CASE STUDY
/Infrastructure
£4.5M
aggregate value potential across portfolio
£2.9M
mean ROI for highest-priority use cases
79.1%
data readiness — strong implementation success indicators
70%
timeline compression, 96% cost savings versus traditional consulting
Negative-ROI initiatives identified pre-implementation, preventing failures.
40+ hours every week. That’s how much time a critical national infrastructure agency’s network engineers spent fighting incidents rather than building infrastructure. For an organisation managing security operations, processing systems, and operational coordination for a national population, this wasn’t sustainable.
Critical outages lasted hours, not minutes. Network capabilities sat underutilised while legacy systems strained beneath operational demands. Engineering teams were exhausted — departing for less stressful roles, taking institutional knowledge with them.
Leadership understood the risk: service interruptions compromised critical government functions. Rising operational costs eroded already constrained budgets. The 70% industry failure rate on network automation projects had created organisational risk aversion.
Traditional consulting alone offered no solution. £100–250K budgets, 12–16 week timelines, subjective assessments disconnected from operational reality.
The infrastructure agency pioneered government sector adoption of AI-powered infrastructure use case prioritisation — representing the inaugural public sector deployment of machine learning-driven strategic planning.
Engineering Workflow Analysis — Quantified precisely where technical capacity was consumed: reactive incident management versus strategic network advancement.
Government Infrastructure Scoring — Frameworks emphasising service reliability protection for mission-critical systems, data platform integration, and cost reduction within constrained public sector budgets.
Regulatory Validation — Embedded compliance ensuring data protection adherence, security clearance protocols, and public sector procurement standards.
Six-Week Execution — Delivered in 6 weeks what traditionally required 12–16 weeks and 96% more cost.
THE INSIGHTS
From 8 evaluated use cases, MountainsAI identified which investments would free capacity, reduce incidents, and enable strategic network evolution — sequenced through the Net Prioritisation Score (NPS):
Intelligent Network Fabric Management (NPS 66.2): Automated network configuration oversight with drift detection. Directly attacks priority-one incident frequency consuming 40+ engineering hours weekly.
AI Network Assistant (NPS 62.4): Natural language interface indexing network architecture documentation. Compresses troubleshooting from hours to minutes.
Network Operations Automation (NPS 61.2): Workflow automation for repetitive operational tasks plus predictive maintenance scheduling.
AI Ops Platform (NPS 46.5): Unified diagnostic intelligence mining historical incident archives. Reveals systemic patterns enabling preventative interventions.
CONCLUSION
The critical infrastructure agency’s engagement delivered more than use case prioritisation. It provided resolution to competing modernisation demands, evidence-based investment validation protecting public capital, and operational focus on initiatives demonstrably protecting service continuity for critical government functions.
Most significantly: organisational reorientation from perpetual incident response toward proactive infrastructure enhancement.
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