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As CIOs Address App Sprawl, Observability Can't Be an Afterthought

Bill Lobig
Bill Lobig is VP of Apptio / IBM IT Automation
IBM

App sprawl has been a concern for technologists for some time, but it has never presented such a challenge as now. As organizations move to implement generative AI into their applications, it's only going to become more complex. In fact, a recent Canva report found that 72% of CIOs see application sprawl as a challenge — and with 71% of CIOs expecting to adopt 30-60 new apps this year, this complexity is poised to keep growing.

Potential solutions include consolidating applications, optimizing workflows, and automating IT processes to reduce strain on technologists so they can tackle the issue of app sprawl head-on. While these are all valid and necessary approaches, observability is a necessary component for understanding the vast amounts of complex data within AI-infused applications, and it must be the centerpiece of an app- and data-centric strategy to truly manage app sprawl.

Cracking the Code for AI App Sprawl Challenges

In a year of elevated global IT spend, ensuring investments aren't wasted is a necessity for overwhelmed technology leaders, who not only must make decisions around which technologies to implement, but also make sense of application performance amid growing tides of vast and complex data.

When AI enters the mix, it's even more important to have complete visibility, which many organizations still lack. Observability tools and practices can help technologists address AI app sprawl by providing visibility into the performance, behavior, and dependencies of AI applications. Unfortunately, many teams are still attempting to work with incomplete visibility, meaning they simply don't know what they don't know.

Simply put, traditional application performance monitoring (APM) tools can provide visibility to a certain degree, but they weren't built to necessarily account for the influx of generative AI applications that modern enterprises are dealing with.

End-users for generative AI-infused applications demand continuous availability and frictionless experience. However, with a lack of real-time visibility, they will feel when an outage or delay compromises their experience, particularly as applications span numerous platforms. Not to mention, the implementation and understanding of complex generative AI models' behaviors are still something many organizations are working to figure out. These potential blind spots could create significant performance, compliance, and security issues.

Observe the Full Stack So You Can Add to It

Organizations can better understand the internal state of their AI applications by analyzing their external outputs with observability. When this is connected to business outcomes and effectively addressed, technology teams can lay the groundwork for a well-functioning application monitoring and management process. So, when generative AI is introduced to the environment, the foundation is already in place to effectively see and optimize application processes.

As generative AI applications join the enterprise equation, observability tools are a must-have for facilitating the delivery of higher-quality software at a faster pace — these are best enabled through:

Finding and fixing the "unknown unknowns": You can't fix what you can't see. Unfortunately, many monitoring practices and tools can only address flaws that are previously known. Observability uncovers conditions that would be impossible to find manually or with traditional platforms. It then monitors the correlation to different performance flaws and gives context for discovering root causes, resulting in quick and easy remediation.

Detecting and remediating issues early on: With observability, monitoring is integrated into the initial stages of software development. It's then easy to pinpoint and rectify new code issues before they affect the service level agreements (SLAs) and customer experience.

Self-healing application infrastructure and automated resolution: Observability can be coupled with automation capabilities to anticipate issues from system outputs and resolve them autonomously without requiring manual intervention.

Scaling and load balancing: We need to be able to observe and control the current load on systems but also help forecast future demands. The data can be used to optimize applications in real-time without having the end-users feel any impact.

Cost management: From optimizing workloads to computational resources, SREs can and should find ways to save on VM, GPU, cloud and inferencing costs through observability.

Observability is crucial for organizations to address AI app sprawl and overcome myriad challenges that come along with it, especially as generative AI becomes a mainstay across enterprises. By following observability best practices and deploying the right automated tools, organizations can proactively identify and resolve issues and ensure all AI applications are always available and friction-free.

Bill Lobig is VP, Product Management, IBM Automation

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As CIOs Address App Sprawl, Observability Can't Be an Afterthought

Bill Lobig
Bill Lobig is VP of Apptio / IBM IT Automation
IBM

App sprawl has been a concern for technologists for some time, but it has never presented such a challenge as now. As organizations move to implement generative AI into their applications, it's only going to become more complex. In fact, a recent Canva report found that 72% of CIOs see application sprawl as a challenge — and with 71% of CIOs expecting to adopt 30-60 new apps this year, this complexity is poised to keep growing.

Potential solutions include consolidating applications, optimizing workflows, and automating IT processes to reduce strain on technologists so they can tackle the issue of app sprawl head-on. While these are all valid and necessary approaches, observability is a necessary component for understanding the vast amounts of complex data within AI-infused applications, and it must be the centerpiece of an app- and data-centric strategy to truly manage app sprawl.

Cracking the Code for AI App Sprawl Challenges

In a year of elevated global IT spend, ensuring investments aren't wasted is a necessity for overwhelmed technology leaders, who not only must make decisions around which technologies to implement, but also make sense of application performance amid growing tides of vast and complex data.

When AI enters the mix, it's even more important to have complete visibility, which many organizations still lack. Observability tools and practices can help technologists address AI app sprawl by providing visibility into the performance, behavior, and dependencies of AI applications. Unfortunately, many teams are still attempting to work with incomplete visibility, meaning they simply don't know what they don't know.

Simply put, traditional application performance monitoring (APM) tools can provide visibility to a certain degree, but they weren't built to necessarily account for the influx of generative AI applications that modern enterprises are dealing with.

End-users for generative AI-infused applications demand continuous availability and frictionless experience. However, with a lack of real-time visibility, they will feel when an outage or delay compromises their experience, particularly as applications span numerous platforms. Not to mention, the implementation and understanding of complex generative AI models' behaviors are still something many organizations are working to figure out. These potential blind spots could create significant performance, compliance, and security issues.

Observe the Full Stack So You Can Add to It

Organizations can better understand the internal state of their AI applications by analyzing their external outputs with observability. When this is connected to business outcomes and effectively addressed, technology teams can lay the groundwork for a well-functioning application monitoring and management process. So, when generative AI is introduced to the environment, the foundation is already in place to effectively see and optimize application processes.

As generative AI applications join the enterprise equation, observability tools are a must-have for facilitating the delivery of higher-quality software at a faster pace — these are best enabled through:

Finding and fixing the "unknown unknowns": You can't fix what you can't see. Unfortunately, many monitoring practices and tools can only address flaws that are previously known. Observability uncovers conditions that would be impossible to find manually or with traditional platforms. It then monitors the correlation to different performance flaws and gives context for discovering root causes, resulting in quick and easy remediation.

Detecting and remediating issues early on: With observability, monitoring is integrated into the initial stages of software development. It's then easy to pinpoint and rectify new code issues before they affect the service level agreements (SLAs) and customer experience.

Self-healing application infrastructure and automated resolution: Observability can be coupled with automation capabilities to anticipate issues from system outputs and resolve them autonomously without requiring manual intervention.

Scaling and load balancing: We need to be able to observe and control the current load on systems but also help forecast future demands. The data can be used to optimize applications in real-time without having the end-users feel any impact.

Cost management: From optimizing workloads to computational resources, SREs can and should find ways to save on VM, GPU, cloud and inferencing costs through observability.

Observability is crucial for organizations to address AI app sprawl and overcome myriad challenges that come along with it, especially as generative AI becomes a mainstay across enterprises. By following observability best practices and deploying the right automated tools, organizations can proactively identify and resolve issues and ensure all AI applications are always available and friction-free.

Bill Lobig is VP, Product Management, IBM Automation

Hot Topics

The Latest

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 ...

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 ...

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 ...

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

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

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 ...

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 ...

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) ...

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 ...

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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 ...

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 ...