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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

AI is the catalyst for significant investment in data teams as enterprises require higher-quality data to power their AI applications, according to the State of Analytics Engineering Report from dbt Labs ...

Misaligned architecture can lead to business consequences, with 93% of respondents reporting negative outcomes such as service disruptions, high operational costs and security challenges ...

A Gartner analyst recently suggested that GenAI tools could create 25% time savings for network operational teams. Where might these time savings come from? How are GenAI tools helping NetOps teams today, and what other tasks might they take on in the future as models continue improving? In general, these savings come from automating or streamlining manual NetOps tasks ...

IT and line-of-business teams are increasingly aligned in their efforts to close the data gap and drive greater collaboration to alleviate IT bottlenecks and offload growing demands on IT teams, according to The 2025 Automation Benchmark Report: Insights from IT Leaders on Enterprise Automation & the Future of AI-Driven Businesses from Jitterbit ...

A large majority (86%) of data management and AI decision makers cite protecting data privacy as a top concern, with 76% of respondents citing ROI on data privacy and AI initiatives across their organization, according to a new Harris Poll from Collibra ...

According to Gartner, Inc. the following six trends will shape the future of cloud over the next four years, ultimately resulting in new ways of working that are digital in nature and transformative in impact ...

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality ... But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill. Welcome to the Great SaaS Hangover ...

Regardless of OpenShift being a scalable and flexible software, it can be a pain to monitor since complete visibility into the underlying operations is not guaranteed ... To effectively monitor an OpenShift environment, IT administrators should focus on these five key elements and their associated metrics ...

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...