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How AIOps Defuses the Impact of Change

Phil Tee

When you see distressing internet outages occur like the recent Fastly incident that threw a slew of websites offline, I am never surprised by how widespread the problem was, but paradoxically that it wasn't worse.

The infrastructure behind our digital world is mind-numbingly complex. The movement to cloud computing has added even more layers to the interconnectedness. So when a simple software update goes awry, despite the best efforts of quality control, the ripple effects can go far and wide. The digital economy in the US alone accounts for at least $1,849 billion annually, according to a 2020 report by the Bureau of Economic Analysis. So every moment offline matters.

Prompt troubleshooting is a herculean task — impossible, really, for the human mind alone. There's just too much information to sift through to quickly identify how a single change event precipitated such a widespread crash. IT teams must rely on artificial intelligence, machine learning and algorithms to find and repair the root cause of the problem.

The Perils of "Change"

What seems near effortless online to most of us — ordering food, a Zoom call, reading this article — is a staggeringly Byzantine interconnected flow of data packets, routers, modems, internet service providers, gateways, network exchanges, servers and applications. The interdependencies are at such a level that any meaningful amount of mappability is out of reach. For a human mind, you're talking about understanding more interdependencies than particles in the observable universe — a stunning amount of complexity.

Amid that landscape is the need to update software, whether to refresh the operating system, add features or bolster security. And from time to time, someone performs a routine update that has an unintended and unforeseen consequence. Identifying a problem before an outage occurs is largely a fool's errand because the scale of the situation is just too great. The key is to find the problem before a widespread outage occurs. In such an interconnected digital world, errors tend to cascade and propagate. Catching them early is paramount.

One simple update that goes awry could cripple e-commerce if widespread system outages lingered. The potential risk is profound. History has shown when unintended consequences snowball. Mexico reeled in the 1990s from the devaluation of the peso. The United States stumbled in the 2000s when collateralized debt obligations tied to the mortgage industry prompted a financial crisis.

To be clear, the Fastly incident wasn't a global crisis. The Fastly team responded remarkably well. But the outage underscored how trouble quickly can spread in the interconnected digital world. What's absolutely necessary is to pinpoint the problem immediately.

How Intelligent Observability Defuses the Threat

This is where intelligent observability comes in to analyze the impact of change. AIOps with observability work together to quickly spot the patterns and interconnections in the application data to identify the root cause of a problem before it cascades further and causes a widespread outage.

Every change, every software update, has some kind of record associated with it. So theoretically, when something goes wrong, a site reliability engineer or other IT expert would get an alert in which they could simply trace the issue back to the record of the change that triggered the issue. But in practice, the situation is very complicated. Thousands of other data points were created before and after this specific change occurred, so the challenge to identifying the root cause of the problem is linking the right data to the relevant change.

AIOps finds the right data. It applies algorithms to observability data such as metrics, logs and traces to identify anomalies, determine event significance, surface meaningful alerts and correlate data to provide valuable context. Observability makes the job easier by engineering the application infrastructure to make all of the data more observable. AIOps surfaces the right data amid an ocean of data so your IT teams can quickly spot and repair the problem.

Every change, every software update, leaves a clue behind. The problem is there are thousands and thousands of potential suspects. Intelligent observability can quickly solve the "whatdunnit" before any outage becomes much worse.

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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 ...

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How AIOps Defuses the Impact of Change

Phil Tee

When you see distressing internet outages occur like the recent Fastly incident that threw a slew of websites offline, I am never surprised by how widespread the problem was, but paradoxically that it wasn't worse.

The infrastructure behind our digital world is mind-numbingly complex. The movement to cloud computing has added even more layers to the interconnectedness. So when a simple software update goes awry, despite the best efforts of quality control, the ripple effects can go far and wide. The digital economy in the US alone accounts for at least $1,849 billion annually, according to a 2020 report by the Bureau of Economic Analysis. So every moment offline matters.

Prompt troubleshooting is a herculean task — impossible, really, for the human mind alone. There's just too much information to sift through to quickly identify how a single change event precipitated such a widespread crash. IT teams must rely on artificial intelligence, machine learning and algorithms to find and repair the root cause of the problem.

The Perils of "Change"

What seems near effortless online to most of us — ordering food, a Zoom call, reading this article — is a staggeringly Byzantine interconnected flow of data packets, routers, modems, internet service providers, gateways, network exchanges, servers and applications. The interdependencies are at such a level that any meaningful amount of mappability is out of reach. For a human mind, you're talking about understanding more interdependencies than particles in the observable universe — a stunning amount of complexity.

Amid that landscape is the need to update software, whether to refresh the operating system, add features or bolster security. And from time to time, someone performs a routine update that has an unintended and unforeseen consequence. Identifying a problem before an outage occurs is largely a fool's errand because the scale of the situation is just too great. The key is to find the problem before a widespread outage occurs. In such an interconnected digital world, errors tend to cascade and propagate. Catching them early is paramount.

One simple update that goes awry could cripple e-commerce if widespread system outages lingered. The potential risk is profound. History has shown when unintended consequences snowball. Mexico reeled in the 1990s from the devaluation of the peso. The United States stumbled in the 2000s when collateralized debt obligations tied to the mortgage industry prompted a financial crisis.

To be clear, the Fastly incident wasn't a global crisis. The Fastly team responded remarkably well. But the outage underscored how trouble quickly can spread in the interconnected digital world. What's absolutely necessary is to pinpoint the problem immediately.

How Intelligent Observability Defuses the Threat

This is where intelligent observability comes in to analyze the impact of change. AIOps with observability work together to quickly spot the patterns and interconnections in the application data to identify the root cause of a problem before it cascades further and causes a widespread outage.

Every change, every software update, has some kind of record associated with it. So theoretically, when something goes wrong, a site reliability engineer or other IT expert would get an alert in which they could simply trace the issue back to the record of the change that triggered the issue. But in practice, the situation is very complicated. Thousands of other data points were created before and after this specific change occurred, so the challenge to identifying the root cause of the problem is linking the right data to the relevant change.

AIOps finds the right data. It applies algorithms to observability data such as metrics, logs and traces to identify anomalies, determine event significance, surface meaningful alerts and correlate data to provide valuable context. Observability makes the job easier by engineering the application infrastructure to make all of the data more observable. AIOps surfaces the right data amid an ocean of data so your IT teams can quickly spot and repair the problem.

Every change, every software update, leaves a clue behind. The problem is there are thousands and thousands of potential suspects. Intelligent observability can quickly solve the "whatdunnit" before any outage becomes much worse.

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 ...

Image
Broadcom

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 ...