Most Business Systems teams operate in a state of controlled chaos. Requests come from everywhere—Slack, emails, hallway conversations. The team triages constantly, context-switches relentlessly, and somehow ships features. But nobody can trace how a business request became a deployed change.
We recently completed a transformation of a B2B software company’s Business Systems team into what we call an Enterprise Engineering function. The goal wasn’t just better tooling—it was building an operating model where every business change has end-to-end traceability.
The Core Insight: Processes → Systems → Data
Before we touched any systems, we established a foundational principle: business change flows through a pipeline. First, you understand the process impact. Then, you determine the system changes. Finally, you account for data implications.
This sounds obvious, but most organizations work backwards. A stakeholder asks for a new Salesforce field. The team adds it. Six months later, three processes use it differently, the data is inconsistent, and nobody remembers why it was added.
The Four-Stage Operating Model
Stage 1: Intake — The Problem Brief
Every request enters through a single front door: the Problem Brief. It’s an AI-guided discovery document that captures business outcome, process impact, system scope, success metrics, and priority.
Key insight: 30 minutes of structured intake saves 3+ hours of back-and-forth discovery.
Stage 2: Decision — Architecture Decision Records
We document every significant decision in an ADR (Architecture Decision Record) with context, options considered, the decision, and rationale.
# ADR-042: Customer Journey Tracking
## Context
The Customer Success team needs visibility into customer health signals...
## Decision
Data warehouse with reverse sync to Salesforce
## Rationale
- Single source of truth for customer metrics
- Flexibility to incorporate product usage data
Stage 3: Delivery — Linked Execution
Every Jira epic links back to its ADR, and every ADR links back to its Problem Brief. We established a predictable release cadence with automated testing.
Stage 4: Control — Change Governance
Mid-flight scope changes go through a lightweight Change Request process. Deviations are logged with rationale and tech debt implications.
Results After 6 Months
- Intake cycle time dropped 60%
- 100% decision traceability
- Quality improved — automated testing caught issues before UAT
- Team satisfaction increased — less context-switching, more engineering
The goal isn’t to eliminate change—it’s to make change traceable, manageable, and measurable.
Have questions about implementing an Enterprise Engineering operating model? Get in touch.
Have questions about this topic? Let's discuss your project.
Get in touch