Data infrastructure dependencies
Identify whether required data exists, is accessible, and meets quality thresholds. Surface gaps in volume, recency, labeling, and schema consistency that would block model training or inference.
Method
Five-category dependency audit
Most AI projects fail in production not because the model was wrong, but because a dependency was invisible at planning time. This process surfaces those blockers before investment is committed.
System integration constraints
Map the downstream and upstream systems the AI solution must connect to — APIs, data pipelines, workflow tools — and identify integration effort, latency requirements, and access constraints.
Process redesign requirements
Determine whether existing workflows must change for AI to operate correctly — including handoff restructuring, role adjustments, or new human review checkpoints that are prerequisites for safe deployment.
Governance and compliance gates
Identify regulatory, legal, and model risk management approvals required before the solution can go live — including documentation, validation requirements, and explainability obligations.
Organizational readiness
Assess whether the team has the skills, change management support, and executive sponsorship to deploy and sustain the solution after launch.
Dependency chain sequencing
Build a dependency graph that shows which blockers must be resolved in which order, and where parallel workstreams are possible versus where sequencing is mandatory.
Outputs
Artifacts produced by the process
Dependency register
Comprehensive inventory of all identified dependencies by category, with owner and resolution path.
- Dependency type and category
- Current status: resolved, in-progress, blocked
- Owner and estimated resolution timeline
Data readiness scorecard
Assessment of data assets required for each initiative — what is available, what needs remediation.
- Data source inventory per use case
- Quality and volume gap analysis
- Labeling and enrichment requirements
Blocker resolution plan
Prioritized plan for resolving the highest-impact dependencies before development begins.
- Critical path blockers identified
- Parallel-workstream opportunities
- Escalation path for unresolvable constraints
Readiness-adjusted roadmap
Initiative roadmap updated to reflect actual dependency resolution timelines — not wishful thinking.
- Phase start gates tied to dependency resolution
- Risk flags for unresolved blockers
- Fallback sequencing if dependencies slip
Engagement Cadence
How the process runs in practice
Typical timeline: 1-2 weeks
- Days 1–3: dependency category audits across data, systems, and process
- Days 4–6: governance and compliance gate mapping, organizational readiness review
- Days 7–10: dependency chain sequencing, blocker resolution planning, roadmap update
Output: a dependency register and readiness-adjusted roadmap that eliminates planning surprises and aligns initiative timelines to what is actually achievable.