Skip to main content

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

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

I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...