Case Study: AI & BI-Powered Enterprise Risk Management

Introduction

Enterprise Risk Management (ERM) requires a proactive approach to identifying, analyzing, and mitigating risks at both strategic and operational levels. The integration of Artificial Intelligence (AI) and Business Intelligence (BI) empowers organizations to handle both qualitative (unstructured text) and quantitative (structured data) risks with precision.

This help center guide provides a detailed walkthrough for deploying an AI- and BI-enabled risk management solution that processes SharePoint data, uses Power BI dashboards, and leverages AI agents for risk impact forecasting.

Step-by-Step Workflow

Phase 1: Qualitative Risk Analysis (Text-Based)

1. Source System Setup

• Platform: SharePoint document libraries, Outlook Emails, MS Teams chat transcripts

• Integration Tools: Microsoft Graph API, Power Automate flows

2. Data Extraction & Cleaning

• Extract text content from documents

• Preprocess using tokenization, lemmatization, and noise removal

3. Outlier Detection & Risk Signal Extraction

• Apply NLP models to detect:

  • Anomalous documents with dense risk language

  • Repeated use of terms like “delay”, “overrun”, “blocker”

4. Categorization Methods

• Sentiment Analysis: Classify sentiment into Positive, Neutral, Negative

• Keyword Extraction: Word clouds highlighting risk terms

• Risk Urgency Statements:

  • Benchmarked against training labels:

    • "High Alert", "Urgent attention needed" ➔ High Urgency

    • "Monitor regularly", "Pending Review" ➔ Medium/Low Urgency

5. Risk Classification Outputs

• Risk Type: Schedule / Cost / Resource / Technical / Compliance

• Risk Source: Internal / External / Vendor / Regulatory

• Severity Score: Scale of 1–5

• Tags: Auto-tagged metadata

Phase 2: Quantitative Risk Analysis (Structured Data)

1. Data Collection

• Sources: ERP, Financial Dashboards, HR Systems, Excel Logs, Project Management Tools

• Connector: Power Query for data integration into Power BI

2. AS-IS Dashboard in Power BI

• KPIs:

  • Cost Variance (CV) = Earned Value - Actual Cost

  • Schedule Performance Index (SPI) = Earned Value / Planned Value

  • Resource Utilization

• Visuals:

  • Risk histograms

  • Control charts

  • Forecast bands

3. Performance Thresholds

• Highlight anomalies via:

  • Conditional formatting

  • RAG status indicators (Red/Amber/Green)

Phase 3: AI Risk Agent Integration

1. Merging Qualitative + Quantitative Data

• Combine structured KPIs with tagged risk documents using a unified risk ID

2. AI Decision Engine

• Use models like:

  • Decision Trees for cause-effect tracing

  • Bayesian Networks for dependency probability

  • Monte Carlo Simulation for probabilistic forecasting

3. Impact Evaluation

• Cost Impact: Quantified in monetary units

• Schedule Impact: Days/weeks delay

• Resource Impact: Additional effort or FTEs

4. Strategic Risk Mapping

• Generate Enterprise Risk Heatmaps with axes:

  • Impact (Low to Catastrophic)

  • Likelihood (Rare to Frequent)

Continuous Learning & AI Agent Evolution

1. Feedback Loop

• Evaluate the effectiveness of mitigation actions

• Re-train AI agent using updated risk logs and closed project reports

2. Drift Detection

• Detect sentiment, topic, or severity drift across time

3. Ontology Update

• Expand classification ontology with emerging risk types and sources

Sample Datasets

A. Qualitative Dataset

B. Quantitative Dataset

Use Cases

• Real-time risk alerts for executive dashboards

• Automated risk reports from document ingestion

• Strategic planning simulations for large-scale initiatives

Downloadable Resources

• Sample Risk Analysis Dataset

• Sample KPI Dashboard Dataset

Final Note

This AI & BI framework is scalable, adaptive, and enterprise-ready. By merging unstructured insights with structured metrics, it creates a strategic risk nervous system that is always learning and evolving.