BlueAlly

Use Case Explorer

Detailed breakdown of 8 priority AI use cases with KPIs and financial benefits

Total Use Cases

8

High Priority

4

Total Annual Benefit

$5.6M

Avg. Payback

1.6 yrs

HIGH PRIORITYUC-001
Automated Network Maintenance & Testing
Deploy AI agents to execute test calls across all network types (PIP, IDA, IVR, DS-CAP) with real-time MOS/jitter metrics, reducing manual testing by 75% and widening coverage to 100%.
Decrease CostReduce Risk

Current State Challenges

1

Manual test execution limited to PIP network only

2

No MOS or jitter metrics collection

3

Each maintenance window requires 2 engineers for 4+ hours

4

Limited test coverage across network types

AI-Enabled Future State

1

AI agents execute tests across all network types automatically

2

Real-time MOS/jitter metrics with anomaly detection

3

Single engineer oversight with 75% time reduction

4

100% network coverage with continuous monitoring

All Use Cases by Priority

High Priority

4 use cases
UC-001

Automated Network Maintenance & Testing

Deploy AI agents to execute test calls across all network types (PIP, IDA, IVR, DS-CAP) with real-time MOS/jitter metrics, reducing manual testing by 75% and widening coverage to 100%.

Implementation Cost

$350K

3-Year NPV

$2.1M

Launch Date

Q2 2025

Annual Benefit

$576,000

Decrease CostReduce Risk
UC-002

AI-driven Incident Management & Root-Cause Analysis

Introduce an AIOps platform to detect anomalies, correlate events, diagnose root causes with 95% accuracy, and orchestrate remediation, reducing MTTR by 80%.

UC-004

Business Case Development & ROI Modelling

Build a data-driven framework with cost models and ROI projections to justify AI investment, demonstrating $10M+ cost avoidance over three years.

UC-005

Tribal Knowledge Capture & Retrieval (RAG)

Create a knowledge repository using RAG so engineers and AI assistants can query institutional memory on demand, reducing onboarding time by 50%.

Medium Priority

4 use cases
UC-003

Enterprise AI Infrastructure & Lab

Build an AI lab with HPE/NVIDIA or Kamiwaza stacks, ensuring enterprise-grade support, scalability, and security for all AI initiatives.

UC-006

Predictive Maintenance & Resource Allocation

Apply ML models to network performance data to predict failures and schedule maintenance proactively, reducing emergency repairs by 70%.

UC-007

Data Volume & Infrastructure Planning

Assess data volume, velocity and variety to size storage, compute and networking correctly, reducing estimation errors by 75%.

UC-008

Call-Quality Monitoring & MOS/Jitter Analysis

Integrate MOS and jitter measurements into network tests with real-time VoIP quality monitoring, improving MOS from 3.5 to 4.0.