Skip to main content

Discovering AIOps - Part 8: The Future of AIOps

Pete Goldin
APMdigest

The future of AIOps holds significant promise and potential, Ali Siddiqui, Chief Product Officer at BMC predicts. As technology continues to advance, AIOps is likely to play a crucial role in reshaping the landscape of IT operations and business processes. In the era of data-driven decision-making and automation, there will be a significant surge in the demand for AIOps and generative AI. The organizations that can effectively leverage the potential of these will be the ones defining the future landscape of enterprise software.

Overall, AIOps is poised to revolutionize how businesses manage their IT operations, making them more efficient, resilient, and customer-centric, Siddiqui continues. As the technology matures and becomes more widely adopted, it will undoubtedly bring about transformative changes across industries, contributing to improved business outcomes and customer satisfaction.

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

Start with: Discovering AIOps - Part 5: More Advantages

Start with: Discovering AIOps - Part 6: Challenges

Start with: Discovering AIOps - Part 7: The Current State of AIOps

Part 7 of this blog series covered the current state of AIOps. Now, in Part 8, the experts provide their visions for the future of AIOps:

Becoming the Norm

"I think AIOps becomes the norm and not hyped as the latest thing. I'm hopeful that more people quickly see the value and purpose faster so as to enable the adoption of more advanced automation," Carlos Casanova, Principal Analyst at Forrester Research

Helping but Not Replacing ITOPS

"AIOps has definitely changed the way we think about solving problems. In the least, it has allowed us to dream about what may be possible for this still nascent set of capabilities. Because that's what AIOps truly is — a set of capabilities. In the same way monitoring has evolved into a conversation about observability, automation and data (of all types) have evolved into a conversation about AIOps. And since capabilities are the true gateway to positive business outcomes, it is great to see that — according to the most recent SRE survey — reliability practitioners believe AIOPs will make their work easier (52%), while not replacing them (4%)," says Leo Vasiliou, Director of Product Marketing at Catchpoint.

Image removed.

"I believe that most of this technology will be in the form of a very intelligent assistant, rather than replace humans in the loop. I do think it will help eliminate a lot of the groundwork and help teams be more effective and faster in remediating problems," adds Spiros Xanthos, SVP and General Manager of Observability at Splunk.

Complementing Observability

"Observability plays a vital role, working hand-in-hand with AIOps to form a powerful combination, which complements and reinforces each other. An organization equipped with both can leverage AIOps for more intelligent and dynamic monitoring, featuring anomaly detection and advanced root cause analysis," says Siddiqui from BMC.

AIOps for Each Vertical

"People tend to only think about it in the context of alerting or applying CI/CD techniques to IT, but you can also see AIOps techniques applied across various industries and use cases. Health organizations used AIOps to report on COVID data that they collected from a variety of databases in different formats, for example. There are many more opportunities to apply AIOps to core business functions, and eventually engineering functions, too," says Camden Swita, Senior Product Manager at New Relic.

"There will be AIOps technology tailored for every vertical — retail, manufacturing, financial services, utilities, etc. These systems will train on machine data unique to each sector, so they'll offer more specific intelligence and insights," Monika Bhave, Product Manager at Digitate, predicts.

Generative AI Improves AIOps

"Generative AI will make AIOps better as complex orchestrations, automations, etc. will leverage natural language processing or other methods to better interact with what can be complex tooling," says Heath Newburn, Distinguished Field Engineer at PagerDuty.

Natural Language Interfaces

Carlos Casanova from Forrester says, "I see AIOps solutions having much more sophisticated Natural Language Interfaces (NLI) such that lower skilled/trained individuals can perform higher level work. This will hopefully offset some of the displacement that the automation is bringing."

"The next generation of AIOps platforms will offer some form of natural language processing interactions as a way to quickly ramp up a knowledge base that is easily understood by users," Thomas LaRock, Principal Developer Evangelist at Selector, agrees. "This is in contrast to legacy systems which rely on manual help files compiled by subject matter experts and need constant revisions. As AIOps continues to mature and become easier to implement as well as utilize, it will spread to every corner of the office in much the same way the Internet did 30 years ago — gradually, then suddenly."

Variety of Personas

"I see a scenario where the vendor's technologies are able to engage a variety of personas from service delivery, to engineering, to security to business owner," Carlos Casanova from Forrester envisions. "If the data is all the same from across the enterprise, there's no reason why there should be a multitude of tools segmented by persona. I speak to the current state of this from just the technology side in my Three Stages of Preparation For AIOps report. Picking your perspective is the first step."

Read the Forrester blog: Perspective Is Key To Understanding AIOps

Delivering on the Promise

"I think the future of AIOps is people actually doing AIOps. The AIOps space is at the peak of inflated expectations on the hype curve. I don't think people have realized value from AIOps in a meaningful way yet, so the future is about actually applying it. The future of AIOps is about delivering on its own productivity promise," says Bill Lobig, VP Product Management of Automation at IBM.

"AIOps gives the enterprise greater command over its monitored environment so the enterprise can adapt faster and with greater confidence. As enterprises adopt new technologies, rely increasingly on complex digital services to deliver value to customers and seek to connect IT organizations with business outcomes, AIOPs will be an important catalyst for change," Andreas Reiss, Head of Product Management, AIOps and Observability, at Broadcom, concludes. "Where that will lead is tremendously exciting."

Auto-Remediation

Probably the most important ultimate vision, and hope, for AIOps is auto-remediation.

“The movement toward higher levels of automation including automated remediation is accelerating. Typically this can be done on two levels — automated remediation requiring initial IT approval, and automated remediation that occurs on its own, but which should also document its actions in some way. There is definitely a move toward the latter, as both business and technology dynamics are becoming more accelerated,” says Dennis Drogseth, VP at Enterprise Management Associates (EMA).

Go to: Discovering AIOps - Part 9: Auto-Remediation

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 8: The Future of AIOps

Pete Goldin
APMdigest

The future of AIOps holds significant promise and potential, Ali Siddiqui, Chief Product Officer at BMC predicts. As technology continues to advance, AIOps is likely to play a crucial role in reshaping the landscape of IT operations and business processes. In the era of data-driven decision-making and automation, there will be a significant surge in the demand for AIOps and generative AI. The organizations that can effectively leverage the potential of these will be the ones defining the future landscape of enterprise software.

Overall, AIOps is poised to revolutionize how businesses manage their IT operations, making them more efficient, resilient, and customer-centric, Siddiqui continues. As the technology matures and becomes more widely adopted, it will undoubtedly bring about transformative changes across industries, contributing to improved business outcomes and customer satisfaction.

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

Start with: Discovering AIOps - Part 5: More Advantages

Start with: Discovering AIOps - Part 6: Challenges

Start with: Discovering AIOps - Part 7: The Current State of AIOps

Part 7 of this blog series covered the current state of AIOps. Now, in Part 8, the experts provide their visions for the future of AIOps:

Becoming the Norm

"I think AIOps becomes the norm and not hyped as the latest thing. I'm hopeful that more people quickly see the value and purpose faster so as to enable the adoption of more advanced automation," Carlos Casanova, Principal Analyst at Forrester Research

Helping but Not Replacing ITOPS

"AIOps has definitely changed the way we think about solving problems. In the least, it has allowed us to dream about what may be possible for this still nascent set of capabilities. Because that's what AIOps truly is — a set of capabilities. In the same way monitoring has evolved into a conversation about observability, automation and data (of all types) have evolved into a conversation about AIOps. And since capabilities are the true gateway to positive business outcomes, it is great to see that — according to the most recent SRE survey — reliability practitioners believe AIOPs will make their work easier (52%), while not replacing them (4%)," says Leo Vasiliou, Director of Product Marketing at Catchpoint.

Image removed.

"I believe that most of this technology will be in the form of a very intelligent assistant, rather than replace humans in the loop. I do think it will help eliminate a lot of the groundwork and help teams be more effective and faster in remediating problems," adds Spiros Xanthos, SVP and General Manager of Observability at Splunk.

Complementing Observability

"Observability plays a vital role, working hand-in-hand with AIOps to form a powerful combination, which complements and reinforces each other. An organization equipped with both can leverage AIOps for more intelligent and dynamic monitoring, featuring anomaly detection and advanced root cause analysis," says Siddiqui from BMC.

AIOps for Each Vertical

"People tend to only think about it in the context of alerting or applying CI/CD techniques to IT, but you can also see AIOps techniques applied across various industries and use cases. Health organizations used AIOps to report on COVID data that they collected from a variety of databases in different formats, for example. There are many more opportunities to apply AIOps to core business functions, and eventually engineering functions, too," says Camden Swita, Senior Product Manager at New Relic.

"There will be AIOps technology tailored for every vertical — retail, manufacturing, financial services, utilities, etc. These systems will train on machine data unique to each sector, so they'll offer more specific intelligence and insights," Monika Bhave, Product Manager at Digitate, predicts.

Generative AI Improves AIOps

"Generative AI will make AIOps better as complex orchestrations, automations, etc. will leverage natural language processing or other methods to better interact with what can be complex tooling," says Heath Newburn, Distinguished Field Engineer at PagerDuty.

Natural Language Interfaces

Carlos Casanova from Forrester says, "I see AIOps solutions having much more sophisticated Natural Language Interfaces (NLI) such that lower skilled/trained individuals can perform higher level work. This will hopefully offset some of the displacement that the automation is bringing."

"The next generation of AIOps platforms will offer some form of natural language processing interactions as a way to quickly ramp up a knowledge base that is easily understood by users," Thomas LaRock, Principal Developer Evangelist at Selector, agrees. "This is in contrast to legacy systems which rely on manual help files compiled by subject matter experts and need constant revisions. As AIOps continues to mature and become easier to implement as well as utilize, it will spread to every corner of the office in much the same way the Internet did 30 years ago — gradually, then suddenly."

Variety of Personas

"I see a scenario where the vendor's technologies are able to engage a variety of personas from service delivery, to engineering, to security to business owner," Carlos Casanova from Forrester envisions. "If the data is all the same from across the enterprise, there's no reason why there should be a multitude of tools segmented by persona. I speak to the current state of this from just the technology side in my Three Stages of Preparation For AIOps report. Picking your perspective is the first step."

Read the Forrester blog: Perspective Is Key To Understanding AIOps

Delivering on the Promise

"I think the future of AIOps is people actually doing AIOps. The AIOps space is at the peak of inflated expectations on the hype curve. I don't think people have realized value from AIOps in a meaningful way yet, so the future is about actually applying it. The future of AIOps is about delivering on its own productivity promise," says Bill Lobig, VP Product Management of Automation at IBM.

"AIOps gives the enterprise greater command over its monitored environment so the enterprise can adapt faster and with greater confidence. As enterprises adopt new technologies, rely increasingly on complex digital services to deliver value to customers and seek to connect IT organizations with business outcomes, AIOPs will be an important catalyst for change," Andreas Reiss, Head of Product Management, AIOps and Observability, at Broadcom, concludes. "Where that will lead is tremendously exciting."

Auto-Remediation

Probably the most important ultimate vision, and hope, for AIOps is auto-remediation.

“The movement toward higher levels of automation including automated remediation is accelerating. Typically this can be done on two levels — automated remediation requiring initial IT approval, and automated remediation that occurs on its own, but which should also document its actions in some way. There is definitely a move toward the latter, as both business and technology dynamics are becoming more accelerated,” says Dennis Drogseth, VP at Enterprise Management Associates (EMA).

Go to: Discovering AIOps - Part 9: Auto-Remediation

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 ...