The Prerequisite Stack
Most companies want Layer 4 benefits (AI-powered intelligence, predictive insights, automated decision support) but they're trying to build on broken Layer 1-3 foundations.
Data Model Integrity
The Foundation of Everything
Objects, properties, and associations that reflect your business reality — not generic SaaS assumptions.
What This Includes
- Objects that match how your business actually operates
- Properties that capture meaningful distinctions
- Associations that reflect real relationships
- Architecture designed for your specific domain
The Diagnostic Question
Does your data model match how your business actually operates?
When This Layer Fails
When data model is wrong: Every report is misleading. Every workflow fights against reality. Every integration breaks.
Human Enablement
Capability, Not Compliance
People who use it correctly and understand why it matters — not just feature training.
What This Includes
- Role-based training that builds genuine capability
- Understanding of "why" not just "how"
- Workflows that feel natural, not bureaucratic
- Champions who model correct behavior
The Diagnostic Question
Are your people using the system correctly and consistently?
When This Layer Fails
When humans aren't enabled: Perfect data model fills with garbage. Workarounds proliferate. Shadow systems emerge.
Operational Discipline
Ongoing Health, Not Hope
Ownership, maintenance rhythm, and governance that prevents decay — not hope-based data quality.
What This Includes
- Clear ownership of data quality
- Regular audits that catch drift early
- Governance that enables, not constrains
- Process evolution as business changes
The Diagnostic Question
Is there ownership and maintenance keeping it healthy?
When This Layer Fails
When discipline is missing: Quality decays weekly. Small problems compound. Trust erodes until system is abandoned.
Context Graph Readiness
The Payoff
Only then can decision traces be captured and leveraged for AI-powered intelligence.
What This Includes
- Decision traces capturable because data model supports them
- AI agents can synthesize across objects because associations are meaningful
- Precedent becomes queryable because history is actually there
- Institutional memory compounds because people are contributing to it
The Diagnostic Question
Can you capture and query decision context, not just outcomes?
When This Layer Fails
When you skip to Layer 4: AI projects underdeliver. "Intelligence" is actually hallucination. Investment is wasted.
Each Layer Depends on the Previous
You can't enable humans on a broken data model. You can't maintain what people don't use correctly. You can't capture context graphs on a foundation that's crumbling.
This is why we build foundations — not features.
Where Are You in the Stack?
Take our Foundation Readiness Assessment to diagnose which layers need work and understand what it will take to get to context graph readiness.