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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

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...

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

Part 4 outlines the challenges enterprise face when adopting AI observability. The main challenge is that AI complexity compounds at every stage of maturity. LLM-based applications introduce new behavior, dependencies, and quality considerations, while Agentic AI adds further failure modes as systems make decisions and take actions ...

Part 3 reports on the current state of AI observability. Most experts agree: enterprises are moving faster with AI than they are with the operational discipline needed to run it safely ...

When discussing AI Observability, one of the first questions that arises: Is this just an extension of traditional observability or is this a whole new type of tool? Part 2 compares traditional observability with AI observability ...

In this extensive 14-part series that will post over the next three weeks, experts across observability and AI will provide insight and opinions on the need for AI Observability, the challenges, the benefits, the current state, and predictions for the future ...

Amid rising complexity, organizations are moving quickly toward agent-driven operations: AgenticOps, according to The Impact of Agentic AI on Network Operations, a new report from Cisco ...

AI, automation, and enterprise software give organizations an extraordinary amount of leverage. Work that once required hours can happen in minutes. Information buried across systems can be analyzed almost instantly. Complex workflows can be coordinated across teams with far less manual intervention. That leverage creates a temptation to start with the technology ...

Everyone wants to talk about models. Compute clusters. Benchmark scores that climb a fraction of a point and make headlines anyway. It's an easy conversation to have because it's measurable. But it's also increasingly beside the point ...

For more than a decade, the default infrastructure play has been simple. Move more workloads to the cloud, retire on-premises gear and treat the data center as a cost to shrink. That logic held while public cloud offered near-limitless capacity at predictable prices, and AI demand is now testing both halves of that assumption ...

Most enterprise IT leaders can tell you exactly how many applications are running across their organization. They can surface a dashboard showing telecom spend by region, pull a report on SaaS license utilization, or call up a real-time view of mobile device inventory. The data is there, and IT leaders have largely delivered on the promise of visibility. Yet, in most organizations, that visibility has not translated into business value ...

Government agencies are largely misjudging their level of AI governance as the technology permeates their organization, according to Racing to Deploy: How Public Sector AI Adoption is Moving Faster Than the Foundations Needed to Support It, a new report from SolarWinds ...