Target outcomes
- Workshop-to-charter cycle cut from days to hours with HITL
- Field-level accuracy tracked before ERP/PM sync
Initiative playbook
Typical delivery arc for this pattern in enterprise programs.
- 1Discovery2 to 4 wks
Define Zod schemas with PMO for initiative charters; collect 50 anonymized workshop transcripts.
- 2Pilot6 to 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.
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
- Server validates output against Zod before returning to client
- 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
Where else this applies
Schema-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.
Using this stack elsewhere
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.
Live demo
The demo is the same code path described above, not a simplified mock UI. Add keys in .env.local when you are ready; the narrative and diagrams stand on their own without them.
Business
Dump workshop notes, get a charter-shaped JSON back. Still needs a human before it becomes a Jira epic, we are not pretending otherwise.
Technical
Claude generateObject against a Zod schema; validated on the server before the UI renders it.