Slow is the New Down


Root-Cause Analysis

US Bank

Alert Correlation

Barclay's Bank

End-to-End Monitoring

The Future of AIOps

Empowering User Experience

With AIOps

Firsthand Insights From Leading Companies

Empowering User Experience

With AIOps

Firsthand Insights From Leading Companies

Empowering User Experience

With AIOps

Firsthand Insights From Leading Companies

Slow is the New Down

In today’s digital economy, you’re one poor user experience away from a lost customer. Downtime is costly, and slow is the new down.

As you increase the volume, velocity and variety of data, it gets more difficult for your IT operations team to manually correlate it, analyze it, and solve any problems that arise before they impact user experience. This is especially true for companies with tools dispersed across different data siloes.


In response, AI and machine learning have emerged as a means of relieving some of the manual intervention required to maintain effective IT operations and protect the user experience. This shift is key going forward, because some of the main threats to user experience call for automated actions and decision-making processes that are just beginning to gain adoption. The challenges include:

Proliferation of
monitoring tools

of IT organizations rely on up to nine different IT monitoring tools to support modern applications 1

Need for
predictive analytics

of companies say the most important AIOps capability is analytics that predict probable future events that could impact availability and performance 2


of companies experience over 50,000 alerts on average per month 3

In the pages to come, you’ll learn about how industry-leading companies are using machine learning and AI to tackle some of the biggest challenges in IT Ops today, including:

Root-Cause Analysis
Alert Correlation
End-to-End Monitoring


How AIOps Accelerates Root-Cause Analysis

At a time when you place such a high premium on user experience, rapid root-cause analysis has never been more important. But it’s made difficult by the proliferation of disparate tools and data siloes, which can make it hard for you to efficiently understand the source of an issue. With AI and machine learning, IT teams can:


•  Understand the root cause of a problem affecting one or more services
•  Contextualize the information relevant to the issue at hand
•  Execute appropriate remediation to minimize impact on user experience

Learn how IntelliNet, a managed service and cloud solutions provider, has made an early investment in AI and machine learning to improve user experience by speeding up root-cause analysis and mean time to repair.

U.S. Bank

Reducing Correlation Pains With AI and Machine Learning

As data increases in volume and complexity, so does the challenge of manually correlating alerts and resolving them before user experience suffers. No wonder that correlation takes up such a large part of the triage process with many IT Ops teams. Which of these top monitoring challenges have you struggled with?

Detecting Issues


of IT leaders say the same

Collaboration Across


of IT leaders identify collaboration as a critical challenge

Alert Correlation Across

All Tools

of IT leaders cite alert correlation as a critical challenge)

Learn how US Bank is using AI and machine learning to analyze large, monitoring-driven data sets to make alert correlation a smaller part of their triage process and deliver better up time today.


More Visibility, Better Experiences

New distributed and microservice-style architectures bring more complexity and new monitoring challenges with them. The use of disparate monitoring tools makes it difficult to get end-to-end visibility across an entire IT estate. And that means it’s even harder to identify and negate issues before they impact up time and user experience.

What AIOps Capabilities do You See as Most Important?

Predictive Analytics

agree that predicting probable future events that may impact availability and performance is the most important AIOps capability

Service Analytics

agree that highlighting the potential impact to key services is the most important AIOps capability

Business Value Dashboards

agree that analyzing both IT and business data showing patterns of behavior to detect positive business outcomes is the most important AIOps capability

Learn how Barclay’s Bank believes AI and machine learning will play a key part in automation by requiring you to look at your full infrastructure, end-to-end, and the various monitoring tools you’re using across that application stack, to create optimal user experiences.

The Future of AIOps

Today, 97 percent of executives are investing in building or launching big data and AI initiatives.4

That’s because if you want to not only survive but thrive in today’s digital economy, you must consider the use of AI in IT operations. Now is the time to start assessing and implementing AIOps-powered solutions to drive the superior user experiences your customers have not only come to expect, but will increasingly demand.

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1-3 TechValidate, AIOps IT industry survey, May 2018

4 Randy Bean, MIT Sloane Management Review, “How Big Data and AI Are Driving Business Innovation in 2018,” February 2018