2024 Application Performance Management Predictions - Part 5: AIOps

Industry experts offer thoughtful, insightful, and often controversial predictions on how APM, AIOps, Observability, OpenTelemetry and related technologies will evolve and impact business in 2024. Part 5 covers AIOps.

Start with: 2024 Application Performance Management Predictions - Part 1

Start with: 2024 Application Performance Management Predictions - Part 2

Start with: 2024 Application Performance Management Predictions - Part 3

Start with: 2024 Application Performance Management Predictions - Part 4

AIOPS: DRIVING FORCE ACROSS ALL IT

AIOps will become even more of a defining force for all of IT, including ITSM, development, and IT executives — as well as for business stakeholders.
Dennis Drogseth
VP, Enterprise Management Associates (EMA)

In 2024, AIOps is poised to revolutionize IT operations. It will unveil self-learning algorithms operating in real-time, proactively detecting and autonomously resolving incidents. These algorithms will be fortified with sophisticated anomaly detection techniques capable of discerning even the most subtle deviations from established operational norms. Additionally, AIOps will leverage correlation engines that seamlessly integrate insights from diverse data sources, offering a comprehensive view across complex, hybrid infrastructures.
Sumithran Danabalan
SVP, Marketing, Cigniti Technologies

AIOPS REACHES POTENTIAL IN 2024

AIOps — having experienced the ups and downs of the hype cycle over the past few years — is now buoyed by rapid AI/ML advances and destined to reach its potential in 2024 and beyond. This means transformative change for data and analytics professionals, as maturing ML-powered solutions take on and mitigate the complexities of database management. Database administrators doing their human-best to achieve performant queries through data traffic pattern analysis and keeping tabs on storage growth can now be more confidently helped by ML decision-making. The AIOps dream is inevitable as ML training sets improve. Automated operations and predictive remediation, including optimized data indexes, reindexing and storage management based on predictive models, is arriving.
Anil Inamdar
VP & Head of Data, Instaclustr, part of Spot by NetApp

SYNERGY BETWEEN AIOPS AND ITOA WILL BE TRANFORMATIVE

IT Operations Analytics (ITOA) will employ advanced statistical models and predictive analytics. This will empower organizations not only to respond swiftly but also to foresee capacity needs. ITOA will facilitate proactive planning, enabling optimal resource allocation and precision performance tuning, culminating in a more agile and efficient IT environment. The synergy between AIOps and ITOA promises a transformative era for IT operations.
Sumithran Danabalan
SVP, Marketing, Cigniti Technologies

GENAI PAIRS WITH AIOPS

As generative AI continues to gain momentum in mainstream business, expect to see a "leveling out" in 2024 as enterprises begin to adopt standards and deploy GenAI in applications that make business sense as they pair it with AIOps. Conversational AI will drive customer-facing elements such as customer support, directing inquiries, managing UX interfaces, and the initial vetting of customers. GenAI will be used to provide personalized support to IT users, such as by answering their questions and troubleshooting problems. Beyond that, GenAI will support improved anomaly detection and prediction, automated remediation, which can free up IT staff to focus on more strategic tasks, and enhanced decision-making, providing insights that help IT leaders make better decisions about resource allocation, capacity planning, and other critical areas.
Ugo Orsi
Chief Customer Officer, Digitate

AI ENABLES SHIFT TO HIGH-FIDELITY TELEMETRY

As companies' reliance on AI grows in the coming year, we will see them work to improve their data collection capabilities through a shift to high-fidelity telemetry. AI can process more data than humans, and when you have more data — especially more accurate data — the decisions AI and ML make are that much more accurate and actionable. We will see IT teams put more trust in AI, which will lead to greater automation, freeing up teams to focus on higher-level tasks.
Payal Kindiger
Senior Director of Product Marketing, Riverbed

AIOPS DELIVERS IMPROVED RESULTS

In 2024, we'll see the rapid sophistication of AIOps —the process of using big data and machine learning to automate IT operations — which will reveal a new world of interactions and capabilities for large language models. Instead of applications that serve as a simple pass-through to LLMs, I predict the proliferation of prompt engineering tools that automatically enrich users' inputs and iterate with LLMs to derive better results. With more efficient AIOps, enterprises of all kinds will have access to scalable automation.
Sean Scott
Chief Product Development Officer, PagerDuty

AIOPS IMPROVES DIGITAL EMPLOYEE EXPERIENCE

Looking ahead at a persistently tight labor market for top young talent, companies are looking to AIOps to improve the Digital Employee Experience in an effort to improve employee happiness and retain top talent. It's a real concern because roughly 68% of tech leaders believe Millennials and Gen Z employees would consider leaving a company if their digital demands are not met. For companies that want to maintain employee trust and retention, it is business critical that they incorporate AIOps that can deliver rapid, actionable insights into the employee experience, and drive intelligent, adaptive automation to remediate issues before they impact the employee. IT teams looking to kickoff AIOps solutions to improve DEX will look at targeted use cases where there's an overarching problem, use anomaly detection to obtain early warning signals and drive XLA transformation by correlating qualitative employee feedback with end user observability data.
Payal Kindiger
Senior Director of Product Marketing, Riverbed

AI-generated code creates need for digital immune systems

In 2024, more organizations will experience major digital service outages due to poor-quality and insufficiently supervised application code. As software developers continue to use generative AI-powered autonomous agents to write code for them, organizations will be exposed to greater risk of unexpected problems that impact customer and user experiences. This is because maintaining code written by these agents will become a similar challenge to preserving code created by developers who have left the team. There will simply be nobody around who fully understands the code to ensure that problems can be resolved quickly when they arise. Those that attempt to use generative AI to review and resolve issues in the code created by another generative AI will find themselves with a recursive problem, as they will still lack the fundamental knowledge and understanding needed to manage it effectively. This will drive organizations to develop digital immune systems that protect their applications from the inside, by ensuring they are resilient by default. To enable this, organizations will harness predictive AI to automatically sense problems as they begin to emerge and trigger an instant, automated response to safeguard the user experience.
Bernd Greifeneder
CTO and Founder, Dynatrace

Go to: 2024 Application Performance Management Predictions - Part 6, covering AIOps and ITSM.

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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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

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IT's Motivation Crisis Is Really a Process Crisis

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...

2024 Application Performance Management Predictions - Part 5: AIOps

Industry experts offer thoughtful, insightful, and often controversial predictions on how APM, AIOps, Observability, OpenTelemetry and related technologies will evolve and impact business in 2024. Part 5 covers AIOps.

Start with: 2024 Application Performance Management Predictions - Part 1

Start with: 2024 Application Performance Management Predictions - Part 2

Start with: 2024 Application Performance Management Predictions - Part 3

Start with: 2024 Application Performance Management Predictions - Part 4

AIOPS: DRIVING FORCE ACROSS ALL IT

AIOps will become even more of a defining force for all of IT, including ITSM, development, and IT executives — as well as for business stakeholders.
Dennis Drogseth
VP, Enterprise Management Associates (EMA)

In 2024, AIOps is poised to revolutionize IT operations. It will unveil self-learning algorithms operating in real-time, proactively detecting and autonomously resolving incidents. These algorithms will be fortified with sophisticated anomaly detection techniques capable of discerning even the most subtle deviations from established operational norms. Additionally, AIOps will leverage correlation engines that seamlessly integrate insights from diverse data sources, offering a comprehensive view across complex, hybrid infrastructures.
Sumithran Danabalan
SVP, Marketing, Cigniti Technologies

AIOPS REACHES POTENTIAL IN 2024

AIOps — having experienced the ups and downs of the hype cycle over the past few years — is now buoyed by rapid AI/ML advances and destined to reach its potential in 2024 and beyond. This means transformative change for data and analytics professionals, as maturing ML-powered solutions take on and mitigate the complexities of database management. Database administrators doing their human-best to achieve performant queries through data traffic pattern analysis and keeping tabs on storage growth can now be more confidently helped by ML decision-making. The AIOps dream is inevitable as ML training sets improve. Automated operations and predictive remediation, including optimized data indexes, reindexing and storage management based on predictive models, is arriving.
Anil Inamdar
VP & Head of Data, Instaclustr, part of Spot by NetApp

SYNERGY BETWEEN AIOPS AND ITOA WILL BE TRANFORMATIVE

IT Operations Analytics (ITOA) will employ advanced statistical models and predictive analytics. This will empower organizations not only to respond swiftly but also to foresee capacity needs. ITOA will facilitate proactive planning, enabling optimal resource allocation and precision performance tuning, culminating in a more agile and efficient IT environment. The synergy between AIOps and ITOA promises a transformative era for IT operations.
Sumithran Danabalan
SVP, Marketing, Cigniti Technologies

GENAI PAIRS WITH AIOPS

As generative AI continues to gain momentum in mainstream business, expect to see a "leveling out" in 2024 as enterprises begin to adopt standards and deploy GenAI in applications that make business sense as they pair it with AIOps. Conversational AI will drive customer-facing elements such as customer support, directing inquiries, managing UX interfaces, and the initial vetting of customers. GenAI will be used to provide personalized support to IT users, such as by answering their questions and troubleshooting problems. Beyond that, GenAI will support improved anomaly detection and prediction, automated remediation, which can free up IT staff to focus on more strategic tasks, and enhanced decision-making, providing insights that help IT leaders make better decisions about resource allocation, capacity planning, and other critical areas.
Ugo Orsi
Chief Customer Officer, Digitate

AI ENABLES SHIFT TO HIGH-FIDELITY TELEMETRY

As companies' reliance on AI grows in the coming year, we will see them work to improve their data collection capabilities through a shift to high-fidelity telemetry. AI can process more data than humans, and when you have more data — especially more accurate data — the decisions AI and ML make are that much more accurate and actionable. We will see IT teams put more trust in AI, which will lead to greater automation, freeing up teams to focus on higher-level tasks.
Payal Kindiger
Senior Director of Product Marketing, Riverbed

AIOPS DELIVERS IMPROVED RESULTS

In 2024, we'll see the rapid sophistication of AIOps —the process of using big data and machine learning to automate IT operations — which will reveal a new world of interactions and capabilities for large language models. Instead of applications that serve as a simple pass-through to LLMs, I predict the proliferation of prompt engineering tools that automatically enrich users' inputs and iterate with LLMs to derive better results. With more efficient AIOps, enterprises of all kinds will have access to scalable automation.
Sean Scott
Chief Product Development Officer, PagerDuty

AIOPS IMPROVES DIGITAL EMPLOYEE EXPERIENCE

Looking ahead at a persistently tight labor market for top young talent, companies are looking to AIOps to improve the Digital Employee Experience in an effort to improve employee happiness and retain top talent. It's a real concern because roughly 68% of tech leaders believe Millennials and Gen Z employees would consider leaving a company if their digital demands are not met. For companies that want to maintain employee trust and retention, it is business critical that they incorporate AIOps that can deliver rapid, actionable insights into the employee experience, and drive intelligent, adaptive automation to remediate issues before they impact the employee. IT teams looking to kickoff AIOps solutions to improve DEX will look at targeted use cases where there's an overarching problem, use anomaly detection to obtain early warning signals and drive XLA transformation by correlating qualitative employee feedback with end user observability data.
Payal Kindiger
Senior Director of Product Marketing, Riverbed

AI-generated code creates need for digital immune systems

In 2024, more organizations will experience major digital service outages due to poor-quality and insufficiently supervised application code. As software developers continue to use generative AI-powered autonomous agents to write code for them, organizations will be exposed to greater risk of unexpected problems that impact customer and user experiences. This is because maintaining code written by these agents will become a similar challenge to preserving code created by developers who have left the team. There will simply be nobody around who fully understands the code to ensure that problems can be resolved quickly when they arise. Those that attempt to use generative AI to review and resolve issues in the code created by another generative AI will find themselves with a recursive problem, as they will still lack the fundamental knowledge and understanding needed to manage it effectively. This will drive organizations to develop digital immune systems that protect their applications from the inside, by ensuring they are resilient by default. To enable this, organizations will harness predictive AI to automatically sense problems as they begin to emerge and trigger an instant, automated response to safeguard the user experience.
Bernd Greifeneder
CTO and Founder, Dynatrace

Go to: 2024 Application Performance Management Predictions - Part 6, covering AIOps and ITSM.

Related Links

Hot Topics

The Latest

Clearing the Path to AI: Why Vendor Consolidation Matters Now

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 ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

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 ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

No Escalations ≠ No Work: Why Visibility in DevOps Matters More Now That AI Is Accelerating Everything

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

Observability Costs Rising Faster Than Value

The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...

Customers Demand Frictionless Experiences - This Is How Retailers Can Provide Them

Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...

IT's Motivation Crisis Is Really a Process Crisis

If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...