Eliminate the Dreaded 2:00 a.m. Call

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Eliminate the dreaded 2:00 a.m. Call

We had the opportunity to meet Gabby Nizri, CEO and Yaron Levy, CTO of Ayehu during the IT Press Tour in Silicon Valley. Ayehu’s objective is to eliminate the 2:00 a.m. phone calls IT operations receive every night with IT automation and orchestration powered by AI. They believe humans do not scale, but automation does.

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Gartner reports that 85% of service desk spend is on personnel. Ayehu strives to replace that with virtual operators — a force multiplier for IT operations. They believe automation combined with smart machine technologies will be a key driver in shaping the future of the IT service industry.

The Ayehu intelligent automation platform is:

  • Simple: codeless, easy to use, fast deployment
  • Intelligent: ML-driven decision support
  • Scalable: highly scalable, supports thousands of automated workflows
  • Integrated: powerful interoperability across IT and security solutions

Automation is hard to rip out once it is successful. Ayehu’s retention rate is 94% and a large percentage of clients are in financial services and insurance. As such, security is a concern.

Ayehu is able to provide zero-touch from alert to resolution in less than 5 seconds with automated incident response including capture, triage, enrich, and communication. Resolution and remediation are automated. Every action is validated, audited, and documented so every step is known. Users are able to remove 80% of L1 activities, improve FTE efficiency by 40%, achieve 98% faster MTTR, and deliver 35% cost saving.

Ayehu integration hub enables automation and integrates with best-of-breed applications and systems including IT service management, chatbots, and AI, monitoring systems, cybersecurity, infrastructure and cloud, and messaging and notification. It acts as the federated integration hub, streamlining automated processes across enterprise systems and applications. The GitHub community includes expansion to other communities for developers to scale.

In a case study with a leading financial service company, Ayehu delivered a 40% productivity gain with improvement in mean-time-to-resolution and recover, 15% savings by the end of the first year, 90% improvement in response time, 85% improvement in turnaround time, 10% resource optimization, and 23% demand reduction by reducing 60% of the alerts.

The business challenge they saw was high operating costs for monitoring maintenance and production support. Increases in the cost of delivery due to increased volume of incidents and events. An opportunity to identify tools for rationalization and process optimization.

The environment in the financial services company includes 60,000 servers, 10,000 database instances, 72 PB storage, 21,000 middleware instances, 23 LPARS mainframe, with 480 applications being supported.

Further Reading

Bringing AI to Automated Testing

Software Test Automation and AI

This UrIoTNews article is syndicated fromDzone