Nexign: Russian Businesses Slow to Adopt AI for Anomaly Detection
A study by Nexign reveals that while 79% of Russian companies use IT infrastructure monitoring tools, only 17% employ machine learning models for anomaly detection. Key barriers include a lack of internal expertise, poor data quality, and difficulty in calculating ROI. The financial sector shows more interest, while small and medium businesses lag.
Nexign's research shows that IT infrastructure monitoring has become standard for Russian business, with 79% of surveyed companies using specialized software to track system status. The most in-demand metrics include CPU, memory, and network load (89%), application errors and failures (86%), and service response delays (70%). However, only 17% use machine learning tools for automatic anomaly detection, despite acknowledging consequences like regulatory fines, reputational damage, and customer churn. The financial sector is more proactive, running pilot projects, while other industries adopt a wait-and-see approach, and small businesses are least prepared, partly due to lacking basic monitoring processes. Experts note that the barrier is organizational and economic rather than technological. Available ML tools exist, but implementation requires mature processes, specialized staff, and high-quality normalized data. Additionally, many projects fail to get funding due to difficulty calculating economic benefits. Maxim Nartov, Business Development Director at Nexign, emphasizes that as IT landscapes become more complex with more services and distributed infrastructure, traditional anomaly detection becomes harder, and machine learning integration into monitoring platforms will deepen in coming years.
- Abbreviations
- ML = Machine Learning — Машинное обучение
- IT = Information Technology — Информационные технологии
- CPU = Central Processing Unit — Центральный процессор
- ROI = Return on Investment — Окупаемость инвестиций
Source: CNews —
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