2024 Application Performance Management Predictions - Part 6: AIOps and ITSM

Industry experts offer thoughtful, insightful, and often controversial predictions on how APM, AIOps, Observability, OpenTelemetry and related technologies will evolve and impact business in 2024. Part 6 covers AIOps and ITSM.

Start with: 2024 Application Performance Management Predictions - Part 1

Start with: 2024 Application Performance Management Predictions - Part 2

Start with: 2024 Application Performance Management Predictions - Part 3

Start with: 2024 Application Performance Management Predictions - Part 4

Start with: 2024 Application Performance Management Predictions - Part 5

MEANING OF AIOPS BECOMES INCREASINGLY CONFUSING

Just what AIOps means will increasingly become a source of confusion, as market definitions seek to confine a diversity of solutions which vary in scope, design point, use case, complexity, and value into a single consistent box. Buyers will need to look for the fit that's right for them, not linear winners in a non-linear set of options.
Dennis Drogseth
VP, Enterprise Management Associates (EMA)

AIOPS DOES NOT REACH DREAM STATE NEXT YEAR

AIOps, in the truest sense, is currently more like a pipe dream or goal state for most companies. I don't expect the industry to reach the dream state of AIOps next year; however, I think we'll see significant progress and investments in the AIOps space. In the not-too-distant future, an engineer is going to have the ability to respond to an incident, get a highly detailed summary—explaining what the problem is, if it's occurred before, if it appears to be novel, etc—and gain solid suggestions on what they should do to remediate the issue, with specific suggestions like rolling back the deployment or updating the configuration. Plus, with the improvements to generative AI over the next few years, these suggestions could very well include the new code or the updated configuration itself. Even so, we won't be quite to the point where an Ops person is going to be able to just sleep through the incident, but the industry is heading in that direction.
Camden Swita
Senior Product Manager, New Relic

AIOPS FALLS OUT FAVOR

AIOps will continue to fall out of favor as those that implemented it realize that the promised benefits like root cause detection just aren't there.
Jeremy Burton
CEO, Observe

SHIFT FROM AIOPS TO LLMs

In 2024, I expect to see more companies reach a breaking point with AIOps and shift their focus towards the potential of LLMs. While AIOps was a laudable concept when introduced, in practice it has failed to live up to its promise. The idea that you could train a model on data emitted by apps, that change every day, is nothing more than a pipe dream. Large Language Models (LLMs) appear to be a far more promising alternative because they attack the problem differently and help users make more intelligent decisions. Companies are waking up to this fact but many more will begin to act on it in the new year.
Jeremy Burton
CEO, Observe

FUSION OF AIOPS AND CONTAINERS REDEFINES ITOPS

The fusion of AIOps with container technology is set to redefine IT operations. This integration promises to catalyze a shift from reactive to predictive management, harnessing intelligent automation to anticipate issues before they impact performance. While high-performance computing (HPC) has traditionally been cautious in adopting such trends, we anticipate a growing recognition of their value in driving efficiency and reliability across all sectors of IT operations, including high-performance environments.
Keith Cunningham
VP of Strategy, Sylabs

USAGE METERING CRITICAL FOR AIOPS

Usage metering will become critical to companies leveraging AIOps due to its user-level granularity and unique insights. To get ahead of this, businesses should instrument usage metering as soon as possible. Accurate usage data is invaluable to understanding your customers, training your AIOps models, and gleaning insights across various business functions.
Puneet Gupta
CEO and Founder, Amberflo

AIOPS EXPANDS BEYOND ITOPS

AIOps will expand beyond its traditional definition of IT operations as other business units will consider applying advanced analytics to their growing piles of data.
Thomas LaRock
Principal Developer Evangelist, Selector.AI

WORKING TOWARDS ITSM POTENTIAL

Similarly to AIOps, the industry is working toward reaching the full potential of ITSM. Eventually, these capabilities will empower engineering teams to automate tasks, like identifying incidents before they reach a critical state, applying changes to remediate issues before they become serious, or writing and resolving support tickets.
Camden Swita
Senior Product Manager, New Relic

ITSM LEVERAGES GENAI INTERACTIONS

IT Service Management (ITSM) will experience a trending adoption of ChatGPT-like interactions for end-users to easily get answers to today's help desk scenarios. Organizations will look to adopt this as well to reduce operational costs.
Zubaid Kazmi
Managing Director, Identity and Access Management, MorganFranklin Consulting

ITSM TEAMS INVEST IN EMPLOYEE EXPERIENCE MANAGEMENT

ITSM teams will invest in employee experience management tools to minimize digital friction and to proactively address issues with connectivity and application performance from the user perspective for both distributed remote and in-office employees. We'll seen an uptick in understanding and mapping user journeys so organizations can best tailor digital experiences to individual preferences.
Gerardo Dada
CMO, Catchpoint

Image removed.

DIGITAL ADOPTION PLATFORMS SUPPORT ITSM

Given the staggering cost of software application downtime — which runs at about $5,600 per minute — in 2024, we expect to see more organizations embracing a digital adoption platform (DAP) in tandem with their ITSM strategy. We increasingly see IT managers taking this step to avoid expensive downtime when a disruption occurs. The key is to have ongoing training, automation or guided risk management processes in place so there is little to no pause in response when the inevitable happens.
Krishna Dunthoori
Founder and CEO, Apty

Go to: 2024 Application Performance Management Predictions - Part 7, covering the user experience.

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Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

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Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

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Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

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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 ...

2024 Application Performance Management Predictions - Part 6: AIOps and ITSM

Industry experts offer thoughtful, insightful, and often controversial predictions on how APM, AIOps, Observability, OpenTelemetry and related technologies will evolve and impact business in 2024. Part 6 covers AIOps and ITSM.

Start with: 2024 Application Performance Management Predictions - Part 1

Start with: 2024 Application Performance Management Predictions - Part 2

Start with: 2024 Application Performance Management Predictions - Part 3

Start with: 2024 Application Performance Management Predictions - Part 4

Start with: 2024 Application Performance Management Predictions - Part 5

MEANING OF AIOPS BECOMES INCREASINGLY CONFUSING

Just what AIOps means will increasingly become a source of confusion, as market definitions seek to confine a diversity of solutions which vary in scope, design point, use case, complexity, and value into a single consistent box. Buyers will need to look for the fit that's right for them, not linear winners in a non-linear set of options.
Dennis Drogseth
VP, Enterprise Management Associates (EMA)

AIOPS DOES NOT REACH DREAM STATE NEXT YEAR

AIOps, in the truest sense, is currently more like a pipe dream or goal state for most companies. I don't expect the industry to reach the dream state of AIOps next year; however, I think we'll see significant progress and investments in the AIOps space. In the not-too-distant future, an engineer is going to have the ability to respond to an incident, get a highly detailed summary—explaining what the problem is, if it's occurred before, if it appears to be novel, etc—and gain solid suggestions on what they should do to remediate the issue, with specific suggestions like rolling back the deployment or updating the configuration. Plus, with the improvements to generative AI over the next few years, these suggestions could very well include the new code or the updated configuration itself. Even so, we won't be quite to the point where an Ops person is going to be able to just sleep through the incident, but the industry is heading in that direction.
Camden Swita
Senior Product Manager, New Relic

AIOPS FALLS OUT FAVOR

AIOps will continue to fall out of favor as those that implemented it realize that the promised benefits like root cause detection just aren't there.
Jeremy Burton
CEO, Observe

SHIFT FROM AIOPS TO LLMs

In 2024, I expect to see more companies reach a breaking point with AIOps and shift their focus towards the potential of LLMs. While AIOps was a laudable concept when introduced, in practice it has failed to live up to its promise. The idea that you could train a model on data emitted by apps, that change every day, is nothing more than a pipe dream. Large Language Models (LLMs) appear to be a far more promising alternative because they attack the problem differently and help users make more intelligent decisions. Companies are waking up to this fact but many more will begin to act on it in the new year.
Jeremy Burton
CEO, Observe

FUSION OF AIOPS AND CONTAINERS REDEFINES ITOPS

The fusion of AIOps with container technology is set to redefine IT operations. This integration promises to catalyze a shift from reactive to predictive management, harnessing intelligent automation to anticipate issues before they impact performance. While high-performance computing (HPC) has traditionally been cautious in adopting such trends, we anticipate a growing recognition of their value in driving efficiency and reliability across all sectors of IT operations, including high-performance environments.
Keith Cunningham
VP of Strategy, Sylabs

USAGE METERING CRITICAL FOR AIOPS

Usage metering will become critical to companies leveraging AIOps due to its user-level granularity and unique insights. To get ahead of this, businesses should instrument usage metering as soon as possible. Accurate usage data is invaluable to understanding your customers, training your AIOps models, and gleaning insights across various business functions.
Puneet Gupta
CEO and Founder, Amberflo

AIOPS EXPANDS BEYOND ITOPS

AIOps will expand beyond its traditional definition of IT operations as other business units will consider applying advanced analytics to their growing piles of data.
Thomas LaRock
Principal Developer Evangelist, Selector.AI

WORKING TOWARDS ITSM POTENTIAL

Similarly to AIOps, the industry is working toward reaching the full potential of ITSM. Eventually, these capabilities will empower engineering teams to automate tasks, like identifying incidents before they reach a critical state, applying changes to remediate issues before they become serious, or writing and resolving support tickets.
Camden Swita
Senior Product Manager, New Relic

ITSM LEVERAGES GENAI INTERACTIONS

IT Service Management (ITSM) will experience a trending adoption of ChatGPT-like interactions for end-users to easily get answers to today's help desk scenarios. Organizations will look to adopt this as well to reduce operational costs.
Zubaid Kazmi
Managing Director, Identity and Access Management, MorganFranklin Consulting

ITSM TEAMS INVEST IN EMPLOYEE EXPERIENCE MANAGEMENT

ITSM teams will invest in employee experience management tools to minimize digital friction and to proactively address issues with connectivity and application performance from the user perspective for both distributed remote and in-office employees. We'll seen an uptick in understanding and mapping user journeys so organizations can best tailor digital experiences to individual preferences.
Gerardo Dada
CMO, Catchpoint

Image removed.

DIGITAL ADOPTION PLATFORMS SUPPORT ITSM

Given the staggering cost of software application downtime — which runs at about $5,600 per minute — in 2024, we expect to see more organizations embracing a digital adoption platform (DAP) in tandem with their ITSM strategy. We increasingly see IT managers taking this step to avoid expensive downtime when a disruption occurs. The key is to have ongoing training, automation or guided risk management processes in place so there is little to no pause in response when the inevitable happens.
Krishna Dunthoori
Founder and CEO, Apty

Go to: 2024 Application Performance Management Predictions - Part 7, covering the user experience.

Related Links

Hot Topics

The Latest

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Reliability Is the New Bottleneck of Innovation

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

5 Takeaways from the Observability Forecast for Retail and eCommerce

Seeing is believing, or in this case, seeing is understanding, according to New Relic's 2025 Observability Forecast for Retail and eCommerce report. Retailers who want to provide exceptional customer experiences while improving IT operations efficiency are leaning on observability ... Here are five key takeaways from the report ...

Maximizing Impact Amid Constraints: The Role of Automation and Orchestration in Federal IT Modernization

Technology leaders across the federal landscape are facing, and will continue to face, an uphill battle when it comes to fortifying their digital environments against hostile and persistent threat actors. On one hand, they are being asked to push digital transformation ... On the other hand, they are facing the fiscal uncertainty of continuing resolutions (CR) and government shutdowns looming near and far. In the face of these challenges, CIOs, CTOs, and CISOs must figure out how to modernize legacy systems and infrastructure while doing more with less and still defending against external and internal threats ...

The SRE Report 2026: Reliability Is Being Redefined

Reliability is no longer proven by uptime alone, according to the The SRE Report 2026 from LogicMonitor. In the AI era, it is experienced through speed, consistency, and user trust, and increasingly judged by business impact. As digital services grow more complex and AI systems move into production, traditional monitoring approaches are struggling to keep pace, increasing the need for AI-first observability that spans applications, infrastructure, and the Internet ...

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

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 ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

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 ...