Skip to main content

Discovering AIOps - Part 5: More Advantages

Pete Goldin
APMdigest

In Part 4 of this blog series, the experts show that AIOps offers some very compelling advantages. Part 5 covers additional expert picks for the advantages that can be gained from AIOps, especially from the business perspective.

Start with: Discovering AIOps - Part 1

Start with: Discovering AIOps - Part 2: Must-Have Capabilities

Start with: Discovering AIOps - Part 3: The Users

Start with: Discovering AIOps - Part 4: Advantages

Reduced Outages

"Adopting AIOps reduces outages for businesses and speeds up the ability to predict and prevent outages before they happen; as such, users should look for an AIOps provider that can improve the time it takes to remediate outages and improve overall customer experience," says Spiros Xanthos, SVP and General Manager of Observability at Splunk.

Improved Operational Resilience

"When well deployed, AIOps not only reduces the length and impact of downtime, but gives us insights on how to create better operational resilience," says Heath Newburn, Distinguished Field Engineer at PagerDuty.

Improved Customer and Employee Experience

"From a business perspective, user, customer and employee experience can be greatly improved from the proactive posture that AIOps enables," says Carlos Casanova, Principal Analyst at Forrester Research.

"Without AIOps, outages that leave a negative impact on performance and reliability may arise, potentially directly impacting revenue and tarnishing brand equity," Xanthos from Splunk comments.

By delivering better availability with shorter outages, customer experience should improve and the associated customer satisfaction (CSAT) and Net Promoter Score (NPS) can increase, adds Newburn from PagerDuty.

"While the IT shop might be winning because it is meeting its SLOs for systems downtime, the larger outcome is that this is improving customer experience, or preventing lost revenue, and that's of course a major impact for the entire organization," Asaf Yigal, CTO of Logz.io asserts.

"AIOps can correlate technical problems to business outcomes and end user experiences. That's the Holy Grail of monitoring that AIOPs achieves," says Andreas Reiss, Head of Product Management, AIOps and Observability, at Broadcom.

"C-levels are using AIOps-supported metrics to understand and manage key business performance indicators. For today's software-fueled businesses, this is just inevitably the value at a certain point," adds Yigal from Logz.io.

Increased IT Productivity

"Since AIOps helps to detect anomalous behaviors unseen by human operators, there is decreased risk to the business, which means IT teams have more bandwidth to help with initiatives that are forward looking, instead of being burdened by manual correlation and firefighting high volumes of alerts," says Michael Gerstenhaber, VP of Product Management at Datadog.

Bob Wambach, VP of Product Marketing at Dynatrace, adds, "We expect AIOps to enable IT teams to do more with their time, cutting out the manual, laborious intervention needed to keep applications secure. AIOps will free up more time for innovation and problem resolution."

Monika Bhave, Product Manager at Digitate, agrees that with AIOps, IT teams are free to work on projects that deliver new business value and ultimately help support revenue growth, such as implementing new software, migrating to the cloud, driving digital transformation efforts, and expediting onboarding/offboarding procedures.

Improved Development Process and Developer Experience

"By reducing the time spent in break/fix and chasing down problems, teams can focus on reducing development cycles and improving feature velocity, adding to the improvements described above, but also improving developer experience, and with the reduction of alert fatigue improving morale and reducing developer turnover," says Newburn of PagerDuty.

Cost Optimization

"AIOps helps organizations achieve cost savings by improving operational efficiency and increased productivity, and by reducing downtime which minimizes the associated costs of disruptions to business operations," says Gerstenhaber from Datadog.

AIOps also delivers cost optimization by enhancing resource allocation and utilization, explains Payal Kindiger, Senior Director of Product Marketing at Riverbed. By analyzing data patterns and resource demands, AIOps helps businesses avoid over-provisioning while ensuring that resources are optimally allocated, resulting in significant cost savings.

Enabling Adoption of Advanced Technologies

In addition to all the advantages outlined in Part 4 and Part 5 of this blog series, the experts say that AIOps also provides a more forward-thinking advantage: empowering organizations to more easily and effectively adopt advanced technologies, such as microservices, containers and hybrid cloud.

Until recently, IT operations teams have had few options when it comes to tackling the expanding complexity of vital technologies, says Brian Emerson, VP & GM, IT Operations Management at ServiceNow.

Modern applications are built from hundreds or thousands of interdependent microservices distributed across multiple clouds, creating incredibly complex software environments, explains Wambach from Dynatrace. This complexity makes it difficult for IT pros to understand the state of these systems, especially when something goes wrong.

Camden Swita, Senior Product Manager at New Relic says consider this: "If it's hard for humans to keep an eye on 'traditional' or simple IT infrastructures and app architectures, it's literally impossible for them to do so for newer versions. The sheer number of 'entities' prohibits human monitoring and challenges our reasoning abilities. You'd be hard-pressed to adequately monitor and triage newer infras and architectures without the assistance of AIOps."

"The dynamic nature of hybrid cloud, microservices, and container environments can lead to increased complexity and challenges in monitoring and managing them effectively," Bharani Kumar Kulasekaran, Product Manager at ManageEngine, agrees. "AIOps platforms help navigate the scale and intricacies of these architectures by providing better visibility and a more holistic view into these environments. By delivering real-time insights obtained from the infrastructure data, AIOps helps optimize resource allocation, ensure performance, and maintain availability across dynamic environments, ultimately resulting in smoother adoption and management of advanced IT infrastructures."

Ali Siddiqui, Chief Product Officer at BMC, concludes, "By adopting AIOps, organizations can confidently embrace new application architectures and navigate increasingly complex hybrid ecosystems while ensuring seamless alignment with the evolving needs of the business and customer demands."

Go to: Discovering AIOps - Part 6, covering the challenges of AIOps.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

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 ...

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 ...

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 ...

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 ...

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 ...

Discovering AIOps - Part 5: More Advantages

Pete Goldin
APMdigest

In Part 4 of this blog series, the experts show that AIOps offers some very compelling advantages. Part 5 covers additional expert picks for the advantages that can be gained from AIOps, especially from the business perspective.

Start with: Discovering AIOps - Part 1

Start with: Discovering AIOps - Part 2: Must-Have Capabilities

Start with: Discovering AIOps - Part 3: The Users

Start with: Discovering AIOps - Part 4: Advantages

Reduced Outages

"Adopting AIOps reduces outages for businesses and speeds up the ability to predict and prevent outages before they happen; as such, users should look for an AIOps provider that can improve the time it takes to remediate outages and improve overall customer experience," says Spiros Xanthos, SVP and General Manager of Observability at Splunk.

Improved Operational Resilience

"When well deployed, AIOps not only reduces the length and impact of downtime, but gives us insights on how to create better operational resilience," says Heath Newburn, Distinguished Field Engineer at PagerDuty.

Improved Customer and Employee Experience

"From a business perspective, user, customer and employee experience can be greatly improved from the proactive posture that AIOps enables," says Carlos Casanova, Principal Analyst at Forrester Research.

"Without AIOps, outages that leave a negative impact on performance and reliability may arise, potentially directly impacting revenue and tarnishing brand equity," Xanthos from Splunk comments.

By delivering better availability with shorter outages, customer experience should improve and the associated customer satisfaction (CSAT) and Net Promoter Score (NPS) can increase, adds Newburn from PagerDuty.

"While the IT shop might be winning because it is meeting its SLOs for systems downtime, the larger outcome is that this is improving customer experience, or preventing lost revenue, and that's of course a major impact for the entire organization," Asaf Yigal, CTO of Logz.io asserts.

"AIOps can correlate technical problems to business outcomes and end user experiences. That's the Holy Grail of monitoring that AIOPs achieves," says Andreas Reiss, Head of Product Management, AIOps and Observability, at Broadcom.

"C-levels are using AIOps-supported metrics to understand and manage key business performance indicators. For today's software-fueled businesses, this is just inevitably the value at a certain point," adds Yigal from Logz.io.

Increased IT Productivity

"Since AIOps helps to detect anomalous behaviors unseen by human operators, there is decreased risk to the business, which means IT teams have more bandwidth to help with initiatives that are forward looking, instead of being burdened by manual correlation and firefighting high volumes of alerts," says Michael Gerstenhaber, VP of Product Management at Datadog.

Bob Wambach, VP of Product Marketing at Dynatrace, adds, "We expect AIOps to enable IT teams to do more with their time, cutting out the manual, laborious intervention needed to keep applications secure. AIOps will free up more time for innovation and problem resolution."

Monika Bhave, Product Manager at Digitate, agrees that with AIOps, IT teams are free to work on projects that deliver new business value and ultimately help support revenue growth, such as implementing new software, migrating to the cloud, driving digital transformation efforts, and expediting onboarding/offboarding procedures.

Improved Development Process and Developer Experience

"By reducing the time spent in break/fix and chasing down problems, teams can focus on reducing development cycles and improving feature velocity, adding to the improvements described above, but also improving developer experience, and with the reduction of alert fatigue improving morale and reducing developer turnover," says Newburn of PagerDuty.

Cost Optimization

"AIOps helps organizations achieve cost savings by improving operational efficiency and increased productivity, and by reducing downtime which minimizes the associated costs of disruptions to business operations," says Gerstenhaber from Datadog.

AIOps also delivers cost optimization by enhancing resource allocation and utilization, explains Payal Kindiger, Senior Director of Product Marketing at Riverbed. By analyzing data patterns and resource demands, AIOps helps businesses avoid over-provisioning while ensuring that resources are optimally allocated, resulting in significant cost savings.

Enabling Adoption of Advanced Technologies

In addition to all the advantages outlined in Part 4 and Part 5 of this blog series, the experts say that AIOps also provides a more forward-thinking advantage: empowering organizations to more easily and effectively adopt advanced technologies, such as microservices, containers and hybrid cloud.

Until recently, IT operations teams have had few options when it comes to tackling the expanding complexity of vital technologies, says Brian Emerson, VP & GM, IT Operations Management at ServiceNow.

Modern applications are built from hundreds or thousands of interdependent microservices distributed across multiple clouds, creating incredibly complex software environments, explains Wambach from Dynatrace. This complexity makes it difficult for IT pros to understand the state of these systems, especially when something goes wrong.

Camden Swita, Senior Product Manager at New Relic says consider this: "If it's hard for humans to keep an eye on 'traditional' or simple IT infrastructures and app architectures, it's literally impossible for them to do so for newer versions. The sheer number of 'entities' prohibits human monitoring and challenges our reasoning abilities. You'd be hard-pressed to adequately monitor and triage newer infras and architectures without the assistance of AIOps."

"The dynamic nature of hybrid cloud, microservices, and container environments can lead to increased complexity and challenges in monitoring and managing them effectively," Bharani Kumar Kulasekaran, Product Manager at ManageEngine, agrees. "AIOps platforms help navigate the scale and intricacies of these architectures by providing better visibility and a more holistic view into these environments. By delivering real-time insights obtained from the infrastructure data, AIOps helps optimize resource allocation, ensure performance, and maintain availability across dynamic environments, ultimately resulting in smoother adoption and management of advanced IT infrastructures."

Ali Siddiqui, Chief Product Officer at BMC, concludes, "By adopting AIOps, organizations can confidently embrace new application architectures and navigate increasingly complex hybrid ecosystems while ensuring seamless alignment with the evolving needs of the business and customer demands."

Go to: Discovering AIOps - Part 6, covering the challenges of AIOps.

Pete Goldin is Editor and Publisher of APMdigest

Hot Topics

The Latest

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

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 ...

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 ...

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 ...

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 ...

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 ...