đź’ˇ Ideation Template: AI-Powered Intelligent Document Processing Platform
Our extensive experience in Human Capital Management (HCM), combined with a strong background in Finance, ICT employee HR system adoption, and HR consultancy, brings a compelling value proposition. Our expertise in transformations to Entra, Organizational Performance Management, Analytical Skills, Security and Compliance, and End User Adoption is crucial in today’s rapidly evolving business landscape.
Framework: PMBOK 7 | Status: Active | Priority: High Project ID: 511ecdde-f6aa-43d9-833c-1c94e8a51fcd Project Lead: Alex Kumar | Budget: $1,500,000 Timeline: October 17, 2025 – July 17, 2026
Document Version: 1.0 | Last Updated: [Auto-generated] | Confidentiality: Public Preview Use Only
1. Executive Summary (250 words)
The AI-Powered Intelligent Document Processing Platform (AIDPP) is a strategic initiative to revolutionize Insurance Services Group’s (ISG) claims processing workflow. Currently, ISG manually processes 50,000+ insurance claims documents monthly, with an average cycle time of 5 days per claim, leading to:
$3.2M annual operational costs (based on 200 FTEs at $50k/year + overhead).
12% error rate in data entry, causing claim delays and customer dissatisfaction (CSAT score: 68%).
Inability to scale during peak seasons (e.g., natural disasters), resulting in 20% backlog accumulation.
AIDPP will leverage machine learning (ML), natural language processing (NLP), and robotic process automation (RPA) to:
Automate 85% of data extraction from unstructured documents (PDFs, scans, emails).
Reduce processing time to <24 hours per claim (78% improvement).
Achieve 99.5% accuracy via human-in-the-loop validation.
Cut operational costs by 40% ($1.28M annual savings).
Alignment with PMBOK 7:
Value Delivery System: Directly addresses ISG’s strategic pillars of operational efficiency and customer-centric innovation.
Project Performance Domains: Focuses on Stakeholder, Development, and Uncertainty domains to mitigate risks in AI adoption.
Key Benefits:
| Metric | Current State | Target State | Improvement |
| Processing Time | 5 days/claim | <24 hours/claim | 78% faster |
| Error Rate | 12% | <0.5% | 96% reduction |
| Operational Cost | $3.2M/year | $1.92M/year | 40% savings |
| Customer Satisfaction | CSAT 68% | CSAT ≥85% | 17% increase |
2. Project Charter (500 words)
2.1 Purpose
The AIDPP project will eliminate manual document processing bottlenecks by deploying an end-to-end AI platform that:
Ingests claims documents (e.g., police reports, medical records, invoices) via OCR + NLP.
Classifies documents by type (e.g., auto, health, property) using supervised ML models.
Extracts structured data (e.g., claimant details, incident dates, damage descriptions) with 99%+ accuracy.
Validates data against business rules (e.g., policy limits, fraud patterns) via RPA bots.
Routes claims to adjusters with auto-prioritization (e.g., severity, SLA compliance).
Business Case Highlights:
ROI: 18-month payback period (savings: $1.28M/year vs. $1.5M investment).
Competitive Edge: Reduce claim resolution time from industry avg. 7 days to <2 days.
Regulatory Compliance: Automated audit trails for GDPR, SOX, and state-specific insurance laws.
2.2 Objectives
| ID | Objective | Success Metric | Target Date | Owner |
| O1 | Deploy OCR+NLP pipeline for 10 doc types | 95% extraction accuracy | 2026-02-28 | Data Science Team |
| O2 | Reduce processing time to <24 hours | Avg. cycle time ≤1 day for 90% of claims | 2026-04-30 | Operations |
| O3 | Achieve 40% cost reduction | Annual opex ≤$1.92M | 2026-07-17 | Finance |
| O4 | Improve CSAT to ≥85% | Post-implementation survey | 2026-06-30 | Customer Experience |
| O5 | Ensure 99.5% data accuracy | <0.5% error rate in validation samples | 2026-03-31 | QA Team |
2.3 High-Level Requirements
Functional:
Support 15+ document types (e.g., PDF, TIFF, email attachments).
Integrate with Guidewire ClaimCenter (ISG’s core claims system).
Human-in-the-loop for edge cases (e.g., handwritten notes).
Non-Functional:
Scalability: Handle 100,000 docs/month (2x current volume).
Security: SOC 2 Type II compliance; AES-256 encryption for PII.
Availability: 99.9% uptime (multi-region cloud deployment).
Constraints:
Budget: $1.5M (hard cap).
Timeline: 9 months (go-live by 2026-07-17).
Legacy Systems: Must coexist with AS/400 mainframe (temporary).
3. Project Management Plan (1,200 words)
Aligned with PMBOK 7’s 9 Knowledge Areas and 12 Principles.
3.1 Integration Management
Project Charter Approval:
Steering Committee: CEO, CIO, COO, Head of Claims.
Change Control Board (CCB): Meets biweekly (see Section 6.1).
Dependencies:
| Dependency | Owner | Risk if Delayed |
| Guidewire API access | IT Infrastructure | 3-week delay to integration phase |
| Training data from adjusters | Claims Operations | Model accuracy <95% |
| SOC 2 audit completion | InfoSec | Go-live blocker |
3.2 Scope Management
In-Scope:
AI models for data extraction, classification, and validation.
RPA bots for system hand-offs (e.g., AIDPP → Guidewire).
Dashboard for real-time processing metrics.
Out-of-Scope:
Replacing Guidewire (future phase).
Mobile app for adjusters (separate project).
Work Breakdown Structure (WBS):
1. Discovery & Planning
1.1 Stakeholder Analysis
1.2 Data Audit
2. Model Development
2.1 OCR Pipeline
2.2 NLP Training
3. Integration
3.1 Guidewire API
3.2 Legacy System Bridge
4. Testing & Validation
4.1 Accuracy Testing
4.2 Load Testing
5. Deployment & Training
3.3 Schedule Management (Critical Path: 9 months)
| Milestone | Target Date | Dependencies | Status |
| Data Audit Complete | 2025-11-30 | IT team access | âś… On Track |
| OCR+NLP Model v1.0 | 2026-01-15 | Training data | âš Risks |
| Guidewire Integration | 2026-03-31 | API access | ❌ Blocked |
| UAT Begin | 2026-05-01 | Model accuracy ≥95% | ⏳ Pending |
| Go-Live | 2026-07-17 | SOC 2 certification | ⏳ Pending |
Gantt Chart: (Visual placeholder—see Appendix A for full timeline.)
3.4 Cost Management
Budget Breakdown:
| Category | Estimated Cost | Notes |
| AI/ML Development | $600,000 | Vendor: DataRobot |
| RPA Licenses | $200,000 | UiPath (20 bots) |
| Cloud Infrastructure | $300,000 | AWS (us-east-1, eu-west-1) |
| Integration | $150,000 | Guidewire API + legacy bridge |
| Training & Change Mgmt | $100,000 | Adjusters + IT staff |
| Contingency (10%) | $150,000 | Risk buffer |
Cost Baseline: $1.5M (approved by CFO on 2025-10-01).
3.5 Quality Management
Quality Metrics:
| KPI | Target | Measurement Method | Frequency | Owner |
| Data Extraction Accuracy | ≥99.5% | Sample validation (1,000 docs) | Weekly | QA Team |
| Processing Time | ≤24 hours | System logs | Daily | Operations |
| User Adoption Rate | ≥90% | Training completion + usage logs | Monthly | HR |
| Fraud Detection Rate | ≥95% | False positive/negative analysis | Quarterly | Risk Team |
3.6 Resource Management
Team Structure:
| Role | FTEs | Responsibilities |
| Project Manager | 1 | PMBOK 7 compliance, risk management |
| Data Scientist | 3 | Model training/optimization |
| RPA Developer | 2 | UiPath bot development |
| Claims SME | 2 | Business rules validation |
| QA Engineer | 1 | Accuracy testing |
| Change Manager | 1 | Stakeholder communication |
Vendor Partners:
DataRobot: AI/ML platform ($600k).
UiPath: RPA licenses + support ($200k).
AWS: Cloud hosting ($300k).
3.7 Communications Management
Stakeholder Matrix:
| Stakeholder | Role | Interest | Influence | Engagement Strategy |
| CEO | Executive Sponsor | High | High | Biweekly updates |
| CIO | Technical Sponsor | High | High | Architecture reviews |
| Claims VP | Business Owner | High | Medium | UAT participation |
| IT Director | Implementation Lead | Medium | High | Daily standups |
| Adjusters (500+) | End Users | High | Low | Training + feedback sessions |
Communication Plan:
Steering Committee: Monthly (strategic decisions).
Project Team: Daily standups (15 mins).
Adjusters: Biweekly newsletters + town halls.
3.8 Risk Management
Risk Register:
| Risk | Probability | Impact | Mitigation Strategy | Owner |
| Model accuracy <95% | Medium | High | Augment training data; hire 1 additional DS | Data Science |
| Guidewire API delays | High | Critical | Escalate to CIO; parallel legacy bridge dev | IT |
| Adjuster resistance to change | Low | Medium | Pilot with 50 adjusters; incentivize adoption | Change Manager |
| SOC 2 audit failure | Medium | High | Engage 3rd-party auditor (Coalfire) early | InfoSec |
| Budget overrun | Low | High | Monthly spend reviews; contingency buffer | Finance |
3.9 Procurement Management
Vendor Contracts:
| Vendor | Service | Contract Value | SLA |
| DataRobot | AI/ML Platform | $600,000 | 99.9% uptime; 24x7 support |
| UiPath | RPA Licenses | $200,000 | 99.5% bot success rate |
| AWS | Cloud Hosting | $300,000 | Multi-AZ redundancy |
3.10 Stakeholder Management
Engagement Tactics:
Executives: Focus on ROI and competitive differentiation.
Adjusters: Highlight time savings (e.g., "Reduce manual entry by 4 hours/day").
IT Team: Emphasize career growth (e.g., AI/RPA skill development).
4. Technical Approach (400 words)
4.1 Architecture Overview
[Claims Documents] → (OCR/NLP Engine) → [Structured Data] → (RPA Bots) → [Guidewire]
↑
Human Validation
Key Components:
Ingestion Layer:
AWS Textract for OCR (handles scans, PDFs, handwriting).
Custom NLP (spaCy) for entity recognition (e.g., dates, names, amounts).
Processing Layer:
DataRobot AutoML for classification (e.g., "auto claim" vs. "health claim").
UiPath Bots for:
Data validation against business rules.
Auto-routing to adjusters.
Integration Layer:
REST API to Guidewire ClaimCenter.
SFTP bridge for legacy AS/400 systems.
Monitoring Layer:
Grafana dashboards for:
Processing time.
Error rates.
Fraud flags.
4.2 Data Strategy
Training Data:
50,000 historical claims (anonymized).
Augmented with synthetic data (e.g., edge cases like blurry scans).
Model Training:
Supervised learning (80% train, 10% validation, 10% test).
Target accuracy: 99.5% (benchmark: 98% for similar systems).
4.3 Fraud Detection
Anomaly detection (Isolation Forest algorithm) for:
Duplicate claims.
Inconsistent damage descriptions vs. photos.
Integration with LexisNexis for external fraud databases.
5. Implementation Plan (300 words)
5.1 Phased Rollout
| Phase | Duration | Activities | Success Criteria |
| Pilot | 4 weeks | 5,000 claims; 50 adjusters | ≥95% accuracy; ≤3 days/claim |
| Scale-Up | 8 weeks | 20,000 claims; 200 adjusters | ≤2 days/claim; CSAT ≥80% |
| Full Deploy | 4 weeks | 50,000 claims; all adjusters | ≤1 day/claim; CSAT ≥85% |
5.2 Training & Change Management
Training Program:
Adjusters: 2-hour hands-on session (simulated claims).
IT Team: 1-week deep dive on AI/RPA monitoring.
Executives: 30-min demo on dashboards.
Resistance Mitigation:
Incentives: "AI Champion" bonuses for top adopters.
Feedback Loops: Weekly "Pain Point" surveys.
6. Performance Monitoring & Control (300 words)
6.1 KPI Dashboard
(Sample metrics—see live dashboard in Power BI.)
| KPI | Target | Current | Trend |
| Claims Processed/Day | 2,000 | 1,200 | ↑ 15% MoM |
| Avg. Processing Time | <24 hours | 36 hours | ↓ 10% MoM |
| Data Accuracy | ≥99.5% | 98.2% | ↑ 1.1% MoM |
| Adjuster Adoption Rate | ≥90% | 75% | ↑ 5% MoM |
6.2 Change Control (7-Step Process)
Submit Request: Via Jira (form: impact, urgency, cost).
Triage: PM assesses alignment with objectives.
CCB Review: Biweekly meeting (quorum: 4/7 members).
Impact Analysis: Cost, timeline, risk assessment.
Approval/Rejection: Majority vote; escalate ties to Steering Committee.
Implementation: Assigned to owner with deadline.
Post-Change Review: Lessons learned documented.
Change Control Board (CCB):
| Name | Role | Responsibilities |
| Alex Kumar | Project Manager | Facilitate meetings; track actions |
| Lisa Chen | CIO | Technical impact assessment |
| Mark Rodriguez | Claims VP | Business priority alignment |
| Priya Patel | Data Science Lead | Model change validation |
| James Lee | Finance Director | Budget impact analysis |
Approval Criteria:
Cost Impact: <5% of budget → PM approval; >5% → CCB.
Timeline Impact: <2 weeks → PM; >2 weeks → CCB.
7. Integration Points
7.1 Systems Interfaces
| System | Integration Method | Data Flow | Owner |
| Guidewire | REST API | Structured claim data → ClaimCenter | IT |
| AS/400 | SFTP (temporary) | Legacy policy data → AIDPP | IT |
| LexisNexis | API | Fraud flags → AIDPP | Risk Team |
| AWS S3 | Native | Document storage | Cloud Team |
7.2 Process Hand-offs
Adjuster Workflow:
AIDPP flags high-risk claims → manual review.
Low-risk claims → auto-approval.
Fraud Team:
- Receives anomaly alerts → investigates within 4 hours.
8. Approval
Steering Committee Sign-Off:
| Name | Role | Signature | Date |
| Sarah Johnson | CEO | _____________ | 2025-10-20 |
| Michael Brown | CIO | _____________ | 2025-10-20 |
| Emily Davis | COO | _____________ | 2025-10-20 |
| Alex Kumar | Project Manager | _____________ | 2025-10-20 |
9. Appendices
A. Full Gantt Chart (Attached) B. Vendor Contracts (Confidential—see SharePoint) C. Data Sample (Anonymized—see Appendix C.xlsx)
đź“Ś Next Steps:
Finalize Guidewire API access (IT Director + CIO—due 2025-11-01).
Kick off data labeling (Claims SMEs—due 2025-11-15).
CCB Meeting #1 (2025-11-05: Review pilot scope).
🚀 Project Health: Green (On track; API risk being mitigated).
Document Owner: Menno Drescher | Version: 1.0 | Confidentiality: Public Previews This document follows PMBOK 7 standards and ISG’s Project Governance Policy v3.2.
Business Case: AI-Powered Intelligent Document Processing Platform (AIDPP)
Framework: PMBOK 7 | Status: Active | Priority: High Project ID: 511ecdde-f6aa-43d9-833c-1c94e8a51fcd Business Sponsor: Alex Kumar, VP of Operations, Insurance Services Group Prepared By: [Your Name], Senior Project Management Consultant Date: October 10, 2025
This document presents a quantified business case for the AI-Powered Intelligent Document Processing Platform (AIDPP), aligning with PMBOK 7 and BABOK v3 principles. All financial projections are based on conservative estimates validated by ISG’s Finance and Operations teams.
1. Executive Summary (280 words)
The AI-Powered Intelligent Document Processing Platform (AIDPP) is a $1.5M strategic initiative to transform Insurance Services Group’s (ISG) claims processing workflow. Currently, ISG manually processes 50,000+ insurance claims documents monthly, with an average cycle time of 5 days per claim, resulting in:
$3.2M annual operational costs (labor, overhead, error correction).
$1.8M annual revenue loss due to delayed claims processing and customer churn (12% attrition rate).
Regulatory compliance risks with a $500K potential fine for untimely filings (NAIC 2024 standards).
AIDPP will deploy machine learning (ML)-based optical character recognition (OCR), natural language processing (NLP), and robotic process automation (RPA) to:
Reduce claim processing time by 80% (from 5 days to <24 hours).
Cut operational costs by 60% ($1.92M annual savings).
Improve data accuracy to 99.5% (from current 85%), reducing fraudulent payouts by $400K/year.
Scale to 100,000+ claims/month without additional FTEs.
Financial Highlights (5-Year Projection):
| Metric | Current State | AIDPP (Recommended) |
| Net Present Value (NPV @ 8%) | -$12.5M (Cost of Inaction) | +$8.7M |
| Return on Investment (ROI) | N/A | 432% |
| Payback Period | N/A | 18 months |
Strategic Alignment:
Supports ISG’s 2025 Digital Transformation Roadmap (Objective 3: "Automate 70% of manual processes").
Addresses customer satisfaction KPIs (CSAT target: ≥85% by 2026).
Mitigates regulatory risks (NAIC, GDPR).
Recommendation: Proceed with Option 3 (Custom AI Platform) due to its highest NPV ($8.7M) and alignment with long-term scalability goals.
For detailed financials, see Section 4.1: Cost-Benefit Analysis*.*
2. Problem Statement
2.1 Current State Analysis
2.1.1 Process Inefficiencies
ISG’s claims processing relies on manual data entry across 3 legacy systems:
ClaimIntake (1998 COBOL system): 40% of documents require rework due to unreadable scans.
PolicyMaster (2010 Java app): No API integration; data transferred via CSV (2-hour daily batch job).
FraudCheck (3rd-party tool): Manual trigger; 30% false positives.
Key Pain Points:
| Issue | Impact | Annual Cost |
| Manual data entry | 5 days/claim cycle time | $2.1M |
| Error correction | 15% rework rate | $800K |
| Compliance delays | NAIC fines (2023: $300K) | $500K |
| Customer churn | 12% attrition (slow processing) | $1.8M |
2.1.2 Technology Gaps
No OCR/NLP capabilities: 60% of claims contain unstructured data (handwritten notes, PDFs).
Disconnected systems: Average 18 clicks per claim to toggle between systems.
No real-time analytics: Fraud detection lags by 48 hours.
2.2 Business Impact of Inaction
| Risk | Probability | Impact (5-Year) | Mitigation Cost |
| Regulatory non-compliance | High | $2.5M in fines | $1M (legal fees) |
| Customer churn | Certain | $9M lost revenue | $2M (retention programs) |
| Operational collapse | Medium | $5M (outsource costs) | $3M (emergency hiring) |
Total Cost of Inaction (5-Year): $12.5M
Data sourced from ISG’s 2024 Operational Audit and 2025 Risk Register.
3. Solution Options
3.1 Option 1: Status Quo (Do Nothing)
Description: Maintain manual processes with incremental improvements (e.g., hire 10 additional FTEs). Pros:
No upfront investment.
Familiarity for staff. Cons:
$3.2M annual operational drain.
Unable to scale beyond 60,000 claims/month.
Regulatory exposure (NAIC 2025 deadlines). Cost:
$1.5M/year (additional FTEs + overtime).
$500K/year (compliance penalties).
3.2 Option 2: Commercial Off-the-Shelf (COTS) Solution (e.g., ABBYY FlexiCapture)
Description: Deploy a pre-built document processing tool with basic AI. Pros:
Faster implementation (6 months).
Lower upfront cost ($800K). Cons:
Limited customization: 30% of ISG’s claim types unsupported.
Vendor lock-in: $200K/year licensing fees.
No fraud detection (requires 3rd-party integration). Cost: | Year | Upfront | Annual OpEx | Total (5-Year) | |------|---------|------------|----------------| | 2025 | $800K | $200K | $1.8M | | 2026-2029 | - | $200K/year | $1M |
Quantified Benefits:
$1.2M/year saved (labor reduction).
40% faster processing (3 days/claim).
3.3 Option 3: Custom AI-Powered Platform (Recommended)
Description: Build a tailored solution with:
OCR + NLP (Amazon Textract + custom ML models).
RPA bots (UiPath) for system integration.
Real-time fraud analytics (Python-based anomaly detection). Pros:
99.5% accuracy (vs. 85% manual).
80% time reduction (<24-hour processing).
Scalable to 100K+ claims/month.
Full compliance with NAIC/GDPR. Cons:
Higher upfront cost ($1.5M).
12-month implementation. Cost: | Year | Upfront | Annual OpEx | Total (5-Year) | |------|---------|------------|----------------| | 2025 | $1.5M | $300K | $3M | | 2026-2029 | - | $300K/year | $1.2M |
Quantified Benefits (5-Year):
| Benefit | Annual Value | 5-Year Total |
| Labor savings | $1.92M | $9.6M |
| Fraud reduction | $400K | $2M |
| Revenue retention | $1.8M | $9M |
| Compliance avoidance | $500K | $2.5M |
| Total | $4.62M | $23.1M |
See Section 4.1 for detailed financial comparisons.
4. Financial and Risk Analysis
4.1 Cost-Benefit Analysis (5-Year Projection)
| Metric | Option 1 (Status Quo) | Option 2 (COTS) | Option 3 (Custom AI) |
| Upfront Cost | $0 | $800K | $1.5M |
| 5-Year OpEx | $7.5M | $1.8M | $1.2M |
| Total Cost | $7.5M | $2.6M | $2.7M |
| 5-Year Benefits | $0 | $6M | $23.1M |
| Net Value | -$7.5M | $3.4M | $20.4M |
| ROI | N/A | 131% | 432% |
| NPV @ 8% | -$12.5M | $2.1M | $8.7M |
| Payback Period | N/A | 30 months | 18 months |
Key Insights:
Option 3 delivers 7x higher net value than COTS.
Breakeven in 18 months (vs. 30 months for COTS).
NPV sensitivity: Even at 12% discount rate, Option 3 remains profitable ($7.2M).
4.2 Risk Analysis
| Risk | Probability | Impact (Option 3) | Mitigation Strategy | Owner |
| AI model accuracy <95% | Medium | High | Pilot with 10K claims; iterate. | Data Science |
| Vendor lock-in (RPA tools) | Low | Medium | Open-source fallback (Robot Framework). | IT Architecture |
| Regulatory changes (NAIC) | High | High | Dedicated compliance sprint every 6 months. | Legal |
| User adoption resistance | High | Medium | Change management program (see Section 5.2). | HR |
4.3 Stakeholder Analysis
| Stakeholder | Role | Interest | Influence | Engagement Strategy |
| Alex Kumar | VP of Operations | High | High | Bi-weekly steering committee. |
| Lisa Chen | CFO | High (ROI focus) | High | Monthly financial reviews. |
| Claims Team | End Users | Medium (job fear) | Medium | Upskilling workshops (AI augmentation). |
| IT Security | Compliance | High | High | Weekly risk assessments. |
| Customers | Beneficiaries | High | Low | CSAT surveys; transparency reports. |
Stakeholder map aligned with ISG’s 2025 Communication Plan (Document ID: COM-2025-03).
5. Recommendation
5.1 Final Recommendation
Proceed with Option 3 (Custom AI Platform) based on:
Financial Superiority:
$20.4M net value (vs. $3.4M for COTS).
432% ROI (highest among options).
Strategic Alignment:
Enables ISG’s 2025 Digital Transformation Goal (70% automation).
Future-proofs for 100K+ claims/month (2027 target).
Risk Mitigation:
Pilot phase (Q1 2026) validates accuracy before full rollout.
Open-source contingencies reduce vendor lock-in.
5.2 Implementation Roadmap
| Phase | Timeline | Key Deliverables | Budget |
| 1. Discovery | Oct 2025 - Dec 2025 | Requirements finalized; vendor selected. | $150K |
| 2. Pilot | Jan 2026 - Mar 2026 | 10K claims processed; accuracy validated. | $300K |
| 3. Development | Apr 2026 - Jun 2026 | Core AI models trained; RPA bots built. | $700K |
| 4. Integration | Jul 2026 - Sep 2026 | Legacy system APIs; fraud analytics. | $200K |
| 5. Rollout | Oct 2026 | Full deployment; user training. | $150K |
Critical Dependencies:
Data availability: 90% of historical claims digitized by Dec 2025 (owned by Records Management).
IT infrastructure: Cloud migration complete (AWS, per ISG-IT-2025-08).
5.3 Success Criteria (KPIs)
| KPI | Target | Measurement Method | Owner |
| Claim processing time | <24 hours | System timestamps (Jira) | Operations |
| Data accuracy | ≥99.5% | Random sample audits (1K claims) | Quality Assurance |
| Operational cost reduction | 60% ($1.92M/year) | Finance ledger analysis | Finance |
| Customer satisfaction (CSAT) | ≥85% | Post-claim surveys | Customer Success |
| Fraud detection rate | ≥95% | False positive/negative analysis | Risk Management |
KPIs traceable to ISG’s 2025 Balanced Scorecard (Document ID: STRAT-2025-11).
6. Approval
6.1 Approval Authority
| Role | Name | Signature | Date |
| Project Sponsor | Alex Kumar | _________ | 2025-10-15 |
| CFO | Lisa Chen | _________ | 2025-10-16 |
| CTO | Raj Patel | _________ | 2025-10-17 |
| Legal | Maria Garcia | _________ | 2025-10-18 |
6.2 Next Steps
Oct 15-22, 2025: Finalize vendor contracts (AWS, UiPath).
Oct 23, 2025: Kickoff discovery phase; assign cross-functional team.
Nov 1, 2025: Submit Project Charter for CCB approval (per PMBOK 7 §4.1).
Dec 15, 2025: Present pilot results to Executive Committee.
Attachments:
Financial Model (Excel): Detailed 5-year projections.
Technical Specifications (PDF): AI/ML architecture blueprint.
Stakeholder Communication Plan: Aligned with ISG-COM-2025-03.
This business case complies with PMBOK 7 §1.2.6 (Business Value) and BABOK v3 §4.1 (Determine Value)*. All projections are conservative and audited by ISG Finance (Sept 2025).*
