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AI Shines Bright in APM, But Challenges Remain

Sudhir Jha

Performance management of enterprise applications is key to achieving business objectives and maximizing returns on IT investments. But with increasing system complexity, rapidly evolving platforms, shorter time to market and inadequate quantitative models and tools, performance management often represents one of the most challenging aspects for enterprise IT.

The hype around artificial intelligence (AI) is beginning to settle, and companies are beginning to see measurable change from early investments in the technology. This initial success, however nascent, is silencing some of the doubt that AI would be able to deliver on its promise.

The deluge of data created by IT infrastructures that generate data every second often takes great investments of time to make sense of. Leveraging AI or machine learning technologies in Application Performance Management (APM) simplifies the complex IT systems, automates application-environment discovery and makes for smarter decisions faster with proactive problem resolution. The benefits of AI-driven APM solutions seem obvious but raise important questions around AI technologies and its greater impact.

To better understand the state of AI among enterprises across industries, Infosys commissioned a survey of more than 1,000 global C-level executives and IT decision makers (ITDMs). The survey focused on the impact AI deployments are having on organizations and reveals the return on investment (ROI) of current AI deployments, as well as its impact on leadership and the workforce.

The research, Leadership in the Age of AI Report, makes clear that AI technologies are no longer experimental, rather they are already broadly deployed, producing real results and impacting business strategy, IT investments, and the workforce.

AI Beyond Automation

The research found that 86 percent of organizations surveyed have middle or late-stage AI deployments and view AI as a major facilitator of future business operations.

Nine out of 10 C-level executives reported measurable benefits from AI within their organizations.

AI is dependent upon data quality and accessibility, making APM a critical underpinning to success of AI initiatives. AI is not a standalone application but part of a knowledge management ecosystem that involves layers of business and IT data. Machine learning, computer vision, and automated reasoning and other technologies may be part of that ecosystem.


The survey found the top strategic advantages organizations report from their AI deployments are improved process performance (45 percent), productivity gains due to IT time spent on higher-value work (40 percent) or related to fewer staff needed to accomplish analogous workloads (38 percent), improved compliance, security and risk management (38 percent).

But the positive effects of AI within organizations go beyond driving efficiencies, as three-fourths of C-level executives said they expect AI to impact their organization's offerings even more than they would impact organizational processes.

The research also showed that the majority of organizations start off using AI to automate or improve routine or inefficient processes with 66 percent of organizations primarily leveraging AI for business process automation.

When looking at companies with later stage AI deployments, 80 percent of IT decision makers (ITDMs) reported they are using AI to augment existing solutions or build new business-critical solutions and services to optimize insights and the consumer experience. Here, APM can play a significant role in how these insights are derived.

Challenges in the Age of AI

AI may end up being a boon to APM solutions — but that's not to say there won't be challenges to address in the new age of AI. Companies need to ensure that their most important investment, their people, are prepared for a future fueled by automation and are equipped with the necessary skills for the new roles AI will create. Additionally, companies need to provide the necessary resources and time to support the learning curve that comes with these technologies.

IT has been the primary focus of AI initiatives, and will continue to be for the foreseeable future. The survey found 61 percent of respondents agreed that IT will be the most impacted job function over the next five years. As IT departments continue to implement AI-based tools, they will become imperative for modern IT operations.

Similar to APM solutions, data largely underpins the successful use of AI. Another challenge important to note as the age of AI takes hold is 49 percent of respondents reported that their organization is unable to deploy the AI technologies they want because their data is not ready to support them. As such, 77 percent of ITDMs reported they plan to invest in data management.

Artificial Intelligence is rapidly being adopted by companies across industries and most would agree that it holds the potential to unlock benefits currently untapped by existing IT. So far, the arc of AI leans toward empowerment and giving IT and business organizations the tools necessary to automate redundant tasks, detect and analyze hidden patterns in data and generally make possible revolutionary insights that will help achieve objectives.

Hot Topics

The Latest

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 ...

 

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 ...

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 ...

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 ...

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 ...

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? ...

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 ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ... 

AI Shines Bright in APM, But Challenges Remain

Sudhir Jha

Performance management of enterprise applications is key to achieving business objectives and maximizing returns on IT investments. But with increasing system complexity, rapidly evolving platforms, shorter time to market and inadequate quantitative models and tools, performance management often represents one of the most challenging aspects for enterprise IT.

The hype around artificial intelligence (AI) is beginning to settle, and companies are beginning to see measurable change from early investments in the technology. This initial success, however nascent, is silencing some of the doubt that AI would be able to deliver on its promise.

The deluge of data created by IT infrastructures that generate data every second often takes great investments of time to make sense of. Leveraging AI or machine learning technologies in Application Performance Management (APM) simplifies the complex IT systems, automates application-environment discovery and makes for smarter decisions faster with proactive problem resolution. The benefits of AI-driven APM solutions seem obvious but raise important questions around AI technologies and its greater impact.

To better understand the state of AI among enterprises across industries, Infosys commissioned a survey of more than 1,000 global C-level executives and IT decision makers (ITDMs). The survey focused on the impact AI deployments are having on organizations and reveals the return on investment (ROI) of current AI deployments, as well as its impact on leadership and the workforce.

The research, Leadership in the Age of AI Report, makes clear that AI technologies are no longer experimental, rather they are already broadly deployed, producing real results and impacting business strategy, IT investments, and the workforce.

AI Beyond Automation

The research found that 86 percent of organizations surveyed have middle or late-stage AI deployments and view AI as a major facilitator of future business operations.

Nine out of 10 C-level executives reported measurable benefits from AI within their organizations.

AI is dependent upon data quality and accessibility, making APM a critical underpinning to success of AI initiatives. AI is not a standalone application but part of a knowledge management ecosystem that involves layers of business and IT data. Machine learning, computer vision, and automated reasoning and other technologies may be part of that ecosystem.


The survey found the top strategic advantages organizations report from their AI deployments are improved process performance (45 percent), productivity gains due to IT time spent on higher-value work (40 percent) or related to fewer staff needed to accomplish analogous workloads (38 percent), improved compliance, security and risk management (38 percent).

But the positive effects of AI within organizations go beyond driving efficiencies, as three-fourths of C-level executives said they expect AI to impact their organization's offerings even more than they would impact organizational processes.

The research also showed that the majority of organizations start off using AI to automate or improve routine or inefficient processes with 66 percent of organizations primarily leveraging AI for business process automation.

When looking at companies with later stage AI deployments, 80 percent of IT decision makers (ITDMs) reported they are using AI to augment existing solutions or build new business-critical solutions and services to optimize insights and the consumer experience. Here, APM can play a significant role in how these insights are derived.

Challenges in the Age of AI

AI may end up being a boon to APM solutions — but that's not to say there won't be challenges to address in the new age of AI. Companies need to ensure that their most important investment, their people, are prepared for a future fueled by automation and are equipped with the necessary skills for the new roles AI will create. Additionally, companies need to provide the necessary resources and time to support the learning curve that comes with these technologies.

IT has been the primary focus of AI initiatives, and will continue to be for the foreseeable future. The survey found 61 percent of respondents agreed that IT will be the most impacted job function over the next five years. As IT departments continue to implement AI-based tools, they will become imperative for modern IT operations.

Similar to APM solutions, data largely underpins the successful use of AI. Another challenge important to note as the age of AI takes hold is 49 percent of respondents reported that their organization is unable to deploy the AI technologies they want because their data is not ready to support them. As such, 77 percent of ITDMs reported they plan to invest in data management.

Artificial Intelligence is rapidly being adopted by companies across industries and most would agree that it holds the potential to unlock benefits currently untapped by existing IT. So far, the arc of AI leans toward empowerment and giving IT and business organizations the tools necessary to automate redundant tasks, detect and analyze hidden patterns in data and generally make possible revolutionary insights that will help achieve objectives.

Hot Topics

The Latest

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 ...

 

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 ...

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 ...

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 ...

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 ...

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? ...

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 ...

Enterprise networks rarely behave the same way for very long. A routing adjustment in one region may unexpectedly alter application performance in another. A cloud migration may introduce hidden dependencies that go unnoticed until an outage occurs. All the while, the network is managed by several different teams, each of whom use different tool sets — and as a result, have different views of the network ... There’s usually an engineer who remembers why traffic fails over a certain way between sites, or which transparent firewall was added where. The problem is that human memory cannot scale alongside enterprise-scale networks ...

Ask an infrastructure team how confident they are in their ability to govern AI, and most will tell you they've got it handled. A recent survey of 406 IT decision-makers and platform engineering leaders found 86% expressing exactly that confidence. Ask the same group whether they have a formal written AI governance policy, and the number drops to 30%, according to Spacelift's Infrastructure Automation Report ...

In MEAN TIME TO INSIGHT Episode 27, Shamus McGillicuddy, EMA VP of Research, Network Infrastructure and Operations, and Parker Hathcock, EMA Research Director covering IT Service/Operations (ServiceOps), discuss observability unification in modern IT operations ...