2018 Application Performance Management Predictions - Part 5

Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2018. Part 5 covers NoOps, Analytics, Machine Learning and AI.

Start with 2018 Application Performance Management Predictions - Part 1

Start with 2018 Application Performance Management Predictions - Part 2

Start with 2018 Application Performance Management Predictions - Part 3

Start with 2018 Application Performance Management Predictions - Part 4

AUTONOMOUS OPERATIONS: NO OPS

2018 will mark the year of blending APM intelligence, as just another data source, into the ultimate IT business goal: Autonomous Operations, also called NoOps by Forrester. These AI-powered, automated and autonomous systems will automate deployment, monitoring, management, securing and remediation of IT environment. If your current APM solution is not already integrated/capable of integrating into these larger systems, you'll want to use 2018 to get yourself acquainted and start your projects. The future starts now.
Daniel Schrijver
Senior Principal Product Marketing Director, Oracle

NO OPS NO LONGER

"NoOps" will no longer be a thing as infrastructure and operations/run teams become more involved in the development aspects of the software engineering and take back the Ops.
Alex Popov
Cloud Enablement and Continuous Delivery, Barclaycard

MACHINE LEARNING

The New Focus: Proactive, Not Reactive. In today's fast-changing, dynamic virtual environments, IT managers can no longer afford to be reactive or to use trial-and-error to address issues. As 2018 progresses, IT management will be able to take full advantage of the holistic, predictive analytics that new machine-learning based tools enable. These tools can predict and even recommend steps to avoid a variety of issues that can take IT application owners by surprise with costly results. For example, IT can use these tools to eliminate application performance issues, threats to failovers and unexpected capacity usage.
Jerry Melnick
President & CEO, SIOS Technology

New global research from Quocirca, Damage Control: The Impact of Critical IT Incidents, shows that improved operational intelligence, driven by machine data, will continue reduce the impact of critical IT incidents in 2018. The average organization records 1,208 IT incidents per month, 5 of which turn out to be critical. In particular, operational intelligence reduces the number of duplicate incidents through machine learning and repeat incidents through improved root cause analysis.
Bob Tarzey
Independent Analyst and Freelance Writer, Quocirca

2018 will see the adoption of AI, in the form of machine learning, by major software vendors who will be embedding it within their core applications. This machine learning will also become a standard platform for data analytics for new development initiatives. The IoT market will take greatest advantage from this adoption, as the volume of data needing analysis grows exponentially.
Sven Hammar
Founder and CSO, Apica

Read Sven Hammar's Blog: What's Ahead for the Software Testing Industry in 2018?

AI

Nearly all IT management product companies are now claiming to be AI driven. Analysts are declaring AI to be a strategic requirement. CIOs are demanding AI products. The market will start to go beyond buzzwords and hype, and focus on how intelligent automation can be used.
Tom Joyce
CEO, Pensa

2017 saw virtual assistants and chatbots popping up a bit more regularly, though mostly confined to the advanced enterprise ITSM and help desk platforms. In 2018 AI-based tools like these will trickle down to more midmarket ITSM products. They'll also be the basis for one or two new ITSM best-of-breed and application startups.
Craig Borowski
Content Analyst, Software Advice (a Gartner Company)

As more enterprises move toward deploying IoT for business applications, AI and machine learning will become imperative, rather than optional. AI will gain more prominence as an enabler of improved ITSM, self-service offerings, and as a necessary element in digital transformation initiatives.
Marcel Shaw
Engineer, Federal Markets, Ivanti

Companies are having trouble keeping up with consumers' desire for innovation. Better, sleeker, faster seems to be in constant demand — and all with a flawless experience. But, old legacy apps weren't built for this modern wave of digital users. They just don't work at speed or scale — at least without performance issues that cause more abandon rates than signups. So, companies are rebuilding their legacy apps on the cloud. But, these rapid changes have given rise to complex IT ecosystems, which make it difficult to monitor digital performance and manage the user-experience effectively — at least by using traditional tools. That's why, in 2018, AI will become critical in IT's ability to master increasing IT complexity in order to deliver on consumer demands. Organizations will look to AI to automate all the heavy lifting and proactively identify problems so that they can pinpoint the underlying root cause of any issues before their customers are impacted.
Alois Reitbauer
Chief Technology Strategist, Dynatrace

Despite the hype, AI has demonstrated value in industries across the board — from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making. AI's success will continue in the new year, specifically in a new area: troubleshooting. Expect to see an impact on troubleshooting for operators, data centers, etc. as AI helps individuals tackle the day-to-day issues, enabling them to focus on critical problems that AI itself can't help. In 2018, AI will guide and augment humans in solving hard problems as it further cements its value-add as a human cognitive partner, guiding us through the trees to make more impactful decisions.
Ash Munshi
CEO, Pepperdata

AIOPS

Continued adoption of machine learning, data science principles, and big data techniques that will improve pattern discovery, anomaly detection and root cause analysis. Because of this, AIOps/ITOA will play a larger role.
David Ishmael
Director of IT Operations Analytics, Trace3

Artificial intelligence will evolve IT by seeing predictive analytics replace manually intensive activities with intelligent automation. This evolution has been coined AIOps. This will allow organizations to leverage data and AI to quickly identify problems, provide recommendations on how to resolve existing issues, streamline automation with self-service and self-recovery capabilities, and predict future outcomes to forecast costs. AIOps will take IT operations analytics (ITOA) to the next level by automatically applying insights to ensure high performing IT environments are proactively making decisions that ultimately improve the health of the business.
Rick Fitz
SVP and GM of IT Markets, Splunk

AI LIMITATIONS

In 2018, we expect to see a growing realization of the limitations of today's AI for IT issue identification and resolution. As the number of performance-impacting elements (and IT complexity) increases, AI can be helpful in identifying some problem spots, but human intervention will always be needed to discern what (if any) issues are truly customer-impacting and thus warrant a call to IT teams in the middle of the night. For example, let's say a front-end server is slowing down. Are customers growing angered? Are revenues in danger? Or can the issue wait until the morning? These are things that a machine can't necessarily learn. AI without guided human intervention can actually have the adverse impact of desensitizing IT staffs and making them less effective.
Mehdi Daoudi
CEO and Founder, Catchpoint

CONVERGENCE OF ITOA AND BI

We expect a convergence of IT Operations Analytics (ITOA) and Business Intelligence (BI), with AI as the bridge. AI allows for the analysis of every metric at the most granular level while still correlating them across disparate data sources. With excessively large amounts of data, traditional dashboards become slow and overwhelming containing many false and missed alerts. The only way to track, learn and derive insights from all of the available data is to use AI. Once you have an AI system evaluating the IT and business metrics, unified alerts can identify true insights, and companies will have access to a "single pane of glass" so that both business and technology executives can have a clear understanding of every aspect of the business.
David Drai
CEO, Anodot

Read 2018 Application Performance Management Predictions - Part 6, covering more about ITOA and data.

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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 ...

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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 ...

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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 ...

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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 ...

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2018 Application Performance Management Predictions - Part 5

Industry experts — from analysts and consultants to users and the top vendors — offer thoughtful, insightful, and often controversial predictions on how APM and related technologies will evolve and impact business in 2018. Part 5 covers NoOps, Analytics, Machine Learning and AI.

Start with 2018 Application Performance Management Predictions - Part 1

Start with 2018 Application Performance Management Predictions - Part 2

Start with 2018 Application Performance Management Predictions - Part 3

Start with 2018 Application Performance Management Predictions - Part 4

AUTONOMOUS OPERATIONS: NO OPS

2018 will mark the year of blending APM intelligence, as just another data source, into the ultimate IT business goal: Autonomous Operations, also called NoOps by Forrester. These AI-powered, automated and autonomous systems will automate deployment, monitoring, management, securing and remediation of IT environment. If your current APM solution is not already integrated/capable of integrating into these larger systems, you'll want to use 2018 to get yourself acquainted and start your projects. The future starts now.
Daniel Schrijver
Senior Principal Product Marketing Director, Oracle

NO OPS NO LONGER

"NoOps" will no longer be a thing as infrastructure and operations/run teams become more involved in the development aspects of the software engineering and take back the Ops.
Alex Popov
Cloud Enablement and Continuous Delivery, Barclaycard

MACHINE LEARNING

The New Focus: Proactive, Not Reactive. In today's fast-changing, dynamic virtual environments, IT managers can no longer afford to be reactive or to use trial-and-error to address issues. As 2018 progresses, IT management will be able to take full advantage of the holistic, predictive analytics that new machine-learning based tools enable. These tools can predict and even recommend steps to avoid a variety of issues that can take IT application owners by surprise with costly results. For example, IT can use these tools to eliminate application performance issues, threats to failovers and unexpected capacity usage.
Jerry Melnick
President & CEO, SIOS Technology

New global research from Quocirca, Damage Control: The Impact of Critical IT Incidents, shows that improved operational intelligence, driven by machine data, will continue reduce the impact of critical IT incidents in 2018. The average organization records 1,208 IT incidents per month, 5 of which turn out to be critical. In particular, operational intelligence reduces the number of duplicate incidents through machine learning and repeat incidents through improved root cause analysis.
Bob Tarzey
Independent Analyst and Freelance Writer, Quocirca

2018 will see the adoption of AI, in the form of machine learning, by major software vendors who will be embedding it within their core applications. This machine learning will also become a standard platform for data analytics for new development initiatives. The IoT market will take greatest advantage from this adoption, as the volume of data needing analysis grows exponentially.
Sven Hammar
Founder and CSO, Apica

Read Sven Hammar's Blog: What's Ahead for the Software Testing Industry in 2018?

AI

Nearly all IT management product companies are now claiming to be AI driven. Analysts are declaring AI to be a strategic requirement. CIOs are demanding AI products. The market will start to go beyond buzzwords and hype, and focus on how intelligent automation can be used.
Tom Joyce
CEO, Pensa

2017 saw virtual assistants and chatbots popping up a bit more regularly, though mostly confined to the advanced enterprise ITSM and help desk platforms. In 2018 AI-based tools like these will trickle down to more midmarket ITSM products. They'll also be the basis for one or two new ITSM best-of-breed and application startups.
Craig Borowski
Content Analyst, Software Advice (a Gartner Company)

As more enterprises move toward deploying IoT for business applications, AI and machine learning will become imperative, rather than optional. AI will gain more prominence as an enabler of improved ITSM, self-service offerings, and as a necessary element in digital transformation initiatives.
Marcel Shaw
Engineer, Federal Markets, Ivanti

Companies are having trouble keeping up with consumers' desire for innovation. Better, sleeker, faster seems to be in constant demand — and all with a flawless experience. But, old legacy apps weren't built for this modern wave of digital users. They just don't work at speed or scale — at least without performance issues that cause more abandon rates than signups. So, companies are rebuilding their legacy apps on the cloud. But, these rapid changes have given rise to complex IT ecosystems, which make it difficult to monitor digital performance and manage the user-experience effectively — at least by using traditional tools. That's why, in 2018, AI will become critical in IT's ability to master increasing IT complexity in order to deliver on consumer demands. Organizations will look to AI to automate all the heavy lifting and proactively identify problems so that they can pinpoint the underlying root cause of any issues before their customers are impacted.
Alois Reitbauer
Chief Technology Strategist, Dynatrace

Despite the hype, AI has demonstrated value in industries across the board — from agriculture to biotech to manufacturing. AI is just beginning to ingest data to power services and offerings, in turn providing information necessary for better decision-making. AI's success will continue in the new year, specifically in a new area: troubleshooting. Expect to see an impact on troubleshooting for operators, data centers, etc. as AI helps individuals tackle the day-to-day issues, enabling them to focus on critical problems that AI itself can't help. In 2018, AI will guide and augment humans in solving hard problems as it further cements its value-add as a human cognitive partner, guiding us through the trees to make more impactful decisions.
Ash Munshi
CEO, Pepperdata

AIOPS

Continued adoption of machine learning, data science principles, and big data techniques that will improve pattern discovery, anomaly detection and root cause analysis. Because of this, AIOps/ITOA will play a larger role.
David Ishmael
Director of IT Operations Analytics, Trace3

Artificial intelligence will evolve IT by seeing predictive analytics replace manually intensive activities with intelligent automation. This evolution has been coined AIOps. This will allow organizations to leverage data and AI to quickly identify problems, provide recommendations on how to resolve existing issues, streamline automation with self-service and self-recovery capabilities, and predict future outcomes to forecast costs. AIOps will take IT operations analytics (ITOA) to the next level by automatically applying insights to ensure high performing IT environments are proactively making decisions that ultimately improve the health of the business.
Rick Fitz
SVP and GM of IT Markets, Splunk

AI LIMITATIONS

In 2018, we expect to see a growing realization of the limitations of today's AI for IT issue identification and resolution. As the number of performance-impacting elements (and IT complexity) increases, AI can be helpful in identifying some problem spots, but human intervention will always be needed to discern what (if any) issues are truly customer-impacting and thus warrant a call to IT teams in the middle of the night. For example, let's say a front-end server is slowing down. Are customers growing angered? Are revenues in danger? Or can the issue wait until the morning? These are things that a machine can't necessarily learn. AI without guided human intervention can actually have the adverse impact of desensitizing IT staffs and making them less effective.
Mehdi Daoudi
CEO and Founder, Catchpoint

CONVERGENCE OF ITOA AND BI

We expect a convergence of IT Operations Analytics (ITOA) and Business Intelligence (BI), with AI as the bridge. AI allows for the analysis of every metric at the most granular level while still correlating them across disparate data sources. With excessively large amounts of data, traditional dashboards become slow and overwhelming containing many false and missed alerts. The only way to track, learn and derive insights from all of the available data is to use AI. Once you have an AI system evaluating the IT and business metrics, unified alerts can identify true insights, and companies will have access to a "single pane of glass" so that both business and technology executives can have a clear understanding of every aspect of the business.
David Drai
CEO, Anodot

Read 2018 Application Performance Management Predictions - Part 6, covering more about ITOA and data.

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 ...