Approval Workflow & RACI Map from Form Submissions
Define states, transitions, approver rules, SLAs, and a clear RACI so requests move from submit to decision without stalls.
Prompt Overview
Tips For You
Use no more than 5–7 core states to start. Write transitions in plain verbs like Send to Risk or Return for Edit. Set SLAs per role capacity, not wishful thinking.
From Operations TeamNexusAi TechnologyProblem It Solves
Ad hoc approvals create delays and blame. This prompt turns messy handoffs into a transparent, enforceable process.
State machine spec
Clear states, transitions, and actions for approvals.
Role-based routing
Assignment rules that scale with the team.
SLA and escalations
Time-bound targets to keep items moving.
Audit and RACI
Governance baked into everyday work.
AI Prompt Instructions
Act as: Operations Designer specializing in approvals and governance.
Why this task matters: Approval bottlenecks slow delivery and erode trust. A crisp state machine, clear roles, and reasonable SLAs keep throughput high and auditable.
Important boundaries:
- Keep the state machine minimal yet complete; avoid overly granular states.
- RACI must be role-based, not person-based.
- Include escalation paths and timeouts.
User inputs:
[Business context]
[Form type]
[Roles and teams]
[Compliance constraints]
[Typical blockers]
Objectives:
1) Produce a state machine with entry/exit criteria per state.
2) Define assignment rules and approver eligibility.
3) Set SLAs per state and escalation triggers.
4) Provide a RACI for each key step.
5) Recommend audit log events.
Analysis workflow:
1) Identify decision points and mandatory checks.
2) Define states from submission to resolution.
3) Map transitions with guards and actions.
4) Assign ownership and RACI per step.
5) Set SLAs and escalation rules with timers.
6) List metrics to monitor (cycle time, rework rate, stuck items).
Required output format:
- StateMachine {states[], transitions[{from,to,guard,action,notify}]}
- AssignmentRules {routing_by_role, load_balancing}
- SLAs {state, target_hours, escalation_chain}
- RACI [{step, R, A, C, I}]
- AuditLogEvents []
Quality controls:
- Each transition has an action and notification guidance.
- SLAs reflect realistic staffing and volume.
Verification checklist:
- Can a new analyst follow the steps without coaching?
- Are escalations unambiguous and time-bound?
Final instruction: Output the state machine and RACI first, then give a quick-start runbook for approvers and submitters.
Expected Outcome
States: Submitted → Triage → Manager Review → Risk Review → Approved/Rejected. SLAs: Triage 8h, Manager Review 24h. RACI: Triage (R: Ops Analyst, A: Ops Lead, C: Requester, I: Finance). Transitions include notify rules and audit events.
Implementation Journey
Map the workflow in Claude
Open Claude and paste your business context, form type, roles, and typical blockers. Ask for a compact state machine with transitions, actions, and notifications. Expect a diagram-like listing with guards and outcomes.
12-15 minPressure-test SLAs in ChatGPT
Paste Claude’s state machine into ChatGPT. Provide average volumes and staffing. Ask for realistic SLAs, escalation chains, and capacity checks. Expect time-bound targets grounded in throughput math.
10-12 minAdopt the RACI in your SOP
Publish the RACI to your internal SOP and add audit events to your app’s logging. Train approvers using the quick-start runbook so decisions are consistent from day one.
20-30 min
