Case studyArchitecture, governance, and how to adapt this pattern in a pilot
Business use case
Problem
Transformation discovery produces workshop notes, interviews, and legacy documents that PMOs manually turn into charters, slowing steering committees and duplicating effort.
Who benefits
- Program management, structured backlog inputs
- Consulting / transformation leads, repeatable discovery accelerators
- Enterprise architecture, systems-in-scope captured consistently
Success metrics
- Workshop-to-charter cycle from days to hours with human-in-the-loop
- Field-level accuracy tracked before syncing to Jira/ServiceNow
- Mandatory human approval before executive readout
Solution
Paste discovery text; Claude returns JSON matching a Zod schema (initiative name, risks, systems, timeline). Powered by AI SDK generateObject for type-safe server responses.
Technical implementation
Stack
- @ai-sdk/anthropic + ai
generateObject - Zod schema in
lib/demos/extraction-schema.ts - Model default:
claude-sonnet-4-20250514(override viaANTHROPIC_MODEL)
Architecture
Workshop notes go in; a schema-validated charter comes out, ready for human edit before it hits the backlog.
Implementation highlights
- Schema encodes risks with severity enum and mitigation text
generateObjectenforces the Zod schema during generation- UI renders pretty JSON for reviewer edit before export
Outcomes and learnings
- Schema-first extraction beats prose summaries for prioritization forums
- Claude handles long workshop transcripts in one pass
- Always keep human approval before committing to program charters
Delivery playbookDiscovery → pilot → scale
- 1Discovery2–4 wks
Define Zod schemas with PMO for initiative charters; collect 50 anonymized workshop transcripts.
- 2Pilot6–8 wks
Human review all extractions before backlog import; track field-level accuracy.
- 3Scaleongoing
Embed extraction in discovery workshops with HITL approval and ERP/PM tool sync.
Where else this appliesSchema-constrained extraction turns messy narrative inputs into systems your organisation already uses, PMO tools, risk registers, architecture repositories.
Initiative chartering
Workshop output becomes structured scope, risks, and timelines for steering forums.
Vendor RFP comparison
Extract requirements fit, gaps, and pricing assumptions into a scoring matrix.
Regulatory gap assessments
Turn interview notes into control gaps and remediation owners.
Architecture discovery
Capture systems, interfaces, and data flows from brown-bag transcripts consistently.
Claude via AI SDK `generateObject` excels at long-context workshop notes; the same approach works on Azure or Bedrock with different models if procurement requires it.