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.

Related Links

Hot Topics

The Latest

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...

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.

Related Links

Hot Topics

The Latest

How AI Agents Are Reshaping DataOps for the Always-On Enterprise

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 ...

AI Deepfakes: Rethinking Trust in the Workplace

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 ...

Nine in Ten Enterprises Plan Cloud Data Repatriation amid Rising Cloud Costs and Data Sovereignty Mandates

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 ...

Why ITOps Need Right-Sized AI, Not Bigger Models

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 ...

The End of Reactive DevOps: AI-Driven Observability for Zero-Defect Digital Systems

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

5 Takeaways from the Observability Forecast for Media and Entertainment

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Why Moving Off the Cloud Is the Easy Part and What Comes Next Is Where Things Get Hard

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

MEAN TIME TO INSIGHT Podcast - Episode 24: Network Observability Tool Sprawl

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

Capacity Isn't a Guess: Observability-Driven Sizing for On-Prem Databases

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

5 Security Principles Every Entrepreneur Should Apply to Leadership

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...