Making a Practical Case for AI in the Enterprise

Futuristic graphical user interface concept.

What happens when a server goes down? The average cost for one hour of downtime is between $300,000 and $400,000 for 25% of U.S. companies, according to Statista. That downtime price tag can rise to a stunning $5 million for 15% of organizations. And the costs don’t stop there; it may take hours and multiple people to identify the root cause and remediate a service incident.

To meet the fast pace of business, organizations must be able to proactively and predictively monitor applications and infrastructure. A tall order in today’s complex IT environment.

“IT operations is challenged by the rapid growth in data volumes generated by IT infrastructure and applications that must be captured, analyzed, and acted on,” said Padraig Byrne, Senior Director Analyst at Gartner. “Coupled with the reality that IT operations teams often work in disconnected silos, this makes it challenging to ensure that the most urgent incident at any given time is being addressed.”

That’s why the use of artificial intelligence in IT operations (AIOps) is now getting a chapter in organizational playbooks.

AIOps and its use cases

Gartner refers to AIOps as an operations platform that uses machine learning to continuously monitor and analyze data collected from multiple IT operations tools and devices. The platform uses these insights to automatically identify and respond to incidents, while yielding continuous improvements.

The benefits are substantial; a Boston Consulting Group survey of 112 CIOs found that AIOps can create significant cost and performance efficiencies, while driving rapid innovation without sacrificing security, stability, or service. Implementation of these new technologies lead to prime advantages like enhanced visibility across IT environments and better prioritization of urgent issues.

Practical use cases for AIOps are many; event noise reduction is one example. It’s not unusual for IT teams to have thousands of events each day coming into their Operations Center, all of which must be investigated, triaged, or touched by an IT professional. In many cases, the volume is beyond human scale to manage.

Continuous monitoring in conjunction with machine learning can distinguish real incidents from false alarms, and reduce the number of events that IT teams must touch. This ultimately helps to prioritize business-critical issues, while cutting costs and saving valuable time.

Other use cases include:

  • Predictive alerts: In addition to distinguishing unusual behavior or incidents, AIOps does so faster. Machine learning algorithms sift through data at scale to identify patterns and find anomalies more quickly than humans.
  • Root cause analysis: Traditional root cause analysis may take six, seven, or eight people multiple hours to determine the fault or problem. The continuous insights and correlations across many data points provided by AIOps enables fast, accurate root cause analysis that significantly speeds mean time to repair (MTTR).
  • Proactive service resolution: Predictive and fast detection — combined with automated processes between IT operations and the service desk — enables a proactive approach to resolving issues, leaving IT to focus on business initiatives as opposed to manual incident response.

Where to start

Whether it’s to gain greater visibility into service incidents, avoid downtime, or improve IT service performance, “companies should first look at the key pain points they wish to solve with AIOps,” says Monica Brink, Director of Solutions Marketing, BMC.

For example, BMC Helix Monitor provides a single pane of glass across hybrid infrastructures for intelligent monitoring and event management. Using machine learning and analytics combined with broad IT operations management capabilities, it offers a 360-degree view of the health and performance of IT systems and applications to meet the speed and quality demands of today’s digital businesses.

Also, BMC Helix Monitor can be quickly deployed in a software-as-a-service model, which reduces infrastructure costs while offering elastic scalability. Plus, there’s no need to hire staff skilled in AI for implementation, says Brink. “Many organizations think they need a data scientist to help deploy AI solutions, but all that intelligence is built right into BMC Helix Monitor, enabling IT to focus on deploying those valuable AIOps use cases.”

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