Stuck in “Firefighting Mode”


How AIOps Predicts Issues Before They Arise

William Hill

Root-Cause Analysis and Self-Healing


Reducing the Noise

The Future of AIOps

Improving IT Efficiency

With AIOps

Empowering User Experience

With AIOps

Firsthand Insights From Leading Companies

Improving IT Efficiency

With AIOps

Stuck in “Firefighting Mode”

In the application economy, you’re under pressure to deliver optimized service all the time. And when you’re slow today, you might as well be down, because both are costing you users and money.


But as vital as optimizing service levels is, it’s proving elusive as IT teams struggle to spot and fix performance issues before they do harm. Fundamentally, it’s an IT efficiency challenge. And, as IT environments get more dynamic and complex, that challenge only gets harder to solve.

Some potential efficiency hurdles include:

of issues

Rather than spotting troubling trends and addressing them proactively, operations teams are playing from behind.

of issues are first spotted by users, According to IDG Research.1

The rise of
hybrid environments

To run modern hybrid environments, IT operations teams have continued to add monitoring tools.

of IT organizations rely on up to nine different monitoring tools to support modern applications 2


IT operations teams struggle with thousands of alerts that are often inaccurate and redundant.

of IT organizations receive more than 10,000 alerts per month 3

In the pages to come, you’ll learn how industry-leading companies are using machine learning and AI to break out of “firefighting mode” and overcome some of the biggest obstacles to IT efficiency today. You’ll learn how these companies are:

Predicting Issues and optimizing resources proactively
root-cause analysis
false alerts


How AIOps Predicts Issues Before They Arise

That’s how much organizations lose for each hour of downtime.4

And that’s just the average. From airlines having to cancel thousands of flights to banks incurring heavy fines for systems failures, the business world is littered with high-profile horror stories of companies losing much more than that.

The inability to predict interruptions and deal with them proactively means you lose time and money that could be better spent on activities more valuable to your customers. With AI and machine learning, IT teams can spot issues before they arise—and before their users do—with predictable, actionable intelligence that:

Alerts you
to potential bottlenecks or
abnormal operations patterns


Delivers predictive insights into utilization across physical, virtual, cloud and mainframe environments, detecting hot spots and waste with minimal effort

Simulates “what if” scenarios
to test different models and run your data center more efficiently


Learn how KPN, a mobile telecommunications company, is using AI and machine learning to speed up remediation and improve operational efficiency.

William Hill

Root-Cause Analysis and Self-Healing in an Age of Data Explosion

Initiatives like multi-cloud deployments, microservices development, and Internet of Things (IoT) implementation have upped the ante on operational efficiency and agility. These complex, hybrid environments are generating an explosive growth in the volume, variety, and velocity of data, and IT teams are struggling to keep pace. When it comes to resolving issues, mean time to repair takes 4.5 hours on average.5

Which of the following AIOps-related business outcomes is the most important for your organization?

IT Efficiency

agree that increasing IT efficiency with AIOps for modern architectures is the most important business outcome 6

Optimizing the
user experience

agree that delivering superior user experiences with predictive analytics is the most important outcome 7


agree that speeding up innovation and collaboration with AI-driven BizDevOps is the most important outcome 8

Discover how William Hill, one of the world’s leading betting and gaming companies, is using AI and machine learning to analyze large volumes of digital data, speed up root-cause analysis and self-healing, and gain visibility across their delivery chain.


Reducing the Noise

Disjointed toolsets are also hindering agility when it comes to another IT efficiency challenge—the tens of thousands of alerts that are often inaccurate and redundant. In fact, 31 percent of alarms generated are false.9

When a business starts to show performance issues, operators struggle to determine why. Waves of false or redundant alerts make it difficult for operators to filter out the noise—which is problematic when a single issue may be the culprit.

With AIOps, you can leverage algorithmic insight to:

Cluster related and
relevant alerts to improve
detection of critical issues

Suppress noise
and duplicate alerts


Speed up
time-to-repair and

Discover how global beauty company Coty is using AIOps to minimize alerts and gain single-pane visibility across its IT estate.

AIOps and the Self-Driving Infrastructure: The Next Phase of IT Efficiency

Today, 90 percent of IT organizations agree that AIOps is very important for the future of IT operations.10

No doubt, the realization is growing that achieving IT efficiency in an application economy means breaking your reliance on disjointed point-monitoring tools and the time-consuming effort they demand. Today, IT efficiency requires a self-driving operational infrastructure—one that delivers insights to automation tools and enables automated remediation. That’s how your organization can maximize operational efficiency and protect user experience.

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1 IDG, “IDG Quick Pulse: State of IT Operations and Analytics,” February 2018,

2, 3 TechValidate, AIOps IT industry survey, May 2018

4 David Gewirtz, ZDNet, “The astonishing hidden and personal costs of IT downtime (and how predictive analytics might help),” May 30, 2017,

5 IDG, “IDG Quick Pulse: State of IT Operations and Analytics,” February 2018,

6,7,8 TechValidate, AIOps IT industry survey, May 2018

9 IDG, “IDG Quick Pulse: State of IT Operations and Analytics,” February 2018,

10 TechValidate, AIOps IT industry survey, May 2018