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

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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 ...

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

AI is becoming the operating system of the enterprise. It acts as an invisible coordination layer that understands intent, connects systems, and executes work across complex SaaS environments. Previously, employees had to click through multiple systems — CRM, ERP, support tools, collaboration platforms — to complete a single task. Now, instead of navigating each application manually, they can simply state what they need to accomplish ...

In 2026, the cost of downtime or an outage is no longer just a technical inconvenience; it's a $600 billion wake up call for global businesses. As our digital ecosystems become  more interconnected, each touchpoint introduces new risks and multiplies the consequences when things go wrong. And the data is clear: aggregate downtime costs  for Global 2,000 companies have surged 50% since 2024, reaching a staggering $600 billion ...

Deloitte found that 74% of enterprises expect to deploy agentic AI solutions in the next 24 months. However, the rush to deployment is outpacing foundational work, though. Only 21% of enterprises have fully formed agent governance models in place. The result? AI agents deployed without guidance or governance begin to function as fragmented islands of complexity ...

Cloud spending is no longer viewed as a passthrough IT expense, but as a strategic financial lever that directly impacts innovation capacity, profitability and enterprise resilience, according to the CFO Cloud Cost Optimization Report from Azul ...

As AI moves from generating responses to performing actions, the need for trust increases exponentially. And as organizations enlist AI agents for increasingly sophisticated business processes, trust is going to be the single most important theme for spurring adoption. What can organizations do to build trustworthy AI agents? ...

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 ...