Strategic Deployment of an AI-Based Network Operations Center (AI-NOC) in a Public Higher Education Institution
Faculty Advisor:
Charla Griffy-Brown
Marco Serrato
Year:
2026
Introductory Excerpt
Higher education institutions now operate as mission-critical digital enterprises. Network reliability is no longer a back-office concern — it is the operational infrastructure that sustains academic continuity, enables research, and shapes the student experience. Yet the systems responsible for protecting that reliability — Network Operations Centers (NOCs) — remain largely manual, reactive, and constrained by the pace of human cognition and staffing capacity.
This Strategic Implementation Report documents the design, governance, active pilot deployment, and institutional integration of an AI-assisted triage model within Arizona State University's (ASU) Digital Infrastructure organization — one of the largest and most complex public university technology environments in the United States. The initiative, instantiated through a purpose-built AI system named ANNA (Arizona State University Network and Nodes Assistant), represents not merely a technology deployment but a governed sociotechnical transformation: a deliberate redesign of how humans and AI systems collaborate to protect institutional reliability at scale.
The central contribution of this work is a replicable model for AI deployment in public-sector digital infrastructure — one that integrates three interdependent outcomes: measurable operational performance improvement, governance integrity within public accountability structures, and sustained human trust and agency in AI-augmented workflows. This model advances the field by demonstrating that these outcomes are not in tension; they are mutually reinforcing when approached through disciplined human-AI collaboration design.
The report evaluates performance across quantitative metrics — including Time-to-Resolution (TTR), escalation rates, engineering hours recovered, and system uptime — alongside qualitative measures of staff trust, adoption confidence, and perceived agency. If the initiative achieves its primary target of a 40% reduction in TTR, the institutional impact extends far beyond faster ticket processing: it represents a structural shift in how ASU's technology organization allocates human expertise, manages risk, and positions itself for the next generation of intelligent infrastructure.
Evidence in this report falls into four categories: (1) Measured results — data drawn directly from ASU's ITSM platform during the active pilot period; (2) Pilot-measured results — outcomes from the Phase 3 twelve-week pilot, now being extended through Phase 4; (3) Benchmark-estimated values — figures modeled against published enterprise benchmarks and disclosed as estimates; and (4) Projected targets — forward-looking goals for Phase 4 full deployment and beyond. Each category is labeled in the data tables and referenced in the prose. ANNA is currently in active pilot deployment at ASU; results presented here reflect that status.
The findings carry transferable significance. The triage bottleneck confronted at ASU is not a higher education anomaly — it is a structural challenge shared by healthcare systems, government IT agencies, and large enterprise infrastructure teams worldwide. The governance architecture, trust-calibration methodology, and phased deployment model developed here are designed to be adapted across any high-volume, accountability-driven operational environment.
This Strategic Implementation Report documents the design, governance, active pilot deployment, and institutional integration of an AI-assisted triage model within Arizona State University's (ASU) Digital Infrastructure organization — one of the largest and most complex public university technology environments in the United States. The initiative, instantiated through a purpose-built AI system named ANNA (Arizona State University Network and Nodes Assistant), represents not merely a technology deployment but a governed sociotechnical transformation: a deliberate redesign of how humans and AI systems collaborate to protect institutional reliability at scale.
The central contribution of this work is a replicable model for AI deployment in public-sector digital infrastructure — one that integrates three interdependent outcomes: measurable operational performance improvement, governance integrity within public accountability structures, and sustained human trust and agency in AI-augmented workflows. This model advances the field by demonstrating that these outcomes are not in tension; they are mutually reinforcing when approached through disciplined human-AI collaboration design.
The report evaluates performance across quantitative metrics — including Time-to-Resolution (TTR), escalation rates, engineering hours recovered, and system uptime — alongside qualitative measures of staff trust, adoption confidence, and perceived agency. If the initiative achieves its primary target of a 40% reduction in TTR, the institutional impact extends far beyond faster ticket processing: it represents a structural shift in how ASU's technology organization allocates human expertise, manages risk, and positions itself for the next generation of intelligent infrastructure.
Evidence in this report falls into four categories: (1) Measured results — data drawn directly from ASU's ITSM platform during the active pilot period; (2) Pilot-measured results — outcomes from the Phase 3 twelve-week pilot, now being extended through Phase 4; (3) Benchmark-estimated values — figures modeled against published enterprise benchmarks and disclosed as estimates; and (4) Projected targets — forward-looking goals for Phase 4 full deployment and beyond. Each category is labeled in the data tables and referenced in the prose. ANNA is currently in active pilot deployment at ASU; results presented here reflect that status.
The findings carry transferable significance. The triage bottleneck confronted at ASU is not a higher education anomaly — it is a structural challenge shared by healthcare systems, government IT agencies, and large enterprise infrastructure teams worldwide. The governance architecture, trust-calibration methodology, and phased deployment model developed here are designed to be adapted across any high-volume, accountability-driven operational environment.
