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.

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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.

Related Links

Hot Topics

The Latest

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

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Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

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The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Empowering the Human Side of the Autonomous IT Age

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI Scale Is Outpacing Infrastructure - and IT Leaders Are Running Out of Time

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.