
Trigger Schedule
Inputs
Outputs
Data
System
API
Data Source
ERP
Analytics Owner
User
AI Agent
Assistant
Stakeholders
User
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What This Workflow Demonstrates

This example shows how to build a reporting workflow that is:

  • repeatable on schedule (no manual scramble)
  • explicit about data definitions and validation
  • safe to augment with AI for summaries without changing numbers
  • governed with human review checkpoints

Example Workflow Structure (Steps + Actors)

Actors

  • System Actor: scheduling, orchestration, distribution
  • Data Source Actors: ERP/BI databases/spreadsheets
  • Analytics Owner: validation and exception review
  • AI Actor: summary generation under guardrails
  • Stakeholders: recipients and approvers

Steps

  1. Trigger schedule Start on a defined cadence (daily/weekly/monthly) and create a run record.

  2. Collect data from sources Pull data from systems of record and approved spreadsheets.

  3. Normalize and map definitions Apply consistent mapping rules so metrics mean the same thing across runs.

  4. Validate data and detect anomalies Run checks such as:

    • missing values
    • outliers vs prior periods
    • totals reconciliation
    • known business rules

  5. Route anomalies for human review If validation flags issues, route to Analytics Owner with explicit resolution outcomes.

  6. Generate report artifacts Create the report tables, charts, and output formats (PDF, slides, dashboards).

  7. Generate narrative summary (AI-assisted) Produce summaries of trends and variances using approved context and templates.

  8. Human review checkpoint Require approval before distribution if policy demands it (month-end, board packs, etc.).

  9. Distribute to stakeholders Deliver via role-based rules with delivery logging.

  10. Audit logging Record inputs, validation outcomes, approvals, and distribution.

Human-in-the-Loop Checkpoints

Humans remain responsible for:

  • anomaly resolution and overrides
  • final review of sensitive reports
  • approving narrative interpretation when required

AI Guardrails (Recommended)

AI can assist with:

  • summarizing trends
  • drafting narrative sections
  • highlighting variances

AI should not:

  • change metric calculations
  • override validation results
  • distribute without required approvals

Related pages

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