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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IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

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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

Immutable by Design: Reinventing Business Continuity and Disaster Recovery

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

Embracing Cost-Effective Observability Through an OpenTelemetry Approach

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Why Employees Hate Security (And What Businesses Can Do About It)

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Cloudbrink's Personal SASE services provide last-mile acceleration and reduction in latency

MEAN TIME TO INSIGHT Podcast - Episode 13: Hybrid Multi-Cloud Networking Strategy

In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ... 

Transforming Network Remediation with a Closed-Loop Approach

In high-traffic environments, the sheer volume and unpredictable nature of network incidents can quickly overwhelm even the most skilled teams, hindering their ability to react swiftly and effectively, potentially impacting service availability and overall business performance. This is where closed-loop remediation comes into the picture: an IT management concept designed to address the escalating complexity of modern networks ...

IT Leaders Are Leveraging AI Agents to Unlock Autonomous Transformation in 2025

In 2025, enterprise workflows are undergoing a seismic shift. Propelled by breakthroughs in generative AI (GenAI), large language models (LLMs), and natural language processing (NLP), a new paradigm is emerging — agentic AI. This technology is not just automating tasks; it's reimagining how organizations make decisions, engage customers, and operate at scale ...

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In the early days of the cloud revolution, business leaders perceived cloud services as a means of sidelining IT organizations. IT was too slow, too expensive, or incapable of supporting new technologies. With a team of developers, line of business managers could deploy new applications and services in the cloud. IT has been fighting to retake control ever since. Today, IT is back in the driver's seat, according to new research by Enterprise Management Associates (EMA) ...

5 Steps to Enhancing Network Observability for Your NOC

In today's fast-paced and increasingly complex network environments, Network Operations Centers (NOCs) are the backbone of ensuring continuous uptime, smooth service delivery, and rapid issue resolution. However, the challenges faced by NOC teams are only growing. In a recent study, 78% state network complexity has grown significantly over the last few years while 84% regularly learn about network issues from users. It is imperative we adopt a new approach to managing today's network experiences ...

Broadcom

Top Cloud Trends for 2025: Wrangling Cloud Spend, Expanding FinOps Teams, and Adopting GenAI

From growing reliance on FinOps teams to the increasing attention on artificial intelligence (AI), and software licensing, the Flexera 2025 State of the Cloud Report digs into how organizations are improving cloud spend efficiency, while tackling the complexities of emerging technologies ...

Data Migration Strategies for Optimizing Cloud Costs

Today, organizations are generating and processing more data than ever before. From training AI models to running complex analytics, massive datasets have become the backbone of innovation. However, as businesses embrace the cloud for its scalability and flexibility, a new challenge arises: managing the soaring costs of storing and processing this data ...