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What Is the Deal with AIOps? - Part 1

Akhilesh Tripathi
Digitate

We are in an era where the rate of technology adoption across nearly all industries has increased significantly in recent years. Growing enterprise complexity has increased demand for new models for business transformation in the form of deployment, scale, and change acceleration. This renewed acceleration of technology adoption is redefining enterprise IT Operations (ITOps).

The increase of instrumentation, monitoring and integrations has increased the amount of data generated by organizations — which created two challenges:

1. making sense of it has become difficult with standard methods.

2. It has also increased noise in the ecosystem, which is leading to high false alerts.

These have led to the need and eventual creation of a new market category within the space of enterprise IT called "AIOps" — the application of Artificial Intelligence (AI) for IT operations.

AIOps is rapidly becoming a de-facto option for enterprises' IT strategies, with nearly immeasurable benefits to be provided. However, AIOps is still a relatively new discipline and misconceptions surrounding the technology's capabilities and uses have caused bottlenecks and roadblocks in its widespread adoption.

So, what should organizations expect from AIOps?

How can organizations that want to digitally transform their IT pursue AIOps for maximum benefit?

What is AIOps? Why AIOps?

First, let's see exactly what AIOps is and why it's critical in today's enterprise IT environment. Recent digital transformation efforts across industries have redefined enterprise IT Operations and led to the emergence of AIOps.

AIOps refers to solutions that leverage AI and Machine Learning (ML) to acquire enterprise IT data, analyze it and take required actions for autonomous IT Operations. It helps transform enterprise IT operations from being slow and reactive to agile and proactive, thus addressing many key IT operational and business challenges.

Automating IT operations enables easy deployment of modern and agile IT systems that support enterprise-wide digital transformation efforts, such as cloud migration and automation enablement. Traditional IT management solutions that involve manual efforts for tedious and repeatable processes cannot keep up with the pace of rapid enterprise IT changes and leaves IT teams facing challenges surrounding infrastructure complexities, long delays in isolating and resolving IT faults, and inconsistent and variable quality of operations. Deploying AIOps helps to overcome these challenges by acting as an intelligent way to assess enterprise system behavior and detect anomalies, prescribe solutions and proactively take action to resolve IT incidents and prevent disruptions in IT operations.

With the increase in scale of enterprise operations, complexity and accelerating change in technology footprints, i.e., the landscape of digital systems across an organization, AIOps is not just an option, but a necessity. The volume and complexity of data generated by, and coming into, any given organization can be quite voluminous and overwhelming. Handling this with traditional IT systems can be quite inadequate. Making sense from this huge amount of information calls for advanced AI/ML based analytics/intelligence layer.

Also, as data might come from correlated sources it can lead to duplicated work and siloed views if handled through a traditional and siloed IT operations approach. This is because it lacks the ability to provide a correlated enterprise-wide view of digital systems and how they interact across business domains. So, it can never match the scale of this data and also cannot reap the full benefits of this data/information.

Simply making sense of the data/information won't solve the problem, it is also necessary to act on the inference drawn from this data, and this is hugely important. Intelligent automation becomes a necessity here. Hence, the need for a highly intelligent, hyper automated and scalable solution that can combine big data, observability, enterprise context, AI/ML based analytics and intelligent automation to help gain full-stack visibility across hybrid environments, understand normal behavior, understand root causes of issues, fix problems, predict failures and their direct impact on IT and business. Thus, providing resilient and efficient IT operations cross organizations — and the answer lies in "AIOps."

Go to What Is the Deal with AIOps? - Part 2, outlining what to keep in mind when considering DevOps, and what results can be expected from AIOps.

Akhilesh Tripathi is CEO at Digitate

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Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

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What Is the Deal with AIOps? - Part 1

Akhilesh Tripathi
Digitate

We are in an era where the rate of technology adoption across nearly all industries has increased significantly in recent years. Growing enterprise complexity has increased demand for new models for business transformation in the form of deployment, scale, and change acceleration. This renewed acceleration of technology adoption is redefining enterprise IT Operations (ITOps).

The increase of instrumentation, monitoring and integrations has increased the amount of data generated by organizations — which created two challenges:

1. making sense of it has become difficult with standard methods.

2. It has also increased noise in the ecosystem, which is leading to high false alerts.

These have led to the need and eventual creation of a new market category within the space of enterprise IT called "AIOps" — the application of Artificial Intelligence (AI) for IT operations.

AIOps is rapidly becoming a de-facto option for enterprises' IT strategies, with nearly immeasurable benefits to be provided. However, AIOps is still a relatively new discipline and misconceptions surrounding the technology's capabilities and uses have caused bottlenecks and roadblocks in its widespread adoption.

So, what should organizations expect from AIOps?

How can organizations that want to digitally transform their IT pursue AIOps for maximum benefit?

What is AIOps? Why AIOps?

First, let's see exactly what AIOps is and why it's critical in today's enterprise IT environment. Recent digital transformation efforts across industries have redefined enterprise IT Operations and led to the emergence of AIOps.

AIOps refers to solutions that leverage AI and Machine Learning (ML) to acquire enterprise IT data, analyze it and take required actions for autonomous IT Operations. It helps transform enterprise IT operations from being slow and reactive to agile and proactive, thus addressing many key IT operational and business challenges.

Automating IT operations enables easy deployment of modern and agile IT systems that support enterprise-wide digital transformation efforts, such as cloud migration and automation enablement. Traditional IT management solutions that involve manual efforts for tedious and repeatable processes cannot keep up with the pace of rapid enterprise IT changes and leaves IT teams facing challenges surrounding infrastructure complexities, long delays in isolating and resolving IT faults, and inconsistent and variable quality of operations. Deploying AIOps helps to overcome these challenges by acting as an intelligent way to assess enterprise system behavior and detect anomalies, prescribe solutions and proactively take action to resolve IT incidents and prevent disruptions in IT operations.

With the increase in scale of enterprise operations, complexity and accelerating change in technology footprints, i.e., the landscape of digital systems across an organization, AIOps is not just an option, but a necessity. The volume and complexity of data generated by, and coming into, any given organization can be quite voluminous and overwhelming. Handling this with traditional IT systems can be quite inadequate. Making sense from this huge amount of information calls for advanced AI/ML based analytics/intelligence layer.

Also, as data might come from correlated sources it can lead to duplicated work and siloed views if handled through a traditional and siloed IT operations approach. This is because it lacks the ability to provide a correlated enterprise-wide view of digital systems and how they interact across business domains. So, it can never match the scale of this data and also cannot reap the full benefits of this data/information.

Simply making sense of the data/information won't solve the problem, it is also necessary to act on the inference drawn from this data, and this is hugely important. Intelligent automation becomes a necessity here. Hence, the need for a highly intelligent, hyper automated and scalable solution that can combine big data, observability, enterprise context, AI/ML based analytics and intelligent automation to help gain full-stack visibility across hybrid environments, understand normal behavior, understand root causes of issues, fix problems, predict failures and their direct impact on IT and business. Thus, providing resilient and efficient IT operations cross organizations — and the answer lies in "AIOps."

Go to What Is the Deal with AIOps? - Part 2, outlining what to keep in mind when considering DevOps, and what results can be expected from AIOps.

Akhilesh Tripathi is CEO at Digitate

Hot Topics

The Latest

Artificial intelligence (AI) is core to observability practices, with some 41% of respondents reporting AI adoption as a core driver of observability, according to the State of Observability for Financial Services and Insurance report from New Relic ...

Application performance monitoring (APM) is a game of catching up — building dashboards, setting thresholds, tuning alerts, and manually correlating metrics to root causes. In the early days, this straightforward model worked as applications were simpler, stacks more predictable, and telemetry was manageable. Today, the landscape has shifted, and more assertive tools are needed ...

Cloud adoption has accelerated, but backup strategies haven't always kept pace. Many organizations continue to rely on backup strategies that were either lifted directly from on-prem environments or use cloud-native tools in limited, DR-focused ways ... Eon uncovered a handful of critical gaps regarding how organizations approach cloud backup. To capture these prevailing winds, we gathered insights from 150+ IT and cloud leaders at the recent Google Cloud Next conference, which we've compiled into the 2025 State of Cloud Data Backup ...

Private clouds are no longer playing catch-up, and public clouds are no longer the default as organizations recalibrate their cloud strategies, according to the Private Cloud Outlook 2025 report from Broadcom. More than half (53%) of survey respondents say private cloud is their top priority for deploying new workloads over the next three years, while 69% are considering workload repatriation from public to private cloud, with one-third having already done so ...

As organizations chase productivity gains from generative AI, teams are overwhelmingly focused on improving delivery speed (45%) over enhancing software quality (13%), according to the Quality Transformation Report from Tricentis ...

Back in March of this year ... MongoDB's stock price took a serious tumble ... In my opinion, it reflects a deeper structural issue in enterprise software economics altogether — vendor lock-in ...

In MEAN TIME TO INSIGHT Episode 15, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses Do-It-Yourself Network Automation ... 

Zero-day vulnerabilities — security flaws that are exploited before developers even know they exist — pose one of the greatest risks to modern organizations. Recently, such vulnerabilities have been discovered in well-known VPN systems like Ivanti and Fortinet, highlighting just how outdated these legacy technologies have become in defending against fast-evolving cyber threats ... To protect digital assets and remote workers in today's environment, companies need more than patchwork solutions. They need architecture that is secure by design ...

Traditional observability requires users to leap across different platforms or tools for metrics, logs, or traces and related issues manually, which is very time-consuming, so as to reasonably ascertain the root cause. Observability 2.0 fixes this by unifying all telemetry data, logs, metrics, and traces into a single, context-rich pipeline that flows into one smart platform. But this is far from just having a bunch of additional data; this data is actionable, predictive, and tied to revenue realization ...

64% of enterprise networking teams use internally developed software or scripts for network automation, but 61% of those teams spend six or more hours per week debugging and maintaining them, according to From Scripts to Platforms: Why Homegrown Tools Dominate Network Automation and How Vendors Can Help, my latest EMA report ...