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The Importance of CX Observability for DevOps Teams: A $1 Billion Missed Opportunity

James Isaacs
Cyara

A company will thrive or go out of business based on its reputation. A brand's reputation is dictated by several different factors, but customer experience (CX) remains the most critical when it comes to customer loyalty, trust and ultimately, sales. Studies have shown that 96% of consumers say CX is a key factor in their choice of loyalty to a brand and 85% of buyers are willing to pay more for great CX.

Yet, every day, companies are missing CX "red flags" because they don't have the tools to observe CX processes or metrics. Even basic errors or defects in automated customer interactions are left undetected for days, weeks or months, leading to widespread customer dissatisfaction. In fact, poor CX and digital technology investments are costing enterprises billions of dollars in lost potential revenue.

You can't fix what you can't see. Without observability into critical data about CX quality, there will always be bugs and errors that live out in the CX system for far too long. These technical issues tamper with the reliability of the CX software, which is so integral to the customer journey now that most businesses are operating on a digital-first approach. Reliability has never been more important for enterprises as they navigate digital transformation, especially cloud migration and automation.

Cultivating Observability in CX Software

Even in today's digital economy, many companies are still missing the mark when it comes to creating a memorable and positive CX through digital and voice channels. Most of the gap can be solved, however, if businesses invest in increasing their observability of CX processes and insightful metrics such as call center data, churn rate and net promoter scores (NPS).

Investing in greater observability allows enterprises to better collect and analyze data on every component of a system, application and infrastructure — from performance to security to accessibility. This information allows DevOps teams to glean insights into the reliability of actions performed within a unique business environment, such as an interactive voice response (IVR) system or a contact center. While an observability approach has been quickly adopted by software development and engineering teams, the practice of observability is still lagging when it comes to CX-related systems and solutions.

Gaining Real-time CX Insights

The fact of the matter is that for even the best DevOps teams, flaws creep into production. While DevOps teams do their best to foresee and predict potential gaps, they need a little help. Automated testing early in the development cycle, and throughout the entire process, is a proven way to identify potential flaws and mitigate errors before it becomes a costly threat to customers' experience. This requires automation because the faster a data set can be analyzed, the more value it will deliver.

Automated CX testing solutions work by generating synthetic interactions — either a single or thousands — to test the customer journey from the outside-in, from the network through IVRs to digital apps and routing systems, all the way to agents at their desktops — engaging in systems just as a customer would. Testing elements such as connectivity, responsiveness, quality and functionality can help DevOps teams ensure quality is achieved throughout the entire CX development life cycle. You can then dictate what data should be collected, who will own and examine the data and why it matters in the business' unique context to ensure the information collected within the customer journey is useful and impactful for the organization.

Be a Champion for CX Success

While the virtues of improving CX through automated testing are well known, it is worth mentioning the importance of executives championing CX observability and making the investment to prioritize it. Considering the potential revenue lost annually due to poor CX, it is important for executives to champion CX improvement efforts. For CX digital transformation initiatives to succeed, they must be led by an executive leader who can assert the need to drive cultural change and strategic investments. Appointing an executive to this role will ensure that CX initiatives are not siloed from DevOps teams, but also from the rest of the enterprise, including sales, marketing, operations and more. The organization's digital transformation champion must have broad authority that covers budget, people and processes in order to be effective in their role.

Online and in-person CX will always play a key role in brand loyalty. In an increasingly digital age, the reliability of our brand's digital frameworks will play an undeniable role in achieving brand loyalty and reputation through positive customer experiences. With greater observability and automated testing capabilities, enterprises can be empowered to innovate faster and deliver higher-quality solutions that will improve customer interactions and solidify brand loyalty for years to come.

James Isaacs is President of Cyara

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The Importance of CX Observability for DevOps Teams: A $1 Billion Missed Opportunity

James Isaacs
Cyara

A company will thrive or go out of business based on its reputation. A brand's reputation is dictated by several different factors, but customer experience (CX) remains the most critical when it comes to customer loyalty, trust and ultimately, sales. Studies have shown that 96% of consumers say CX is a key factor in their choice of loyalty to a brand and 85% of buyers are willing to pay more for great CX.

Yet, every day, companies are missing CX "red flags" because they don't have the tools to observe CX processes or metrics. Even basic errors or defects in automated customer interactions are left undetected for days, weeks or months, leading to widespread customer dissatisfaction. In fact, poor CX and digital technology investments are costing enterprises billions of dollars in lost potential revenue.

You can't fix what you can't see. Without observability into critical data about CX quality, there will always be bugs and errors that live out in the CX system for far too long. These technical issues tamper with the reliability of the CX software, which is so integral to the customer journey now that most businesses are operating on a digital-first approach. Reliability has never been more important for enterprises as they navigate digital transformation, especially cloud migration and automation.

Cultivating Observability in CX Software

Even in today's digital economy, many companies are still missing the mark when it comes to creating a memorable and positive CX through digital and voice channels. Most of the gap can be solved, however, if businesses invest in increasing their observability of CX processes and insightful metrics such as call center data, churn rate and net promoter scores (NPS).

Investing in greater observability allows enterprises to better collect and analyze data on every component of a system, application and infrastructure — from performance to security to accessibility. This information allows DevOps teams to glean insights into the reliability of actions performed within a unique business environment, such as an interactive voice response (IVR) system or a contact center. While an observability approach has been quickly adopted by software development and engineering teams, the practice of observability is still lagging when it comes to CX-related systems and solutions.

Gaining Real-time CX Insights

The fact of the matter is that for even the best DevOps teams, flaws creep into production. While DevOps teams do their best to foresee and predict potential gaps, they need a little help. Automated testing early in the development cycle, and throughout the entire process, is a proven way to identify potential flaws and mitigate errors before it becomes a costly threat to customers' experience. This requires automation because the faster a data set can be analyzed, the more value it will deliver.

Automated CX testing solutions work by generating synthetic interactions — either a single or thousands — to test the customer journey from the outside-in, from the network through IVRs to digital apps and routing systems, all the way to agents at their desktops — engaging in systems just as a customer would. Testing elements such as connectivity, responsiveness, quality and functionality can help DevOps teams ensure quality is achieved throughout the entire CX development life cycle. You can then dictate what data should be collected, who will own and examine the data and why it matters in the business' unique context to ensure the information collected within the customer journey is useful and impactful for the organization.

Be a Champion for CX Success

While the virtues of improving CX through automated testing are well known, it is worth mentioning the importance of executives championing CX observability and making the investment to prioritize it. Considering the potential revenue lost annually due to poor CX, it is important for executives to champion CX improvement efforts. For CX digital transformation initiatives to succeed, they must be led by an executive leader who can assert the need to drive cultural change and strategic investments. Appointing an executive to this role will ensure that CX initiatives are not siloed from DevOps teams, but also from the rest of the enterprise, including sales, marketing, operations and more. The organization's digital transformation champion must have broad authority that covers budget, people and processes in order to be effective in their role.

Online and in-person CX will always play a key role in brand loyalty. In an increasingly digital age, the reliability of our brand's digital frameworks will play an undeniable role in achieving brand loyalty and reputation through positive customer experiences. With greater observability and automated testing capabilities, enterprises can be empowered to innovate faster and deliver higher-quality solutions that will improve customer interactions and solidify brand loyalty for years to come.

James Isaacs is President of Cyara

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Most organizations approach OpenTelemetry as a collection of individual tools they need to assemble from scratch. This view misses the bigger picture. OpenTelemetry is a complete telemetry framework with composable components that address specific problems at different stages of organizational maturity. You start with what you need today and adopt additional pieces as your observability practices evolve ...

One of the earliest lessons I learned from architecting throughput-heavy services is that simplicity wins repeatedly: fewer moving parts, loosely coupled execution (fewer synchronous calls), and precise timing metering. You want data and decisions to travel the shortest possible path. The goal is to build a system where every strategy and each line of code (contention is the key metric) complements the decision trees ...

As discussions around AI "autonomous coworkers" accelerate, many industry projections assume that agents will soon operate alongside human staff in making decisions, taking actions, and managing tasks with minimal oversight. But a growing number of critics (including some of the developers building these systems) argue that the industry still has a long way to go to be able to treat AI agents like fully trusted teammates ...

Enterprise AI has entered a transformational phase where, according to Digitate's recently released survey, Agentic AI and the Future of Enterprise IT, companies are moving beyond traditional automation toward Agentic AI systems designed to reason, adapt, and collaborate alongside human teams ...

The numbers back this urgency up. A recent Zapier survey shows that 92% of enterprises now treat AI as a top priority. Leaders want it, and teams are clamoring for it. But if you look closer at the operations of these companies, you see a different picture. The rollout is slow. The results are often delayed. There's a disconnect between what leaders want and what their technical infrastructure can handle ...

Kyndryl's 2025 Readiness Report revealed that 61% of global business and technology leaders report increasing pressure from boards and regulators to prove AI's ROI. As the technology evolves and expectations continue to rise, leaders are compelled to generate and prove impact before scaling further. This will lead to a decisive turning point in 2026 ...

Cloudflare's disruption illustrates how quickly a single provider's issue cascades into widespread exposure. Many organizations don't fully realize how tightly their systems are coupled to thirdparty services, or how quickly availability and security concerns align when those services falter ... You can't avoid these dependencies, but you can understand them ...

If you work with AI, you know this story. A model performs during testing, looks great in early reviews, works perfectly in production and then slowly loses relevance after operating for a while. Everything on the surface looks perfect — pipelines are running, predictions or recommendations are error-free, data quality checks show green; yet outcomes don't meet the ground reality. This pattern often repeats across enterprise AI programs. Take for example, a mid-sized retail banking and wealth-management firm with heavy investments in AI-powered risk analytics, fraud detection and personalized credit-decisioning systems. The model worked well for a while, but transactions increased, so did false positives by 18% ...

Basic uptime is no longer the gold standard. By 2026, network monitoring must do more than report status, it must explain performance in a hybrid-first world. Networks are no longer just static support systems; they are agile, distributed architectures that sit at the very heart of the customer experience and the business outcomes ... The following five trends represent the new standard for network health, providing a blueprint for teams to move from reactive troubleshooting to a proactive, integrated future ...

APMdigest's Predictions Series concludes with 2026 AI Predictions — industry experts offer predictions on how AI and related technologies will evolve and impact business in 2026. Part 5, the final installment, covers AI's impacts on IT teams ...