Context
Growth-stage company attempting to introduce AI-driven features on top of fragmented data systems.
Problem
AI initiatives stalled due to inconsistent data quality, unclear ownership, and unreliable analytics.
Constraints
Existing production systems could not be disrupted and data teams were already overextended.
Scope
Fractional CTO overseeing data strategy, analytics reliability, and AI enablement.
Strategy
Establish a single source of truth and clear data ownership before introducing AI capabilities.
Architecture
Implemented event-driven ingestion, data contracts, and separation between operational and analytical workloads.
Impact
Enabled delivery of production AI features and restored executive trust in analytics reporting.
Effects
Product teams began using data proactively rather than defensively justifying metrics.
Artifacts
Key Insights
AI readiness is primarily a data governance problem, not a model selection problem.