intent driven development

Enterprise KPI/OKR Workflow

A complete walkthrough showing how chant drives a real business OKR from data analysis through implementation.

OKR vs KPI — What’s the difference?

KPI (Key Performance Indicator) = An ongoing metric being tracked (e.g., churn rate, NPS score)

OKR (Objectives and Key Results) = A time-bound goal framework targeting improvement

Objective: Qualitative goal (“Improve customer retention”)

Key Result: Quantitative target (“Reduce churn from 8% to 5%”)

In this guide, churn rate is the KPI being tracked. The Q1 OKR sets a target to improve that KPI.

The Scenario

Acme SaaS Corp is a B2B platform with 5,000 customers. Their Q1 OKR targets improving customer retention by reducing churn. This guide follows their team through the full workflow — from gathering data to shipping fixes to tracking results.

Team

Role	Person	Responsibility
VP Product	Sarah	Sets OKRs, approves specs
Data Analyst	Mike	Gathers data, creates digests
Engineers	(managed by chant)	Implement approved changes

Q1 OKR

Objective: Improve customer retention

Key Result: Reduce monthly churn rate from 8% to 5%

KPI tracked:  Monthly customer churn rate
Baseline:     8% (December 2025)
Target:       5% by end of Q1 2026
Timeline:     4 weeks

Workflow Phases

Week 1          Week 2              Week 2            Week 3         Week 4
┌──────────┐   ┌───────────────┐   ┌──────────┐   ┌───────────┐   ┌──────────┐
│  Human    │   │    Chant      │   │  Human   │   │   Chant   │   │  Track   │
│  Data     │──>│   Research    │──>│ Approval │──>│  Execute  │──>│ Results  │
│ Ingestion │   │   Phase       │   │  Gate    │   │  Parallel │   │  Daily   │
└──────────┘   └───────────────┘   └──────────┘   └───────────┘   └──────────┘

Guide Pages

The Business Context — Acme’s product, churn problem, and Q1 OKR
Data Ingestion — Week 1: Human investigation and data gathering
Research Phase — Week 2: Chant agent analyzes churn drivers
Approval Gate — Week 2: Team reviews and approves findings
Implementation — Week 3: Parallel execution of fixes
Reporting — Week 4: Daily tracking and dashboards

Key Concepts Demonstrated

Context directories for ingesting external data
Research specs for AI-driven analysis
Approval workflow with reject/approve cycle
Driver specs that decompose into parallel member specs
Activity tracking and reporting for stakeholder visibility

Prerequisites

Familiarity with core concepts, research workflows, and approval workflows.

Chant Documentation