2024 Application Performance Management Predictions - Part 2: Observability
December 05, 2023
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Industry experts offer thoughtful, insightful, and often controversial predictions on how APM, AIOps, Observability, OpenTelemetry and related technologies will evolve and impact business in 2024. Part 2 covers more on Observability.

Start with: 2024 Application Performance Management Predictions - Part 1


Modern observability platforms generate large volumes of data, making it impossible for humans to process the data in real-time for business insights. With AI, these platforms will analyze and correlate not only performance metrics but also pinpoint the impact of business — including financial implications. In 2024, these platforms will embrace and innovate with new technologies like GenAI for real-time analytics, becoming the fulcrum for digital experience management.
Srinivasa Raghavan
Director of Product Management at Site24x7 and ManageEngine

Generative AI will continue to transform how operations teams interact with data, moving from data chasing to an interactive experience that intuitively provides the data needed for decision-making. Generative AI capabilities will complement IT teams and enable employees at all levels to identify and troubleshoot problems faster, reducing the need to escalate issues to level 3 or higher support engineers so that they can focus on the most pressing issues, delivering feature velocity to further customer experience. However, generative AI in observability cannot work without context. Observability tools need to go beyond traditional monitoring to show site reliability engineers, DevOps, and IT teams what is going on and why problems occur, within increasingly complex and dynamic infrastructure and application architectures. That added layer of insight will need to feed into and augment public LLMs with private data and runbooks so that the AI can more accurately identify and resolve problems.
Gagan Singh
VP, Product Marketing, Elastic


With the rise of generative AI influencing observability, we can expect to see fewer barriers to adoption, as new technologies and innovations help make observability accessible to all engineers and product stakeholders. This accessibility will help wear down some of these barriers, as a result of natural language queries, auto-configuration, and even some instrumentation assistance from generative AI.
Camden Swita
Senior Product Manager, New Relic


Traditional application monitoring solutions will be phased out, with organizations opting for a modern APM strategy that incorporates AI, machine learning and observability. Technologists are overwhelmed with soaring levels of data, higher end-user demands and more complex IT infrastructures; and according to a recent Cisco AppDynamics study, this is making manual monitoring impossible for most (78%). As a result, they are on the hunt for more proactive, automated solutions that can improve detection and offer better end-to-end visibility.
Gregg Ostrowski
CTO Advisor, Cisco AppDynamics


AI can aid observability, but it won't fundamentally change it. Vendors are adding large language models (LLMs) to their product to help teams troubleshoot issues more effectively. While AI has its use cases in observability — such as to detect abnormal behavior or shorten the learning curve of a query language — it's a bad fit for more fundamental use cases, like troubleshooting. That's for two main reasons. First, there are many errors in observability that you'll never see again, making it impossible to meaningfully train an LLM. Second, there are many small contents within large bits of observability data that can influence the LLM and deliver a result you weren't anticipating.
Ozan Unlu
CEO, Edge Delta

AI won't replace manual troubleshooting — what's more likely is it will augment the human who still needs to be in the loop. AI/ML will be faster at sifting through all the available data and recommending where to focus, and in what order, to get to problem identification.
Patrick Lin
SVP, GM, Observability, Splunk


Historically, machine learning (ML) had a somewhat modest impact on how the average engineer used logs, in part because every organization has its own unique way of creating and structuring logs and it was somewhat difficult to build powerful, generally-applicable features. This made it quite tricky to employ ML techniques that would offer organizations any sort of meaningful pattern analysis or help them update the pattern of the anomaly detection models. However, this is changing with the renewed energy and investment around AI and ML, which will lead observability vendors to offer better log analysis, features, and more to help with notoriously difficult problems.
Camden Swita
Senior Product Manager, New Relic


Observability data will become a key enabler for GenAI in 2024. For an AI model to be successful, it must be built on high-quality and complete telemetry data. Observability solutions are well-positioned to be primary data sources for GenAI solutions. GenAI paired with telemetry data will greatly improve how we understand major incidents by summarizing important events as well as enable more efficient, high-quality monitoring.
Ben Sigelman
VP and GM of Cloud Observability, ServiceNow

As AI adoption surges, observability will emerge as essential to oversee the increase in data and complexity. Generative AI and code copilots will make us superhuman, right? Rising productivity is going to cause an explosion in the number, scale and complexity of things that we build in the coming years. In the next 5 years, you will have to observe many more things, like environments, applications, microservices, code pushes and clusters. Thanks to AI, observability solutions will have to deal with far more variety and volume of data — open standards will become much more important.
Arijit Mukherji
Distinguished Architect, Splunk


The visibility imperative will drive up the adoption of observability. Understanding the potential impact of resources, applications, or usage is vital to improve business performance. This will enable IT leadership to create more resilient organizations that can better weather the constant uncertainty.
Rupert Colbourne
CTO, Orbus Software

Observability will continue to rise in significance for enterprises, especially as emerging technologies further unlock of its potential. With AI becoming more prevalent, observability will be a crucial component for successful business operations.
Renuka Nadkarni
Chief Product Officer, Aryaka


Observability, characterized as a fusion of log analytics, monitoring, and APM, will continue to grow into a consolidated and expansive market. This is a pattern that echoes many enterprise software segments from enterprise applications to collaboration tools and it is likely to reshape the segment as a whole. We'll continue to see legacy companies, burdened by outdated architectures, struggle to meet the demands of modern distributed applications. As a result, M&A of newer disruptive startups will increase but also, PE firms or strategic partners will step in to end dreams of innovation and growth and instead focus on maximizing cash flow from trapped customers. Funding of observability startups in 2024 will reach record levels with newer players building on modern architectures to handle the cost associated with massive growth in data volumes and C-Level pressure to drive down incident resolution times and improve customer experience.
Jeremy Burton
CEO, Observe


In 2024, organizations will shift from traditional, one-size-fits-all infrastructure-centric observability platforms to specialized developer-focused tools. This change, fueled by the growing recognition of prioritizing developer needs, is crucial for delivering exceptional User Experience (UX) and Customer Experience (CX). With an increased focus on DevOps, developers will actively participate in day-to-day monitoring, collaborating closely with operations teams. Leading application development organizations aim to gain a competitive edge by ensuring error-free, highly performant applications, delighting end users. The adoption of a new class of Developer Observability tools, tailor-made for developers, will provide teams with the visibility needed for high-quality releases, fostering confidence in continuous deployment.
Anthony Bryce
VP of Product Management, SmartBear

For enterprise-level organizations that have already migrated to a microservices architecture, complex DevOps observability tools will become increasingly important in the management and understanding of these systems.
Sarah Morgan
Head of Product, Scout APM


Companies will continue to integrate Application Performance Management (APM) solutions to achieve full-stack observability, enabling a panoramic view of their systems' health and performance. APM will provide more granularity in analytics such that technical teams will be able to not only detect but predict system anomalies. This will enable preemptive measures that drastically reduce downtime and empower developers to continuously deliver better quality releases.
Guillaume Moigneu
VP Product, Growth and Monetization, Platform.sh

Full-stack observability will be even more essential to modern organizations' business strategies. In fact, in a recent study, Cisco AppDynamics found that 88% of technologists believe observability with business context will enable them to be more strategic and spend more time on innovation. As migration to the cloud continues, observability will grow in popularity to provide technologists with more robust visibility into their now hybrid or fragmented IT estates, helping them avoid potential gaps of "invisible downtime." Full-stack observability also allows for performance data to be connected with business insights, helping organizations make better, more informed decisions tied to business outcomes. The end users' digital experience is more important than ever for customer satisfaction and retention, and full-stack observability will ensure enterprises can deliver on that promised experience.
Joe Byrne
CTO Advisor, Cisco AppDynamics


As organizations move their operations to multi and hybrid cloud environments, enterprise IT leaders are under newfound pressure to deliver customer experience excellence, prove ROI, and manage secure, reliable environments. Successful enterprise IT leaders quickly understood the need to build and invest in observability for their cloud environments to manage the complexity and visibility of cloud-native technologies with distributed architectures.
Gagan Singh
VP, Product Marketing, Elastic

With the rising adoption of AI and machine learning technologies, cloud will continue to play a significant role in business strategy due to its scalability and flexibility. As organizations explore hybrid or multi-cloud infrastructures, a more holistic monitoring approach will be critical to avoiding siloed operations and maintaining the high-performance of applications, whether they are hosted on-premises or across public clouds. It is expected that in the next five years, most new digital transformation programs will be built with cloud, and it will be crucial for organizations to move to an observability solution in order to manage those hybrid environments.
Joe Byrne
CTO Advisor, Cisco AppDynamics

The acceleration of digital transformation has also brought on complexities in data management, intelligence and observability. In 2024, organizations will need to prioritize investment in tools that address these complexities. In a recent study, 96% of technologists reported that for their organizations to deliver accelerated and sustainable innovation, observability across multi-cloud and hybrid environments is one of the top requirements. Organizations cannot rely on traditional monitoring systems anymore, and instead, need to implement modern solutions that provide real-time visibility across the entire stack.
Gregg Ostrowski
CTO Advisor, Cisco AppDynamics


Complexity will continue to drive up MTTR, so look for observability solutions that use AI to troubleshoot faster. In our annual DevOps Pulse survey, we have witnessed the gradual increase of the Mean Time To Recovery (MTTR) for production issues year over year. In the 2021 survey, 47% of respondents stated that it took multiple hours on average to resolve production issues. This rose to 64% of respondents in 2022, and a stunning 73% in 2023. A major impetus behind this growing challenge is expanding system complexity, especially due to the adoption of Kubernetes and cloud-native technologies and practices. Technologies such as Kubernetes generate abundant and complex data, making it difficult to monitor and troubleshoot. In fact, these technologies were cited by 46% of this year's survey respondents as the most difficult obstacle for organizations to gain full observability of their environment.
Asaf Yigal
Co-Founder and CTO, Logz.io

Go to: 2024 Application Performance Management Predictions - Part 3, covering even more on observability.

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