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What Can AIOps Do For IT Ops? - Part 3

APMdigest asked the top minds in the industry what they think AIOps can do for IT Operations. Part 3 covers abilities AIOps gives to IT Operations, such as speed and efficiency.

Start with What Can AIOps Do For IT Ops? - Part 1

Start with What Can AIOps Do For IT Ops? - Part 2

PROACTIVE RESPONSIVENESS

Embracing Observability with AIOps gives time back to developers and SREs; it makes their lives easier, so they can focus on improvements and innovation. AIOps surfaces real insights and automates workflows to indicate when there's issues forming — sometimes before they result in an outage — guiding users to the probable root cause of the issue, thus allowing users and teams to fix issues faster and take a proactive approach to prevent future issues from happening. AIOps eliminates the need to manually verify builds, tests, deploys, and releases, and also the need to switch between dashboards communications channels. The collaboration aspects built directly into, and integrated with, a solution unite users with the data and information they need to make informed and proactive decisions.
Adam Frank
VP, Product Management & UX Design, Moogsoft

REAL-TIME RESPONSIVENESS

Under the umbrella of AIOps solutions are features that help teams respond and resolve issues more quickly and as efficiently as possible. Real-time response is a priority for any organization serving customers with high expectations for their digital experience. The pressure on digital service providers continues to increase at an unprecedented pace. In fact, according to Gartner, the average cost to companies of IT downtime is almost $6,000 per minute and can range anywhere from $140,000 per hour to as much as $540,000 per hour. Almost one-third of enterprise companies reported one hour of downtime could cost their business $1-5 million.
Andrew Marshall
Sr. Director of Product Marketing and Advocacy, PagerDuty

SPEED AND EFFICIENCY

AIOps exists to make IT operations efficient and fast by taking advantage of machine learning and big data. With the proper usage, AIOps helps teams act with speed and efficiency and respond to issues proactively and in real-time. This has proven to be a necessity in our new world of working, as organizations need to remain agile and resilient in the face of the next business disruption. This is a game-changer for IT, as teams would be left to solve issues manually, and now, AIOps frees them up to focus on more important tasks.
Gab Menachem
Senior Director, Product Management, ITOM, ServiceNow, and founder and CEO of Loom Systems (a ServiceNow company)

REDUCED MTTR

AIOps helps Dev and Ops teams deliver improvement across the primary SLOs for application reliability and resiliency: MTTR (mean time to resolution) of issues, less system downtime and more time between failures, and faster application response time because of better maintenance.
Jason English
Principal Analyst, Intellyx

IT Central Station users have been impressed with the way AIOps help reduce their mean time to repair (MTTR). This is an important factor for companies that are reliant on their critical IT applications.
Russell Rothstein
Founder and CEO, IT Central Station

Using analytics linked to automation AIOps enables IT operations teams to identify, address and resolve issues more effectively than traditional manual-powered functions. AIOps puts the technology and tools needed to support operational efficiency in one central location — resulting in a more automated and collaborative network that significantly reduces resolution time.
Michael Procopio
Product Marketing Manager, Micro Focus

Today, most enterprises struggle when it comes to technology operations and processes around operations like ITIL. AIOps is going to be the next-gen Ops word for the next 2-3 years around predictive intelligence and predictive insights using artificial intelligence. AIOps will help teams prevent issues occurring in the first place using pattern analysis and also do proactive monitoring. Converting the knowledge base on the repeated issues into knowledge scripts will help reduce the MTTR by invoking those scripts based on the knowledge-based scripts during the failure.
Vishnu Vasudevan
Head of Product Engineering and Management, Opsera

MANAGING NETWORK PERFORMANCE

EMA research has found that 90% of IT organizations believe that applying AIOps to network infrastructure and operations can lead to better overall business outcomes for a company. They find it particularly useful for optimizing their network infrastructure, driving operational efficiency, and reducing security and compliance risk. It isn't easy to achieve these benefits. Only 28% of the IT organizations that are active with applying AIOps to network management consider themselves fully successful with these technology engagements. One big pitfall is risk. While they think AIOps can reduce security and compliance risk, they also think that it might introduce more risk if implemented poorly. They're also struggling with network complexity and data quality. Bad data leads to bad AIOps outcomes.
Shamus McGillicuddy
VP of Research, Networking, Enterprise Management Associates (EMA)

View an on-demand webinar with EMA's Shamus McGillicuddy: Revolutionizing Network Management with AIOps

As networks become more complex and workloads become more distributed, AIOps and virtual AI assistants are increasing becoming essential members of future IT teams. These virtual AI assistants with conversational interfaces are more efficient at viewing the network and managing the end-to-end user experience. As enterprises increasingly move to cloud-managed solutions and services, network vendors whose organizations integrate their customer support, and DevOps teams, with their AIOps data science teams, are fundamentally changing the customer support experience. Enterprises are finding fewer support issues and better visibility as network data moves to the cloud, as well as proactive support such as automated RMA, where their vendor knows a network element needs to be replaced before they do.
Bob Friday
VP and AI Chief Scientist, Juniper Networks

When you think about what AIOps can do for IT Operations it's easy to say that it can do all things and be all things, but the truth behind AIOps is that it will be as good as the data it's fed, and the outcomes you expect. And as we make advancements in AI, ML, and approaches to advanced analytics, the right implementation of AIOps coupled with a complete data set will empower IT organizations to be nimble, accurate, and calculated in their decisions when facing performance issues with critical business applications. While it doesn't replace the operator, it enabled the operator to be pin-point accurate reducing the MTTR and maintaining the high levels of network performance users expect.
Brandon Carroll
Director, Technical Evangelist, Riverbed

MANAGING PERFORMANCE IN THE CLOUD

Enterprises across industries are adopting cloud native patterns to rapidly build contactless, immersive experiences for their customers. But this increased velocity comes with increased complexity. As organizations adopt more cloud native patterns AIOps is a must have and not nice to have. Because without AI OPS there is no way enterprises can effectively manage infrastructure performance in a multi cloud environment or accurately predict capacity.
Milan Bhatt
EVP, Hexaware

Go to What Can AIOps Do For IT Ops? - Part 4

Hot Topics

The Latest

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 ...

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 ...

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 ...

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? ...

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 ...

What Can AIOps Do For IT Ops? - Part 3

APMdigest asked the top minds in the industry what they think AIOps can do for IT Operations. Part 3 covers abilities AIOps gives to IT Operations, such as speed and efficiency.

Start with What Can AIOps Do For IT Ops? - Part 1

Start with What Can AIOps Do For IT Ops? - Part 2

PROACTIVE RESPONSIVENESS

Embracing Observability with AIOps gives time back to developers and SREs; it makes their lives easier, so they can focus on improvements and innovation. AIOps surfaces real insights and automates workflows to indicate when there's issues forming — sometimes before they result in an outage — guiding users to the probable root cause of the issue, thus allowing users and teams to fix issues faster and take a proactive approach to prevent future issues from happening. AIOps eliminates the need to manually verify builds, tests, deploys, and releases, and also the need to switch between dashboards communications channels. The collaboration aspects built directly into, and integrated with, a solution unite users with the data and information they need to make informed and proactive decisions.
Adam Frank
VP, Product Management & UX Design, Moogsoft

REAL-TIME RESPONSIVENESS

Under the umbrella of AIOps solutions are features that help teams respond and resolve issues more quickly and as efficiently as possible. Real-time response is a priority for any organization serving customers with high expectations for their digital experience. The pressure on digital service providers continues to increase at an unprecedented pace. In fact, according to Gartner, the average cost to companies of IT downtime is almost $6,000 per minute and can range anywhere from $140,000 per hour to as much as $540,000 per hour. Almost one-third of enterprise companies reported one hour of downtime could cost their business $1-5 million.
Andrew Marshall
Sr. Director of Product Marketing and Advocacy, PagerDuty

SPEED AND EFFICIENCY

AIOps exists to make IT operations efficient and fast by taking advantage of machine learning and big data. With the proper usage, AIOps helps teams act with speed and efficiency and respond to issues proactively and in real-time. This has proven to be a necessity in our new world of working, as organizations need to remain agile and resilient in the face of the next business disruption. This is a game-changer for IT, as teams would be left to solve issues manually, and now, AIOps frees them up to focus on more important tasks.
Gab Menachem
Senior Director, Product Management, ITOM, ServiceNow, and founder and CEO of Loom Systems (a ServiceNow company)

REDUCED MTTR

AIOps helps Dev and Ops teams deliver improvement across the primary SLOs for application reliability and resiliency: MTTR (mean time to resolution) of issues, less system downtime and more time between failures, and faster application response time because of better maintenance.
Jason English
Principal Analyst, Intellyx

IT Central Station users have been impressed with the way AIOps help reduce their mean time to repair (MTTR). This is an important factor for companies that are reliant on their critical IT applications.
Russell Rothstein
Founder and CEO, IT Central Station

Using analytics linked to automation AIOps enables IT operations teams to identify, address and resolve issues more effectively than traditional manual-powered functions. AIOps puts the technology and tools needed to support operational efficiency in one central location — resulting in a more automated and collaborative network that significantly reduces resolution time.
Michael Procopio
Product Marketing Manager, Micro Focus

Today, most enterprises struggle when it comes to technology operations and processes around operations like ITIL. AIOps is going to be the next-gen Ops word for the next 2-3 years around predictive intelligence and predictive insights using artificial intelligence. AIOps will help teams prevent issues occurring in the first place using pattern analysis and also do proactive monitoring. Converting the knowledge base on the repeated issues into knowledge scripts will help reduce the MTTR by invoking those scripts based on the knowledge-based scripts during the failure.
Vishnu Vasudevan
Head of Product Engineering and Management, Opsera

MANAGING NETWORK PERFORMANCE

EMA research has found that 90% of IT organizations believe that applying AIOps to network infrastructure and operations can lead to better overall business outcomes for a company. They find it particularly useful for optimizing their network infrastructure, driving operational efficiency, and reducing security and compliance risk. It isn't easy to achieve these benefits. Only 28% of the IT organizations that are active with applying AIOps to network management consider themselves fully successful with these technology engagements. One big pitfall is risk. While they think AIOps can reduce security and compliance risk, they also think that it might introduce more risk if implemented poorly. They're also struggling with network complexity and data quality. Bad data leads to bad AIOps outcomes.
Shamus McGillicuddy
VP of Research, Networking, Enterprise Management Associates (EMA)

View an on-demand webinar with EMA's Shamus McGillicuddy: Revolutionizing Network Management with AIOps

As networks become more complex and workloads become more distributed, AIOps and virtual AI assistants are increasing becoming essential members of future IT teams. These virtual AI assistants with conversational interfaces are more efficient at viewing the network and managing the end-to-end user experience. As enterprises increasingly move to cloud-managed solutions and services, network vendors whose organizations integrate their customer support, and DevOps teams, with their AIOps data science teams, are fundamentally changing the customer support experience. Enterprises are finding fewer support issues and better visibility as network data moves to the cloud, as well as proactive support such as automated RMA, where their vendor knows a network element needs to be replaced before they do.
Bob Friday
VP and AI Chief Scientist, Juniper Networks

When you think about what AIOps can do for IT Operations it's easy to say that it can do all things and be all things, but the truth behind AIOps is that it will be as good as the data it's fed, and the outcomes you expect. And as we make advancements in AI, ML, and approaches to advanced analytics, the right implementation of AIOps coupled with a complete data set will empower IT organizations to be nimble, accurate, and calculated in their decisions when facing performance issues with critical business applications. While it doesn't replace the operator, it enabled the operator to be pin-point accurate reducing the MTTR and maintaining the high levels of network performance users expect.
Brandon Carroll
Director, Technical Evangelist, Riverbed

MANAGING PERFORMANCE IN THE CLOUD

Enterprises across industries are adopting cloud native patterns to rapidly build contactless, immersive experiences for their customers. But this increased velocity comes with increased complexity. As organizations adopt more cloud native patterns AIOps is a must have and not nice to have. Because without AI OPS there is no way enterprises can effectively manage infrastructure performance in a multi cloud environment or accurately predict capacity.
Milan Bhatt
EVP, Hexaware

Go to What Can AIOps Do For IT Ops? - Part 4

Hot Topics

The Latest

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 ...

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 ...

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 ...

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? ...

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 ...