BlueAlly

Executive Summary

Strategic overview of AI-enabled network operations transformation

AI Transformation Business Case

This analysis presents a comprehensive evaluation of AI-enabled network operations transformation, identifying key use cases, quantifying financial benefits, and providing a roadmap for implementation. The findings demonstrate significant potential for cost reduction, risk mitigation, and operational efficiency gains.

3-Year NPV

$10.5M

IRR

252.4%

Payback

0.4 years

BCR

3.89x

Business Driver Alignment

Grow Revenue

Increase top-line revenue through new capabilities

Use Cases1

Decrease Cost

Reduce operational and labor costs

Use Cases7

Increase Cash Flow

Improve working capital and cash position

Use Cases3

Reduce Risk

Minimize outage and compliance risks

Use Cases6

Strategic Initiatives

Automated cross-network testing platform

Decrease CostReduce Risk
Confidence:
90%

AIOps-based incident management

Reduce RiskDecrease Cost
Confidence:
85%

Enterprise AI infrastructure & lab

Decrease CostGrow Revenue
Confidence:
75%

Business case development & ROI modelling

Increase Cash FlowReduce Risk
Confidence:
80%

Tribal knowledge capture & RAG integration

Decrease CostIncrease Cash FlowReduce Risk
Confidence:
70%

Predictive maintenance & AI forecasting

Decrease CostReduce Risk
Confidence:
60%

Data volume & infrastructure planning

Decrease CostIncrease Cash Flow
Confidence:
65%

Team & skill development programme

Decrease CostGrow Revenue
Confidence:
70%

Call-quality monitoring & metrics integration

Reduce RiskDecrease Cost
Confidence:
75%

Friction Point Inventory

Delays and Handoffs
2 friction points identified

Manual test execution across networks

Severity 4/5
IT Operations•Monthly

Multiple engineers per maintenance window

Severity 3/5
IT Operations•Weekly
Data Silos
1 friction points identified

PIP, IDA, IVR, DS-CAP networks not integrated

Severity 4/5
Network Engineering•Ongoing
Knowledge Gaps
2 friction points identified

Lack of understanding of data volumes and integration

Severity 3/5
Data Architecture•Per project

Tribal knowledge concentrated in senior engineers

Severity 5/5
Knowledge Management•Ongoing
Low-Value Work
1 friction points identified

Repetitive manual testing with limited tool features

Severity 3/5
IT Operations•Daily
Key Findings & Recommendations

Opportunities

  • Automated Network Testing: Deploy AI agents to execute test calls across all network types with real-time MOS/jitter metrics, reducing manual testing by 75% and widening coverage to 100%.
  • AIOps Incident Management: Introduce an AIOps platform to detect anomalies, correlate events, and diagnose root causes with 95% accuracy, reducing MTTR by 80%.
  • Tribal Knowledge Capture: Create a RAG-based knowledge repository to preserve institutional memory and reduce onboarding time by 50%.
  • Predictive Maintenance: Apply ML models to predict failures and schedule maintenance proactively, reducing emergency repairs by 70%.

Considerations

  • Data Integration: Current network systems (PIP, IDA, IVR, DS-CAP) operate in silos. Integration effort required for unified AI platform.
  • Change Management: Transition from manual to AI-assisted operations requires training and cultural adaptation across engineering teams.
  • Infrastructure Investment: Initial capital expenditure of ~$800K for HPE/NVIDIA hardware stack with 3-year refresh cycle.
  • Skill Development: Need to build cross-functional team with AI/ML expertise (4-6 FTEs) through hiring and upskilling.
Implementation Roadmap
Phased approach to AI transformation over 24 months
1
Months 1-6Foundation & Quick Wins
  • • Business case development & ROI modeling
  • • AI infrastructure planning & procurement
  • • Tribal knowledge capture pilot
  • • Team formation & training initiation
2
Months 7-12Core Implementation
  • • Automated network testing deployment
  • • AIOps incident management rollout
  • • RAG knowledge base expansion
  • • Integration with existing monitoring tools
3
Months 13-18Advanced Capabilities
  • • Predictive maintenance models
  • • Call quality monitoring with MOS/jitter
  • • Cross-network automation expansion
  • • Performance optimization & tuning
4
Months 19-24Optimization & Scale
  • • Full automation of maintenance windows
  • • Self-healing network capabilities
  • • Continuous improvement processes
  • • ROI validation & reporting