2016 Application Performance Management Predictions - Part 2

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 2016. Part 2 features visions of expanded capabilities of Application Performance Management solutions.

Start with 2016 Application Performance Management Predictions - Part 1

APM SUPPORTS INTERNET OF THINGS

With growing and astronomical opportunities in IoT, coupled with the fact that IoT is driven by massive scale software and data interactions, APM technologies are directly applicable to these new software use cases. Analytics and transaction tracing are key to extract and get value out of these new, emerging, and exciting platforms. In 2016 we will see this become a reality, whereas today it's in its nascency.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

A COMPLETE MONITORING SOLUTION

I foresee the (3) main factions of APM (Wire Data Analytics, Synthetic Transactions, and Agent Code Instrumentation) coming together to round out the monitoring spectrum as a complete solution. Wire Data Analytics will automatically discover and decipher all applications coming from a simple network tap. Synthetic Transactions will be made easier with recording features that support advanced browser functions which will be seamless for all mobile devices. Finally, there will be a new agent concept which is lightweight, deploys quickly, and goes in virtually undetected with zero configuration.
Larry Dragich
Director of Customer Experience Management at the Auto Club Group and Founder of the APM Strategies Group on LinkedIn.

For a better understanding of the (3) main factions of APM read: Slow Applications are Criminal

APM OFFERS DEEP DIVE VISIBILITY

APM requirements will expand past the capabilities of transaction visibility and real user monitoring, seeking deep-dive visibility across the entire infrastructure. Troubleshooting performance issues requires extensive expertise inside each and every tier, and enabling performance diagnosis to be accomplished with minimal human expertise requires a great deal of automation. In response to this demand, APM tools must go beyond user experience monitoring and transaction tracing with in-depth insights and domain expertise into every layer and every tier of the infrastructure. These tools will need to be easy to set up and use, to keep the barriers to adoption as low as possible.
Srinivas Ramanathan
CEO, eG Innovations

5 Predictions for Application Performance Management in 2016

DATABASE BECOMES APPLICATION PERFORMANCE FOCUS

With application performance the heart of most businesses, 2016 will see APM professionals start doing a much better job at realizing that, in turn, the database is the heart of application performance. As a result, the importance of having shared visibility into how databases affect application performance and the need to embrace response time analysis to identify bottlenecks will only grow in the coming year.
Gerardo Dada
VP, Product Marketing and Strategy, SolarWinds

THE NEED FOR SPEED

2016 will be the year of Fast Insights in APM. We have big data, small data, analytics, user experience data, etc. But what is the point of having all that data if I have to wait to figure out what happened that caused a performance outage or degradation, or even worse not have a clue that something happened? So 2016 will be about how do I get the information I need to have to make the right decision, right now. With customer experience more critical to business success than ever, IT's ability to respond to issues faster or pre-empt them altogether has never been more crucial.
Drit Suljoti
Chief Product Officer, Catchpoint

APM STRATEGIES INCLUDE MAINFRAME

Due to the sheer number of transactions and users impacted, even slight mainframe code optimizations can have a hugely positive experience on customer experience. For these reasons, modern IT teams will increasingly extend APM strategies to include the mainframe, identifying opportunities for improved customer experience and cost optimizations. Deep insights into mainframe code can also help IT teams identify and fix inefficiencies that may be driving up mainframe licensing costs unnecessarily.
Christopher O'Malley
President and CEO, Compuware

APM INTEGRATES WITH HELPDESK AND PRODUCT MANAGEMENT SYSTEM

To continue to be successful and increase adoption it is imperative that good (automated) integration is developed between the APM system, the Helpdesk system and the Product/Issue management systems used by developers.
Frank Puranik
Senior Technical Specialist, iTrinegy

THE RENAISSANCE OF BSM

The emergence of provisioning tools that can be leveraged to perform low maintenance, automatically updating dependency maps and CMDBs – leading to the renaissance of Business Service Management (BSM).
Grant Glading
Sales & Marketing Director, Interlink Software

Read 2016 Application Performance Management Predictions - Part 3

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Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

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When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

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Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

2016 Application Performance Management Predictions - Part 2

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 2016. Part 2 features visions of expanded capabilities of Application Performance Management solutions.

Start with 2016 Application Performance Management Predictions - Part 1

APM SUPPORTS INTERNET OF THINGS

With growing and astronomical opportunities in IoT, coupled with the fact that IoT is driven by massive scale software and data interactions, APM technologies are directly applicable to these new software use cases. Analytics and transaction tracing are key to extract and get value out of these new, emerging, and exciting platforms. In 2016 we will see this become a reality, whereas today it's in its nascency.
Jonah Kowall
VP of Market Development and Insights, AppDynamics

A COMPLETE MONITORING SOLUTION

I foresee the (3) main factions of APM (Wire Data Analytics, Synthetic Transactions, and Agent Code Instrumentation) coming together to round out the monitoring spectrum as a complete solution. Wire Data Analytics will automatically discover and decipher all applications coming from a simple network tap. Synthetic Transactions will be made easier with recording features that support advanced browser functions which will be seamless for all mobile devices. Finally, there will be a new agent concept which is lightweight, deploys quickly, and goes in virtually undetected with zero configuration.
Larry Dragich
Director of Customer Experience Management at the Auto Club Group and Founder of the APM Strategies Group on LinkedIn.

For a better understanding of the (3) main factions of APM read: Slow Applications are Criminal

APM OFFERS DEEP DIVE VISIBILITY

APM requirements will expand past the capabilities of transaction visibility and real user monitoring, seeking deep-dive visibility across the entire infrastructure. Troubleshooting performance issues requires extensive expertise inside each and every tier, and enabling performance diagnosis to be accomplished with minimal human expertise requires a great deal of automation. In response to this demand, APM tools must go beyond user experience monitoring and transaction tracing with in-depth insights and domain expertise into every layer and every tier of the infrastructure. These tools will need to be easy to set up and use, to keep the barriers to adoption as low as possible.
Srinivas Ramanathan
CEO, eG Innovations

5 Predictions for Application Performance Management in 2016

DATABASE BECOMES APPLICATION PERFORMANCE FOCUS

With application performance the heart of most businesses, 2016 will see APM professionals start doing a much better job at realizing that, in turn, the database is the heart of application performance. As a result, the importance of having shared visibility into how databases affect application performance and the need to embrace response time analysis to identify bottlenecks will only grow in the coming year.
Gerardo Dada
VP, Product Marketing and Strategy, SolarWinds

THE NEED FOR SPEED

2016 will be the year of Fast Insights in APM. We have big data, small data, analytics, user experience data, etc. But what is the point of having all that data if I have to wait to figure out what happened that caused a performance outage or degradation, or even worse not have a clue that something happened? So 2016 will be about how do I get the information I need to have to make the right decision, right now. With customer experience more critical to business success than ever, IT's ability to respond to issues faster or pre-empt them altogether has never been more crucial.
Drit Suljoti
Chief Product Officer, Catchpoint

APM STRATEGIES INCLUDE MAINFRAME

Due to the sheer number of transactions and users impacted, even slight mainframe code optimizations can have a hugely positive experience on customer experience. For these reasons, modern IT teams will increasingly extend APM strategies to include the mainframe, identifying opportunities for improved customer experience and cost optimizations. Deep insights into mainframe code can also help IT teams identify and fix inefficiencies that may be driving up mainframe licensing costs unnecessarily.
Christopher O'Malley
President and CEO, Compuware

APM INTEGRATES WITH HELPDESK AND PRODUCT MANAGEMENT SYSTEM

To continue to be successful and increase adoption it is imperative that good (automated) integration is developed between the APM system, the Helpdesk system and the Product/Issue management systems used by developers.
Frank Puranik
Senior Technical Specialist, iTrinegy

THE RENAISSANCE OF BSM

The emergence of provisioning tools that can be leveraged to perform low maintenance, automatically updating dependency maps and CMDBs – leading to the renaissance of Business Service Management (BSM).
Grant Glading
Sales & Marketing Director, Interlink Software

Read 2016 Application Performance Management Predictions - Part 3

Related Links

Hot Topics

The Latest

The Performance Bottleneck in Enterprise AI Has Been Exposed – Now We Must Fix It

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

What Capital Markets Can Teach Enterprises About Integrated Data Infrastructure

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

Why AI in IT Is Producing High Productivity but Heavy Workloads

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Implementing AI Isn't the Hard Part Anymore - Adoption Is

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

The Incident Load Is Rising, but Recovery Times Aren't Moving

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

Your AI Pipeline May Have a Document Observability Problem

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

The SIEM Isn't the Problem. Your Telemetry Architecture Is

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

As AI Scales Across Enterprises, Breaking Points Emerge

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

Preparing Network Infrastructure for AI Starts with the Basics

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Agentic Operations Begin with Governed Middleware Access

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...