03 · The reusable foundation
Data, AI, Integration, and Assurance converge around an owned workflow, connecting enterprise priorities to permitted effects, operating evidence, and portfolio decisions.
BUSINESS ARCHITECTURE
The architecture traces enterprise intent through the four capability domains to a controlled effect, operating evidence, and the subsequent portfolio decision.
CAPABILITY DOMAINS
Can the required data support the outcome and operating mode?
Evidence to look for Data-product contracts, quality tests, lineage, access decisions, and recovery exercises.
Can the AI-enabled workflow deliver the declared outcome reliably?
Evidence to look for Baselines, evaluation packs, release records, adoption signals, service ownership, and cost records.
Can the workflow consume governed context and act through stable interfaces?
Evidence to look for Approved mappings and authority, gateway tool contracts, policy propagation, lineage, compatibility tests, action policies, and effect reconciliation. A graph database is optional; the governed semantic baseline is not.
Can the organization constrain, observe, recover, and prove operation?
Evidence to look for Authorization decisions, stop tests, telemetry correlation, incident exercises, and redress records.
Data, AI, Integration, and Assurance connect an owned outcome to a permitted effect and the operating evidence people need to learn.
Choose a view, inspect blocks in the map, or inspect every connection in the complete text list. The map explains capability relationships; it does not assess an organization.
The guided tour starts automatically when this diagram enters view.
Business value path. Follow a bounded need through an owned workflow, controlled effect, evidence, and learning.
Selected Business value path: Enterprise need
This educational business architecture does not establish readiness, prescribe a target state, or create a customer roadmap.
DAIS-owned Archify architecture specification; public educational sidecar; no customer or assessment data.Each lens keeps its question, capability conditions, and evidence examples separate.
Can the required data support the outcome and operating mode?
Evidence to look forData-product contracts, quality tests, lineage, access decisions, and recovery exercises.
Can the AI-enabled workflow deliver the declared outcome reliably?
Evidence to look forBaselines, evaluation packs, release records, adoption signals, service ownership, and cost records.
Can the workflow consume data and act through stable interfaces?
Evidence to look forAPI and event contracts, semantic mappings, action policies, error handling, and effect reconciliation.
Can the organization constrain, observe, recover, and prove operation?
Evidence to look forAuthorization decisions, stop tests, telemetry correlation, incident exercises, and redress records.