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AIOps Exchange: The AIOps Manifesto

Pete Goldin
APMdigest

AIOps Exchange, a not-for-profit private forum defining the future of AIOps, published The AIOps Manifesto discussing the role of AI in supporting digital transformation.


The document provides a best practices framework for deploying AIOps solutions to help IT Ops and DevOps teams deliver high-quality services continuously and consistently.

The AIOps Manifesto was co-authored by a group of industry analysts that sit on AIOps Exchange’s steering committee. The authors are encouraging feedback and open debate, with the hopes of having the document ratified by Exchange members at a future meeting.

The AIOps Manifesto states: "The burdens of modern IT Operations Management can be ameliorated, even largely removed, using Artificial Intelligence (AI). The market for AI applied to IT Operations, or 'AIOps' for short, is growing in parallel with digital transformation. AI has emerged as the key to mastering both the explosion in system data and the automation of human-to-machine interactions."

The manifesto includes:

■ 5 distinct algorithm definitions

■ 6 kinds of vendor solutions

■ Key AIOps market drivers

The AIOps Manifesto was co-authored by Will Cappelli, a former research VP at Gartner (now CTO of Moogsoft); James Governor, founder of RedMonk; and Clive Longbottom, co-founder of Quocirca.

“Gartner’s own definition of AIOps is still evolving, and the impact it can have on enterprise business strategy is a continuing conversation,” explains Will Cappelli. “Our goal with The AIOps Manifesto is to foster an open, honest exchange of ideas among end user organizations. By debating AIOps best practices, we hope all market stakeholders will benefit, especially IT Operations teams. All feedback to The AIOps Manifesto is welcome during this open comment period, after which we hope it will be officially adopted.”

In today’s business landscape, the complexity of digital business supporting IT systems has resulted in an ever-growing volume of noisy event data that impact the IT Operations Management function. The AIOps Manifesto states that without the assistance of AI technology across the spectrum of IT Operations — or AIOps — it has become impossible to observe, understand, and modify digital business supporting IT systems. Additionally, it explains how AIOps can significantly reduce the fixed costs and enhance the value of IT-related decision making, as well as linking and integrating tasks performed within the different roles of IT Operations, DevOps, IT Service Management, and other functions.

“The increasingly complexity of an organization’s IT platform, combined with the dependency of the business upon it, means that standard approaches to operational management are no longer fit for purpose,” said Clive Longbottom. “A range of approaches need to be brought to bear: a combination of data aggregation, analysis and pattern recognition, and rule-based systems, along with data transfer and results communication to entities that are not just human. In essence, we need the equivalent of a human, albeit one that does not make mistakes and can deal with the masses of data that a modern platform produces in real time. Therefore, we need artificial intelligence. Welcome to the age of AIOps.”

Pete Goldin is Editor and Publisher of APMdigest

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AIOps Exchange: The AIOps Manifesto

Pete Goldin
APMdigest

AIOps Exchange, a not-for-profit private forum defining the future of AIOps, published The AIOps Manifesto discussing the role of AI in supporting digital transformation.


The document provides a best practices framework for deploying AIOps solutions to help IT Ops and DevOps teams deliver high-quality services continuously and consistently.

The AIOps Manifesto was co-authored by a group of industry analysts that sit on AIOps Exchange’s steering committee. The authors are encouraging feedback and open debate, with the hopes of having the document ratified by Exchange members at a future meeting.

The AIOps Manifesto states: "The burdens of modern IT Operations Management can be ameliorated, even largely removed, using Artificial Intelligence (AI). The market for AI applied to IT Operations, or 'AIOps' for short, is growing in parallel with digital transformation. AI has emerged as the key to mastering both the explosion in system data and the automation of human-to-machine interactions."

The manifesto includes:

■ 5 distinct algorithm definitions

■ 6 kinds of vendor solutions

■ Key AIOps market drivers

The AIOps Manifesto was co-authored by Will Cappelli, a former research VP at Gartner (now CTO of Moogsoft); James Governor, founder of RedMonk; and Clive Longbottom, co-founder of Quocirca.

“Gartner’s own definition of AIOps is still evolving, and the impact it can have on enterprise business strategy is a continuing conversation,” explains Will Cappelli. “Our goal with The AIOps Manifesto is to foster an open, honest exchange of ideas among end user organizations. By debating AIOps best practices, we hope all market stakeholders will benefit, especially IT Operations teams. All feedback to The AIOps Manifesto is welcome during this open comment period, after which we hope it will be officially adopted.”

In today’s business landscape, the complexity of digital business supporting IT systems has resulted in an ever-growing volume of noisy event data that impact the IT Operations Management function. The AIOps Manifesto states that without the assistance of AI technology across the spectrum of IT Operations — or AIOps — it has become impossible to observe, understand, and modify digital business supporting IT systems. Additionally, it explains how AIOps can significantly reduce the fixed costs and enhance the value of IT-related decision making, as well as linking and integrating tasks performed within the different roles of IT Operations, DevOps, IT Service Management, and other functions.

“The increasingly complexity of an organization’s IT platform, combined with the dependency of the business upon it, means that standard approaches to operational management are no longer fit for purpose,” said Clive Longbottom. “A range of approaches need to be brought to bear: a combination of data aggregation, analysis and pattern recognition, and rule-based systems, along with data transfer and results communication to entities that are not just human. In essence, we need the equivalent of a human, albeit one that does not make mistakes and can deal with the masses of data that a modern platform produces in real time. Therefore, we need artificial intelligence. Welcome to the age of AIOps.”

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.