Guide: Model Completeness & Data-Driven Expansion
Introduction
With this guide, you will develop your own model completion roadmap — supported by the AI Assistant.
The assistant helps reveal gaps in your enterprise model and derive concrete extensions and prioritized actions.
Click a prompt to launch it directly in the AI Assistant — from overview and completeness checks to quick wins, prioritization, and the business case.
Step 1 – Build an Overview
📋 Example Output:
🏭 Processes (4)
- Car Rental – from booking to return
- Vehicle Maintenance – planning, QA, cleaning
- Customer Complaints – intake and response
- Customer Support – issue resolution and documentation
⚙️ Activities (Examples)
Car Rental: Reservation → Access → Return
Maintenance: Planning → Execution → QA → Cleaning
🧩 Systems (11)
Car-sharing platform (no description) · DMS · Fleet Management · Accounting · Vehicle Tracking · …
👥 Services · Organization
Services: Customer Support · Car Rental · Vehicle Maintenance
Departments: Technology · Maintenance · Customer Service
Teams: Fleet Management · Quality Assurance · Digital Support · IT Operations
👉 Goal: Quickly understand model scope and detect initial gaps or inconsistencies.
How the AI Assistant works
- Reads available process and system data (name, description, link) and groups them by domain or process.
- Highlights missing descriptions or incomplete areas.
- Outputs a compact, summarized list (with pagination for >50 items).
Step 2 – Process Steps & Completeness
📋 Example Output:
| Process | Start | Flow (simplified) | End | Status |
|---|---|---|---|---|
| Car Rental | Reservation | Reservation → Access → Monitoring → Return | Return | 🟢 Complete |
| Vehicle Maintenance | Planning | Planning → Execution & QA → Cleaning → Fleet Balancing | Fleet Balancing | 🟢 Complete |
| Customer Complaints | (multiple) | Intake • Record • Forward • Reply • Send • Document (some unclear) | (multiple) | 🟡 Unclear |
| Customer Support | First Contact | First Contact → Resolution → Documentation → QA/Escalation | QA/Escalation | 🟢 Complete |
👉 Goal: Identify logical order, entry and exit points, and potential gaps.
How the AI Assistant works
- Reconstructs sequences from
activity-[:triggers]->activitylinks (or heuristics if missing). - Detects start and end activities via inbound/outbound relations; flags unclear ones.
- Returns a traffic-light indicator only — no lengthy explanations.
Step 3 – System Coverage & Manual Steps
📋 Example Output:
| Process | Activity | System | Manual Share |
|---|---|---|---|
| Vehicle Maintenance | Cleaning & Preparation | None | 🔴 High |
| Vehicle Maintenance | Execution & QA | None | 🔴 High |
| Customer Complaints | Draft Response | None | 🔴 High |
| Customer Support | Documentation & Follow-Up | Available | 🟡 Medium |
👉 Goal: Identify manual hotspots and missing automation potential.
How the AI Assistant works
- Checks
system-[:serves]->activityandexecution_type(auto / semi / manual). - Infers automation levels heuristically where metadata is missing.
Step 4 – Generate Concrete Recommendations
4a) Adjust Processes
| Priority | Process | Recommended Change | Benefit |
|---|---|---|---|
| P1 · Quick Win | Car Rental | Add “Payment / Contract Confirmation” before vehicle access | Ensures revenue & compliance; low effort |
| P1 · Quick Win | Customer Complaints | Enable automatic acknowledgment email | Fewer inquiries; instant customer response |
| P2 · Manual | Maintenance | Add mandatory final test drive | Higher quality; moderate training effort |
👉 Goal: Identify clear, actionable improvements that deliver quick value.
How the AI Assistant works
- Cross-references sequences from Step 2 with manual hotspots from Step 3.
- Estimates impact and effort, assigning priority (P1–P3).
- Labels “Quick Wins” as high-impact, low-effort (≤2 weeks).
- Outputs each recommendation with expected outcome and reasoning.
🧭 Priority Levels (P1–P3)
- P1 · Quick Win: High benefit, low effort (≤2 weeks); immediate visible results.
- P2 · Medium Priority: Strong benefit, requires coordination or training (≈2–6 weeks).
- P3 · Long-Term: Strategic but complex (>6 weeks); suited for roadmap initiatives.
4b) Extend Data Model
| Priority | Extension | Description | Benefit |
|---|---|---|---|
| P1 · Quick Win | Trigger Properties | Type, order, probability, mandatory | Enables consistent sequencing |
| P1 · Quick Win | Data Flow Attributes | Interface, frequency, volume, latency, PII | Improves integration & compliance transparency |
| P1 · Quick Win | SLA / Lead-Time | At activity level; RPO / SLO for systems | Enables resilience & governance monitoring |
👉 Goal: Strengthen data structure for analytics, compliance, and decision-making.
How the AI Assistant works
- Analyzes failed reports or unclear metrics caused by schema gaps.
- Suggests minimal, backward-compatible schema extensions.
- Rates each suggestion by transparency, controllability, and compliance value.
4c) Add Missing Processes or Systems
| Priority | Name | Core Steps | Involved Teams / Systems |
|---|---|---|---|
| P1 · Quick Win | Microservice “Auto-Ack for Complaints” | Detect intake → Fill template → Send | Digital Support, Dev · Email, DMS |
| P1 · Quick Win | Sub-Process “Payment & Contract Confirmation” | Verify reservation → Authorize payment → Confirm contract | IT, Dev, Accounting |
👉 Goal: Close end-to-end gaps by introducing targeted sub-processes or systems.
How the AI Assistant works
- Detects missing steps or unlinked services from previous analysis.
- Outlines scope, key steps (A→B→C), and responsible systems/teams.
- Rates feasibility and dependencies, recommending MVP first.
Step 5 – Evaluation & Prioritization
📊 Example Output:
| Recommendation | Category | Benefit | Effort | Criticality | Priority | Justification |
|---|---|---|---|---|---|---|
| Payment & Contract Confirmation | Sub-Process | High | Low | High | P1 (Quick Win) | Ensures revenue and compliance before access. |
| Add Trigger Properties | Data Model | High | Low | Medium | P1 (Quick Win) | Enables reliable sequence and completeness analysis. |
| Add RPO/SLO Attributes | Data Model | Medium | Low | High | P1 | Improves resilience and disaster recovery readiness. |
| Digital Damage Documentation | Sub-Process | Medium | Medium | Medium | P2 | Increases traceability and quality documentation. |
👉 Goal: Provide a clear, management-ready prioritization list (P1–P3) with concise reasoning.
How the AI Assistant works
- Aggregates all proposals (4a–4c) into a single scorecard (Benefit × Effort × Criticality).
- Normalizes results, removes duplicates, and assigns final priorities.
- Highlights “Quick Wins” with clear justification for leadership decisions.
Step 6 – Quick Wins Summary
📋 Example Output:
- Auto-Ack for Complaints (Quick Win): faster first response; Owner: Digital Support · Next Step: activate ticket ID template.
- Ticket Closure & CSAT (Quick Win): cleaner closure + feedback; Owner: QA · Next Step: trigger CSAT on resolution.
- Trigger Properties (Quick Win): consistent sequence tracking; Owner: Knowledge Graph Admin · Next Step: extend schema & backfill.
- Maintain RPO/SLO Attributes (Quick Win): improves resilience; Owner: IT Ops · Next Step: populate fields and dashboard view.
👉 Goal: Provide an actionable to-do list with ownership and measurable follow-up.
How the AI Assistant works
- Consolidates all Quick Wins from Steps 4–5.
- Assigns owners and defines immediate next actions.
- Suggests success metrics (e.g. ”% Auto-Acks”, “Time-to-First-Response”).
Step 7 – Business Benefits Summary
🎯 Example Output:
- Efficiency: −15–30% lead time · −10–25% handling time · +5–15 pp first-contact resolution
- Cost: −10–25% cost per case · −20–40% chargebacks
- Quality/Compliance: +10–20 pp SLA adherence · −30–50% audit deviations
- Employees: −20–30% onboarding time · −15–25% search effort
- Customers: −50–80% first-response time · +8–15 pp CSAT
- Governance: +60–90% sequence coverage · +70–95% dependency transparency
👉 Goal: Present a concise, measurable business case summary.
How the AI Assistant works
- Aggregates quantitative effects from previous steps across six benefit categories.
- Provides indicative ranges for measurable impact (based on typical enterprise benchmarks).
Step 8 – Generate PDF Business Case Report
👉 Goal: Deliver a professional, management-ready PDF summary of the overall business impact.
How the AI Assistant works
- Compiles metrics from Step 7 into formatted tables by benefit category.
- Creates a concise executive summary and visual presentation in company style.
- Exports a print-ready A4 PDF suitable for management reviews or board discussions.
Conclusion
With these prompts, the AI Assistant guides you from model overview to concrete, prioritized improvements and measurable impact:
- Steps 1–3: Analyze structure, sequence, and system coverage
- Steps 4–5: Close gaps, expand data and process scope, define priorities (P1–P3)
- Steps 6–8: Summarize quick wins, quantify benefits, and create a business-ready report
🎯 Result: A complete, consistent, and data-driven enterprise model — with clear priorities, measurable results, and tangible business impact across automation, reporting, and decision-making.
👉 Get started: Launch the prompts in chat, apply them to your own domain, and progressively build a robust, connected enterprise model.