Accelerating Incident Diagnosis and Service Recovery Through Generative AI-Assisted Failure Intelligence in Critical Digital Infrastructure
DOI:
https://doi.org/10.63125/61etqg30Keywords:
Generative Artificial Intelligence, Failure Intelligence, Root Cause Analysis, Incident Diagnosis, Service RecoveryAbstract
Critical digital infrastructure increasingly depends on cloud platforms, telecommunications systems, enterprise digital services, distributed applications, data centers, and interconnected computing environments, yet incident-response teams often struggle to interpret the large volumes of fragmented logs, alerts, metrics, traces, configuration records, and service-dependency information generated during failures. This study examined whether Generative AI-assisted failure intelligence can accelerate incident diagnosis and improve service recovery performance in critical digital infrastructure. The study adopted a quantitative, cross-sectional, case-based research design involving cloud and enterprise cases across cloud platforms, telecommunications systems, enterprise digital services, distributed computing environments, data centers, and other mission-critical infrastructures. Using purposive sampling, data were collected from Site Reliability Engineers, DevOps engineers, cloud engineers, network engineers, platform engineers, infrastructure specialists, systems engineers, AIOps professionals, incident managers, system architects, and technical managers. Of 320 questionnaires distributed, 298 were returned and 286 were valid and usable, producing an 89.4% valid response rate. The four explanatory variables were Generative AI-Assisted Failure Data Interpretation, Generative AI-Enabled Root Cause Analysis, Context-Aware Incident Knowledge Synthesis, and Generative AI-Assisted Recovery Recommendation, while Incident Diagnosis and Service Recovery Performance was the dependent variable. Analysis included descriptive statistics, Cronbach’s alpha, KMO and Bartlett’s tests, Pearson correlation, multiple regression, ANOVA, VIF, tolerance, Durbin-Watson testing, and hypothesis evaluation. Incident Diagnosis and Service Recovery Performance Recorded M = 4.19, SD = 0.53. Generative AI-Enabled Root Cause Analysis showed the strongest correlation, r = .76, p < .001, and the largest regression effect, β = .33, p < .001, followed by Recovery Recommendation, β = .29, Knowledge Synthesis, β = .24, and Failure Data Interpretation, β = .22. The overall model explained 70.8% of performance variance, R² = .708, Adjusted R² = .704, F (4, 281) = 170.35, p < .001. These findings indicate that organizations should prioritize evidence-grounded Generative AI systems that integrate causal reasoning, contextual knowledge, observability data, and validated recovery guidance to strengthen diagnostic accuracy, reduce incident uncertainty, and support faster and more reliable service restoration.
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