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2022 Application Performance Management Predictions - Part 2

Industry experts offer thoughtful, insightful, and often controversial predictions on how APM, AIOps, Observability, OpenTelemetry, and related technologies will evolve and impact business in 2022. Part 2 covers AIOps.

Start with: 2022 Application Performance Management Predictions - Part 1

AIOPS: Find, Fail and Fix Faster

Rising inflation, worker shortage, and the uncertainty of COVIDs long-term impact on the Global economy will make visibility to Operations more critical now than ever. There will be renewed focus on solving visibility, scalability and predictability for a large global, mobile workforce. In 2022, Automation at scale and Telemetry pre-release will be key around predictable find, fail and fix faster cycle times.
Jeanne Morain
Founder and Digital Transformation Catalyst, iSpeak Cloud

AIOPS LEADS TO SELF HEALING

2022 is the year where IT hygiene will catch up to the speed and scale of business. Much like the rise of personal wellness, we will start to think in similar terms when it comes to the people and technology that support the digital infrastructure of every organization today. We are headed, eventually, toward a future of the "self-healing enterprise" via technologies like AIOps. But before we get there, we need "self-care in the enterprise" — which involves constantly looking at the health and performance of the infrastructure and proactively addressing its stressors. Just like we go to the dentist whether we "need to" or not in order to prevent cavities, IT teams will focus on keeping employees happy and healthy and the infrastructure resilient and agile with more transparent monitoring and management.
Christina Kosmowski
President, LogicMonitor

FULL STACK AIOPS

Full stack AIOPs will be the key AI theme of 2022 enterprise networking. Fueled by increasingly complex networks and distributed workloads, artificial intelligence for IT operations (AIOps) has throttled its way to the top theme of the coming year. Expect to see enterprises invest in four key areas where AIOps make the biggest impact: user experience, operations experience, DevOps/application experience and location services. Further, organizations will also increasingly turn to AIOps to boost cybersecurity and immediately identify and mitigate potential issues before they occur.
Bob Friday
VP and CTO, Juniper Networks AI-Driven Enterprise

Infrastructure teams were the IT heroes of the pandemic as they moved systems and processes out of the office and into people's homes. This upheaval has highlighted the need for infrastructure teams to be both agile and resilient in the face of unforeseen emergencies. As organizations are able to take stock of post-Covid infrastructure requirements, we predict an increase in investments which improve visibility across the stack and which enable organizations to make large scale infrastructure changes at speed.
Andrew Sweeney
Co-Founder and Co-CEO, ReadyWorks

AIOPS GOES FROM DRAWING BOARD TO SHOP FLOOR

In 2022, enterprises will take big steps forward in their journey to AIOps. The catalyst: Organizations must deliver comprehensive, service-level awareness that measures business outcomes by visualizing connections across the IT estate and existing SLAs. This will force a shift away from device-level performance; ITOps leaders will instead prioritize how they define and determine enterprise service health, availability and risk via automated, real-time dashboards that can gauge business and customer impact. Furthermore, AIOps initiatives will elevate and enrich multi-directional data federation in real time to improve data-driven business decisions. It will be vital to accelerate automations with enterprise endpoints and functional capabilities, such as DevSecOps and financial systems. Watch for 2022 as the year AIOps goes from drawing board to shop floor.
Michael Nappi
CPO, ScienceLogic

AIOPS CLOSES SILOS BETWEEN OPS TEAMS

IT operations teams are finding it more difficult to rely on rule-based approaches alone for preventing and managing outages and alerts. Over the next year, we anticipate seeing a growing demand for AI models to be explainable when it comes to recommendations around diagnostics and solutions. Additionally, we believe AIOps will be a critical component to closing the silos between operations teams and enabling them to grow their skillset by offering better visibility across application, infrastructure and network domains.
Girish Muckai
Chief Sales & Marketing Officer, HEAL Software

DEMOCRATIZATION OF DOMAIN-AGNOSTIC AIOPS

In the 2022 timeframe, we will continue to see the democratization of domain agnostic AIOps, as complex ML tools kits are replaced by cost-effective purpose-built tools that leverage no-code/low-code and bring powerful API's that encourage automation. A more specific area of innovation will be around the application of AI to change management, where ML can be used to perform risk and impact analysis on potential changes.
Richard Whitehead
Evangelist in Chief, CTO, Moogsoft

Closing the AI Talent Gap Will Give Rise to AIOps Adoption

AIOps introduces governance, scaling, and transparency issues. Do you have AI governance in place? How are you scaling? Is it transparent? What do you do when the model is drifting? There's a lot of issues specific to AI that people are uncovering, but don't know how to resolve because of the lack of skilled professionals in the space. This AI talent shortage presents a huge barrier to AIOps adoption. As we continue to train and hire skilled AI professionals, more organizations will have the capabilities to utilize big data, machine learning and other advanced analytics technologies to gather data from their application environment. With these insights, it will soon be possible for organizations to have a bird's eye view of their entire ecosystem, allowing them to proactively spot trends and issues that will enable them to proactively manage their IT operations.
Rachel Roumeliotis
VP of AI and Data Content Strategy, O'Reilly Media

AIOPS SOLVES STAFFING AND SKILLS GAP

As we get into 2022, ITOps leaders will be pressing their software providers to keep pushing the bounds on what AIOps can bring to bear. Faster root-cause isolation and automated remediations must be huge parts of this. Teams are not growing, and tech workers are changing jobs more frequently — especially with the great disruption continuing for the foreseeable future. The only way to fill the staffing and skills gap is by deploying smarter systems. AIOps can fill this gap and leaders who aren't planning a 2022 rollout of an AIOps platform risk hurting the business's bottom line.
Rich Lane
Chief Strategy Officer, Netenrich

AIOPS STRUGGLES IN THE CLOUD

AIOps will continue to struggle with the increasing rate of change in modern cloud environments. High deployment frequencies coupled with cloud-native product velocities makes it increasingly difficult to baseline systems and discover useful patterns.
Tobias KunzeCEO and Co-Founder, Glasnostic

AIOps Will Be Dismantled, Repackaged and Rebranded

AIOps is an amalgamation of capabilities with some being more hype than hope. Considering that 39% of DevOps' SRE said they've never used AIOps and 68% said received AIOps value was only "low" or "moderate", the shift to AIOps in its current form is slow. Capabilities like "self-heal" and "auto remedy" have largely failed to deliver on their promises whereas capabilities such as "correlation" and "root cause analysis" have not. The result: a repackaged set of capabilities (sans "self-heal") that usefully focus on actionability and the addition of active, observational data sources (e.g., business KPI and competitive intelligence benchmarking) will emerge as AIOps NextGen.
Leo Vasiliou
Director of Product Marketing, Catchpoint

Go to: 2022 Application Performance Management Predictions - Part 3, covering Observability predictions.

Hot Topics

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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 ...

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 ...

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 ...

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 ...

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 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.

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 ...

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 ...

2022 Application Performance Management Predictions - Part 2

Industry experts offer thoughtful, insightful, and often controversial predictions on how APM, AIOps, Observability, OpenTelemetry, and related technologies will evolve and impact business in 2022. Part 2 covers AIOps.

Start with: 2022 Application Performance Management Predictions - Part 1

AIOPS: Find, Fail and Fix Faster

Rising inflation, worker shortage, and the uncertainty of COVIDs long-term impact on the Global economy will make visibility to Operations more critical now than ever. There will be renewed focus on solving visibility, scalability and predictability for a large global, mobile workforce. In 2022, Automation at scale and Telemetry pre-release will be key around predictable find, fail and fix faster cycle times.
Jeanne Morain
Founder and Digital Transformation Catalyst, iSpeak Cloud

AIOPS LEADS TO SELF HEALING

2022 is the year where IT hygiene will catch up to the speed and scale of business. Much like the rise of personal wellness, we will start to think in similar terms when it comes to the people and technology that support the digital infrastructure of every organization today. We are headed, eventually, toward a future of the "self-healing enterprise" via technologies like AIOps. But before we get there, we need "self-care in the enterprise" — which involves constantly looking at the health and performance of the infrastructure and proactively addressing its stressors. Just like we go to the dentist whether we "need to" or not in order to prevent cavities, IT teams will focus on keeping employees happy and healthy and the infrastructure resilient and agile with more transparent monitoring and management.
Christina Kosmowski
President, LogicMonitor

FULL STACK AIOPS

Full stack AIOPs will be the key AI theme of 2022 enterprise networking. Fueled by increasingly complex networks and distributed workloads, artificial intelligence for IT operations (AIOps) has throttled its way to the top theme of the coming year. Expect to see enterprises invest in four key areas where AIOps make the biggest impact: user experience, operations experience, DevOps/application experience and location services. Further, organizations will also increasingly turn to AIOps to boost cybersecurity and immediately identify and mitigate potential issues before they occur.
Bob Friday
VP and CTO, Juniper Networks AI-Driven Enterprise

Infrastructure teams were the IT heroes of the pandemic as they moved systems and processes out of the office and into people's homes. This upheaval has highlighted the need for infrastructure teams to be both agile and resilient in the face of unforeseen emergencies. As organizations are able to take stock of post-Covid infrastructure requirements, we predict an increase in investments which improve visibility across the stack and which enable organizations to make large scale infrastructure changes at speed.
Andrew Sweeney
Co-Founder and Co-CEO, ReadyWorks

AIOPS GOES FROM DRAWING BOARD TO SHOP FLOOR

In 2022, enterprises will take big steps forward in their journey to AIOps. The catalyst: Organizations must deliver comprehensive, service-level awareness that measures business outcomes by visualizing connections across the IT estate and existing SLAs. This will force a shift away from device-level performance; ITOps leaders will instead prioritize how they define and determine enterprise service health, availability and risk via automated, real-time dashboards that can gauge business and customer impact. Furthermore, AIOps initiatives will elevate and enrich multi-directional data federation in real time to improve data-driven business decisions. It will be vital to accelerate automations with enterprise endpoints and functional capabilities, such as DevSecOps and financial systems. Watch for 2022 as the year AIOps goes from drawing board to shop floor.
Michael Nappi
CPO, ScienceLogic

AIOPS CLOSES SILOS BETWEEN OPS TEAMS

IT operations teams are finding it more difficult to rely on rule-based approaches alone for preventing and managing outages and alerts. Over the next year, we anticipate seeing a growing demand for AI models to be explainable when it comes to recommendations around diagnostics and solutions. Additionally, we believe AIOps will be a critical component to closing the silos between operations teams and enabling them to grow their skillset by offering better visibility across application, infrastructure and network domains.
Girish Muckai
Chief Sales & Marketing Officer, HEAL Software

DEMOCRATIZATION OF DOMAIN-AGNOSTIC AIOPS

In the 2022 timeframe, we will continue to see the democratization of domain agnostic AIOps, as complex ML tools kits are replaced by cost-effective purpose-built tools that leverage no-code/low-code and bring powerful API's that encourage automation. A more specific area of innovation will be around the application of AI to change management, where ML can be used to perform risk and impact analysis on potential changes.
Richard Whitehead
Evangelist in Chief, CTO, Moogsoft

Closing the AI Talent Gap Will Give Rise to AIOps Adoption

AIOps introduces governance, scaling, and transparency issues. Do you have AI governance in place? How are you scaling? Is it transparent? What do you do when the model is drifting? There's a lot of issues specific to AI that people are uncovering, but don't know how to resolve because of the lack of skilled professionals in the space. This AI talent shortage presents a huge barrier to AIOps adoption. As we continue to train and hire skilled AI professionals, more organizations will have the capabilities to utilize big data, machine learning and other advanced analytics technologies to gather data from their application environment. With these insights, it will soon be possible for organizations to have a bird's eye view of their entire ecosystem, allowing them to proactively spot trends and issues that will enable them to proactively manage their IT operations.
Rachel Roumeliotis
VP of AI and Data Content Strategy, O'Reilly Media

AIOPS SOLVES STAFFING AND SKILLS GAP

As we get into 2022, ITOps leaders will be pressing their software providers to keep pushing the bounds on what AIOps can bring to bear. Faster root-cause isolation and automated remediations must be huge parts of this. Teams are not growing, and tech workers are changing jobs more frequently — especially with the great disruption continuing for the foreseeable future. The only way to fill the staffing and skills gap is by deploying smarter systems. AIOps can fill this gap and leaders who aren't planning a 2022 rollout of an AIOps platform risk hurting the business's bottom line.
Rich Lane
Chief Strategy Officer, Netenrich

AIOPS STRUGGLES IN THE CLOUD

AIOps will continue to struggle with the increasing rate of change in modern cloud environments. High deployment frequencies coupled with cloud-native product velocities makes it increasingly difficult to baseline systems and discover useful patterns.
Tobias KunzeCEO and Co-Founder, Glasnostic

AIOps Will Be Dismantled, Repackaged and Rebranded

AIOps is an amalgamation of capabilities with some being more hype than hope. Considering that 39% of DevOps' SRE said they've never used AIOps and 68% said received AIOps value was only "low" or "moderate", the shift to AIOps in its current form is slow. Capabilities like "self-heal" and "auto remedy" have largely failed to deliver on their promises whereas capabilities such as "correlation" and "root cause analysis" have not. The result: a repackaged set of capabilities (sans "self-heal") that usefully focus on actionability and the addition of active, observational data sources (e.g., business KPI and competitive intelligence benchmarking) will emerge as AIOps NextGen.
Leo Vasiliou
Director of Product Marketing, Catchpoint

Go to: 2022 Application Performance Management Predictions - Part 3, covering Observability predictions.

Hot Topics

The Latest

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

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 ...

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 ...

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 ...

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 ...

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 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.

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 ...

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 ...