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AIOps and the Modern Enterprise

Modern times, modern demands
Bhanu Singh

Thanks to digital transformation, enterprise application and IT infrastructure stacks have witnessed a dramatic shift. Enterprises have transitioned from monolithic applications, bare metal infrastructure and virtual workloads to agile microservices, public cloud platforms and containerized deployments. To keep pace with dynamic and distributed digital services, enterprise IT teams have turned to monitoring point tools to solve specific pain points.


With a majority of enterprises investing in ten or more monitoring tools, it is no easy task keeping up with the volume, variety, and velocity of events for hybrid IT environments. Analyst firm EMA has estimated that IT admins can waste more than half their day digging through irrelevant or redundant alerts. How can IT teams focus on the critical events that can impact their business instead of wading through false positives? The emerging discipline of AIOps is a much-needed panacea for detecting patterns, identifying anomalies, and making sense of alerts across hybrid infrastructure.

What is AIOps?

AIOps leverages a broad set of technology approaches, including machine learning, network science, combinatorial optimization and other computational approaches for solving everyday IT operational problems at scale. Enterprises can address a wide variety of IT management activities with AIOps, such as intelligent alerting, alert correlation, alert escalation, auto-remediation, root cause(s) analysis and capacity optimization.

How are digital operations teams taking advantage of this new application of machine learning and artificial intelligence? OpsRamp, recently released its Top Trends In AIOps Adoptionreport. We surveyed 120 IT executives at enterprises with 500+ employees to better understand their operational challenges and see how they’re using AIOps tools.

Here are four insights from the report that offer an inside look into how enterprises are using issue identification, pattern discovery, and predictive analytics to improve IT-service performance:

1. AIOps Is No Longer A Science Project

AIOps adoption is gaining momentum, with enterprises either experimenting or actively using machine learning and data science for hybrid infrastructure management. 68% of IT decision-makers are piloting AIOps to better manage the availability and performance of business-critical IT services.

The bottom line? The use cases of advanced analytics and automation for IT management are just gaining traction. Gartner projects an increase of 40% in AIOps adoption by 2022. It’s not going away any time soon.

2. Data Insights and Root Cause Analysis Drive AIOps Usage

Modern IT services combine legacy datacenter and multi-cloud environments with numerous commercial and open-source monitoring products for tracking service health and performance. AIOps tools are ingesting, storing and analyzing monitoring data and delivering intelligent insights to fix IT service visibility issues.

Nearly three-quarters of these IT teams are using AIOps capabilities to gain more meaningful insights (73%) from system generated and monitoring-related alerts. Two-thirds of respondents are also applying AIOps to cut through the noise and determine the root cause (68%) of performance issues.

The bottom line? Across the board, respondents resoundingly agreed: AIOps is a chief solution in the battle against data smog. In fact, using AIOps to extract the signal from the noise is one of the primary use cases.

3. AIOps Provides Much-Needed Relief

The two big benefits of AIOps are the ability to automate routine functions (74%) and avoid costly service disruptions with faster recovery (67%). AIOps can also drive better anomaly detection (58%), by predicting shifts in system behavior across dynamic production environments.

The bottom line? I believe that as AIOps tools grow in sophistication, IT teams can expect to save time and money with actionable event context and data-driven recommendations. AIOps will let them focus on high-visibility projects instead of mundane operational tasks.

4. Data Quality and Talent Crunch Top Concerns For AIOps Adoption

While AIOps adoption is gaining steam, we found that there are a few apprehensions which could prevent wider adoption. The accuracy of prediction models (54%), quality of large datasets (52%) for machine learning models and the IT talent (48%) needed for building machine learning algorithms are all key constraints for scaling AIOps.

The bottom line? Accuracy, data quality, and transparency are the biggest AIOps roadblocks. IT leaders will need to identify emerging AIOps challenges and partner with technology vendors to prioritize the right solutions.

A Future, Unsupervised

AIOps is gaining traction in the modern enterprise, and it’s easy to see why. In 2018, the only effective way to tame alert storms is to combine human intuition with machine intelligence. IDC’s Worldwide CIO Agenda 2019 Predictions shows that 70% of CIOs will leverage artificial intelligence and machine learning for IT operations to increase staff productivity, drive faster incident response and minimize downtime. Our research corroborates these findings. The future will almost assuredly include a degree of self-healing IT operations management. That degree is still uncertain. But the age of AIOps is definitely upon us.

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Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

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AIOps and the Modern Enterprise

Modern times, modern demands
Bhanu Singh

Thanks to digital transformation, enterprise application and IT infrastructure stacks have witnessed a dramatic shift. Enterprises have transitioned from monolithic applications, bare metal infrastructure and virtual workloads to agile microservices, public cloud platforms and containerized deployments. To keep pace with dynamic and distributed digital services, enterprise IT teams have turned to monitoring point tools to solve specific pain points.


With a majority of enterprises investing in ten or more monitoring tools, it is no easy task keeping up with the volume, variety, and velocity of events for hybrid IT environments. Analyst firm EMA has estimated that IT admins can waste more than half their day digging through irrelevant or redundant alerts. How can IT teams focus on the critical events that can impact their business instead of wading through false positives? The emerging discipline of AIOps is a much-needed panacea for detecting patterns, identifying anomalies, and making sense of alerts across hybrid infrastructure.

What is AIOps?

AIOps leverages a broad set of technology approaches, including machine learning, network science, combinatorial optimization and other computational approaches for solving everyday IT operational problems at scale. Enterprises can address a wide variety of IT management activities with AIOps, such as intelligent alerting, alert correlation, alert escalation, auto-remediation, root cause(s) analysis and capacity optimization.

How are digital operations teams taking advantage of this new application of machine learning and artificial intelligence? OpsRamp, recently released its Top Trends In AIOps Adoptionreport. We surveyed 120 IT executives at enterprises with 500+ employees to better understand their operational challenges and see how they’re using AIOps tools.

Here are four insights from the report that offer an inside look into how enterprises are using issue identification, pattern discovery, and predictive analytics to improve IT-service performance:

1. AIOps Is No Longer A Science Project

AIOps adoption is gaining momentum, with enterprises either experimenting or actively using machine learning and data science for hybrid infrastructure management. 68% of IT decision-makers are piloting AIOps to better manage the availability and performance of business-critical IT services.

The bottom line? The use cases of advanced analytics and automation for IT management are just gaining traction. Gartner projects an increase of 40% in AIOps adoption by 2022. It’s not going away any time soon.

2. Data Insights and Root Cause Analysis Drive AIOps Usage

Modern IT services combine legacy datacenter and multi-cloud environments with numerous commercial and open-source monitoring products for tracking service health and performance. AIOps tools are ingesting, storing and analyzing monitoring data and delivering intelligent insights to fix IT service visibility issues.

Nearly three-quarters of these IT teams are using AIOps capabilities to gain more meaningful insights (73%) from system generated and monitoring-related alerts. Two-thirds of respondents are also applying AIOps to cut through the noise and determine the root cause (68%) of performance issues.

The bottom line? Across the board, respondents resoundingly agreed: AIOps is a chief solution in the battle against data smog. In fact, using AIOps to extract the signal from the noise is one of the primary use cases.

3. AIOps Provides Much-Needed Relief

The two big benefits of AIOps are the ability to automate routine functions (74%) and avoid costly service disruptions with faster recovery (67%). AIOps can also drive better anomaly detection (58%), by predicting shifts in system behavior across dynamic production environments.

The bottom line? I believe that as AIOps tools grow in sophistication, IT teams can expect to save time and money with actionable event context and data-driven recommendations. AIOps will let them focus on high-visibility projects instead of mundane operational tasks.

4. Data Quality and Talent Crunch Top Concerns For AIOps Adoption

While AIOps adoption is gaining steam, we found that there are a few apprehensions which could prevent wider adoption. The accuracy of prediction models (54%), quality of large datasets (52%) for machine learning models and the IT talent (48%) needed for building machine learning algorithms are all key constraints for scaling AIOps.

The bottom line? Accuracy, data quality, and transparency are the biggest AIOps roadblocks. IT leaders will need to identify emerging AIOps challenges and partner with technology vendors to prioritize the right solutions.

A Future, Unsupervised

AIOps is gaining traction in the modern enterprise, and it’s easy to see why. In 2018, the only effective way to tame alert storms is to combine human intuition with machine intelligence. IDC’s Worldwide CIO Agenda 2019 Predictions shows that 70% of CIOs will leverage artificial intelligence and machine learning for IT operations to increase staff productivity, drive faster incident response and minimize downtime. Our research corroborates these findings. The future will almost assuredly include a degree of self-healing IT operations management. That degree is still uncertain. But the age of AIOps is definitely upon us.

Hot Topics

The Latest

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

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From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...