Skip to main content

Full-Stack Observability in 2024 and the Importance of End-to-End Visibility for IT Teams

Full-stack observability is key to deliver a seamless digital experience and here's why
Gregg Ostrowski
AppDynamics

Applications and digital services have become a core to how we live, work and play, providing more convenient and intuitive solutions for tedious tasks. However, end user expectations have accelerated over the past two years, putting pressure on IT teams to develop and maintain seamless, always on services.

In our latest research, Cisco's The App Attention Index 2023: Beware the Application Generation, 62% of consumers report their expectations for digital experiences are far higher than they were two years ago, and 64% state they are less forgiving of poor digital services than they were just 12 months ago. People have enjoyed the benefits of the most innovative, seamless and secure applications and digital services available and will no longer tolerate poor performance.

In today's competitive digital marketplace, IT teams need a multi-purpose solution to help optimize the digital experience. By taking this modern approach to application performance, IT teams will be equipped to quickly detect and resolve possible bottlenecks across even the most complex and fragmented IT environments before the end user is impacted.

The Focus Has Shifted from Quantity to Quality

77% have stopped using digital services or deleted applications from their devices because of a performance issue

The research revealed the digital experience is now imperative for business success, as people's reactions to failed or underperforming applications has grown stronger. Over the last 12 months, 77% have stopped using digital services or deleted applications from their devices because of a performance issue. Consumers are also more likely to share their negative experiences, with 67% saying they would warn others about applications that fail to perform. This intel alone shows positive digital experiences are now a business imperative.

But, just as expectations for seamless digital experiences are rising, instances of digital disruption are also becoming more frequent. As many as 88% of consumers report they have experienced performance issues when using applications over the past 12 months, which is a significant increase from the 81% recorded in 2021. The introduction of more complex cloud environments, increased security risks, and growing user numbers are likely all factors contributing to the rise in digital disruption. And for many IT teams, the task of optimizing application availability, performance and security to address these concerns is easier said than done.

Full-Stack Observability: The Multi-Purpose Solution for IT Teams

IT teams are hindered by increasingly complex and fragmented environments. With the widespread adoption of cloud native technologies, IT teams have had to manage broader, more intricate application infrastructures spanning across both cloud and on-premises. For most, this has been a rapid transformation, leaving IT teams without all the necessary tools and insights needed to optimize application performance in a hybrid environment.

The more complexity you introduce, the harder it is to have complete visibility. IT teams are struggling with cloud native technologies such as Kubernetes, and they can't get a clear line of sight for applications where components are running across hybrid environments. This has made it near impossible for them to quickly identify and fix performance and security issues in real time, before they impact the end user experience.

Full-stack observability can help technology leaders and their teams overcome these challenges by generating full and unified visibility. As cloud adoption and optimization continues to trend in 2024, we should similarly expect observability to be a critical part of modern business strategies. With full-stack observability, IT teams can receive performance updates in real time, so they can rapidly detect problems, understand what's causing them and avoid downtime.

Another key advantage of full-stack observability is the ability to correlate the application performance and security data with key business metrics. Over the last year, telemetry data has emerged as an invaluable instrument to extract, collate and analyze performance data at every layer of a distributed system. It's a component of observability we expect will further mature in 2024, as this data can help IT teams identify, prioritize, and most importantly, take action on the issues that will have the biggest impact on digital experience. Rather than firefighting an overwhelming number of alerts, IT teams can take a more proactive, thoughtful approach, focused on what will deliver the biggest impact to customers and the business.

There is an appetite for applications and digital services to deliver the best performance, but modern IT environments are getting more complex. IT teams need a unified solution that can centralize and correlate availability, performance and security data from across hybrid infrastructures. With full-stack observability, technology leaders can equip their teams with the tools and insights needed to meet this soaring demand for increasingly intuitive, seamless and secure digital experiences.

Gregg Ostrowski is CTO Advisor at Cisco AppDynamics

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Full-Stack Observability in 2024 and the Importance of End-to-End Visibility for IT Teams

Full-stack observability is key to deliver a seamless digital experience and here's why
Gregg Ostrowski
AppDynamics

Applications and digital services have become a core to how we live, work and play, providing more convenient and intuitive solutions for tedious tasks. However, end user expectations have accelerated over the past two years, putting pressure on IT teams to develop and maintain seamless, always on services.

In our latest research, Cisco's The App Attention Index 2023: Beware the Application Generation, 62% of consumers report their expectations for digital experiences are far higher than they were two years ago, and 64% state they are less forgiving of poor digital services than they were just 12 months ago. People have enjoyed the benefits of the most innovative, seamless and secure applications and digital services available and will no longer tolerate poor performance.

In today's competitive digital marketplace, IT teams need a multi-purpose solution to help optimize the digital experience. By taking this modern approach to application performance, IT teams will be equipped to quickly detect and resolve possible bottlenecks across even the most complex and fragmented IT environments before the end user is impacted.

The Focus Has Shifted from Quantity to Quality

77% have stopped using digital services or deleted applications from their devices because of a performance issue

The research revealed the digital experience is now imperative for business success, as people's reactions to failed or underperforming applications has grown stronger. Over the last 12 months, 77% have stopped using digital services or deleted applications from their devices because of a performance issue. Consumers are also more likely to share their negative experiences, with 67% saying they would warn others about applications that fail to perform. This intel alone shows positive digital experiences are now a business imperative.

But, just as expectations for seamless digital experiences are rising, instances of digital disruption are also becoming more frequent. As many as 88% of consumers report they have experienced performance issues when using applications over the past 12 months, which is a significant increase from the 81% recorded in 2021. The introduction of more complex cloud environments, increased security risks, and growing user numbers are likely all factors contributing to the rise in digital disruption. And for many IT teams, the task of optimizing application availability, performance and security to address these concerns is easier said than done.

Full-Stack Observability: The Multi-Purpose Solution for IT Teams

IT teams are hindered by increasingly complex and fragmented environments. With the widespread adoption of cloud native technologies, IT teams have had to manage broader, more intricate application infrastructures spanning across both cloud and on-premises. For most, this has been a rapid transformation, leaving IT teams without all the necessary tools and insights needed to optimize application performance in a hybrid environment.

The more complexity you introduce, the harder it is to have complete visibility. IT teams are struggling with cloud native technologies such as Kubernetes, and they can't get a clear line of sight for applications where components are running across hybrid environments. This has made it near impossible for them to quickly identify and fix performance and security issues in real time, before they impact the end user experience.

Full-stack observability can help technology leaders and their teams overcome these challenges by generating full and unified visibility. As cloud adoption and optimization continues to trend in 2024, we should similarly expect observability to be a critical part of modern business strategies. With full-stack observability, IT teams can receive performance updates in real time, so they can rapidly detect problems, understand what's causing them and avoid downtime.

Another key advantage of full-stack observability is the ability to correlate the application performance and security data with key business metrics. Over the last year, telemetry data has emerged as an invaluable instrument to extract, collate and analyze performance data at every layer of a distributed system. It's a component of observability we expect will further mature in 2024, as this data can help IT teams identify, prioritize, and most importantly, take action on the issues that will have the biggest impact on digital experience. Rather than firefighting an overwhelming number of alerts, IT teams can take a more proactive, thoughtful approach, focused on what will deliver the biggest impact to customers and the business.

There is an appetite for applications and digital services to deliver the best performance, but modern IT environments are getting more complex. IT teams need a unified solution that can centralize and correlate availability, performance and security data from across hybrid infrastructures. With full-stack observability, technology leaders can equip their teams with the tools and insights needed to meet this soaring demand for increasingly intuitive, seamless and secure digital experiences.

Gregg Ostrowski is CTO Advisor at Cisco AppDynamics

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...