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How Quality Engineering Helps Build Flawless Customer Experiences

Akshaya Choudhary

Customer experience has become the cornerstone for success with enterprises splitting their hair on how to achieve the same without robbing a bank. It can make or mar the prospects of any business venture and should be the focus of any organization. Let us understand its importance with an example:

A big brand decided to move online and offer its products and services in the eCommerce model. The launch of the portal was done with much fanfare and excitement with expectations about customers making the most of the eCommerce portal. However, when it came to the brass-tacks, the venture turned out to be a fiasco with customers shunning the portal. Why, you may wonder? Well, the pages loaded slowly, the payment gateway did not function as it ought to be, and the images were not attractive and visible enough. Result? Instead of improving sales and enhancing brand equity, the reputation of the brand took a nosedive.


When customers interact with a digital product or service, they expect it to work without any hiccup. And should they find one — such as a click, touch, or swipe failing to offer the desired result — they may disown the product or service altogether and settle for the competitor.

Hence, people entrusted with delivering a superior customer experience (developers, QA specialists, and customer service personnel) must make the processes seamless. This should be irrespective of the digital touchpoints used by the customers — smartphones, desktops, tablets, laptops, notebooks, or smartwatches, among others. However, ensuring the same requires thinking differently by using quality engineering. It is an Agile and DevOps based approach wherein the QA process is automated to deliver outcomes like continuous integration, testing, and delivery. In short, digital quality engineering can lead to enhanced CX (customer experience).

As new technologies are incorporated to develop attractive, fast, feature-rich, secure, responsive, and turnkey software solutions, the expectations of customers continue to rise. They want their software applications to be high performing irrespective of the digital devices, browsers, operating systems, or networks. And with the preponderance of so many avenues and systems, the complexity of software systems (and their failure) is bound to increase. To address such challenges, enterprises need to look beyond traditional software quality assurance services and embrace quality engineering.

To deliver enhanced CX, enterprises should be able to meet three requirements:

■ Automate most processes of the engineering value chain.

■ The quality engineering approach to be focused around delivering a great customer experience.

■ Use effective automation tools to build an enabling test environment to further the cause of CX

How Software Quality Engineering Can Enhance the CX

With an increase in the complexities of software applications, especially the ERP ones, software quality engineering can help build a tool-agnostic platform to facilitate software releases. By taking the Agile and DevOps approaches, QE services can help enterprises to design, build, and test software applications, quickly and consistently. In fact, the AI-enabled tools can detect glitches that are preventing the delivery of great CX, quickly. AI can leverage tools such as chatbots and social networking accounts to analyze and verify if the applications are functioning as expected. To execute and verify customer experience testing, a holistic digital quality engineering process should address a range of issues. These include automating the value chain by incorporating Agile and DevOps methodologies. To drive an effective CX, QE services should consider the following types of testing:

Compatibility: It validates the seamless run of any software application across digital mediums comprising laptops, smartphones, tablets, notebooks, and desktops. For example, a fixed deposit policy can be initiated on a smartphone and completed on a notebook without any hiccups.

Usability: In this type of testing, aspects like error rates, task times, and human-computer interactions are looked into. For example, is the customer getting the information he or she asked for or the CTAs on the webpages are easy to follow? Also, there should be a visual consistency across pages in situations such as the Black Friday sale. Here, the navigation for an eCommerce portal should be fast and seamless across web pages to achieve great CX.

Accessibility: According to the World Wide Web Consortium’s accessibility guidelines, there should be inclusiveness in offering web content to the users. For example, a mobile app should have features like alternative text (describing objects on the screen), visible focus indicators (enabling a customer to use a keyboard rather than a mouse), and good contrast ratios.

Performance: In this quality engineering approach, tests are conducted to verify whether the features and functionalities of a software application perform to their optimum when subjected to a certain traffic load. For example, an eCommerce portal needs the fastest load and response times from its digital assets during peak demand. In the absence of performance testing, the load threshold will remain unknown leading to system crashes.

Security: Security has emerged as the biggest challenge confronting the digital ecosystem. And unless system vulnerabilities and glitches are dealt with during the development and testing process, the consequences can be disastrous — for all stakeholders. For example, an eCommerce app with a payment gateway should disallow the autofill option for passwords and other sensitive fields. If such practices are incorporated in the value chain, the risk of security breaches and any non-compliance with regulatory protocols can be eliminated.

Conclusion

By implementing enterprise quality engineering, organizations can detect and fix flaws or glitches in the software application before it is delivered to the end customers. It helps to design an application with minimal scope for glitches thus saving time, effort, and cost in rework. In the competitive business landscape of today, software quality engineering can help enterprises to rationalize the cost of operations, eliminate errors, and deliver superior customer experiences, quickly and consistently.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

How Quality Engineering Helps Build Flawless Customer Experiences

Akshaya Choudhary

Customer experience has become the cornerstone for success with enterprises splitting their hair on how to achieve the same without robbing a bank. It can make or mar the prospects of any business venture and should be the focus of any organization. Let us understand its importance with an example:

A big brand decided to move online and offer its products and services in the eCommerce model. The launch of the portal was done with much fanfare and excitement with expectations about customers making the most of the eCommerce portal. However, when it came to the brass-tacks, the venture turned out to be a fiasco with customers shunning the portal. Why, you may wonder? Well, the pages loaded slowly, the payment gateway did not function as it ought to be, and the images were not attractive and visible enough. Result? Instead of improving sales and enhancing brand equity, the reputation of the brand took a nosedive.


When customers interact with a digital product or service, they expect it to work without any hiccup. And should they find one — such as a click, touch, or swipe failing to offer the desired result — they may disown the product or service altogether and settle for the competitor.

Hence, people entrusted with delivering a superior customer experience (developers, QA specialists, and customer service personnel) must make the processes seamless. This should be irrespective of the digital touchpoints used by the customers — smartphones, desktops, tablets, laptops, notebooks, or smartwatches, among others. However, ensuring the same requires thinking differently by using quality engineering. It is an Agile and DevOps based approach wherein the QA process is automated to deliver outcomes like continuous integration, testing, and delivery. In short, digital quality engineering can lead to enhanced CX (customer experience).

As new technologies are incorporated to develop attractive, fast, feature-rich, secure, responsive, and turnkey software solutions, the expectations of customers continue to rise. They want their software applications to be high performing irrespective of the digital devices, browsers, operating systems, or networks. And with the preponderance of so many avenues and systems, the complexity of software systems (and their failure) is bound to increase. To address such challenges, enterprises need to look beyond traditional software quality assurance services and embrace quality engineering.

To deliver enhanced CX, enterprises should be able to meet three requirements:

■ Automate most processes of the engineering value chain.

■ The quality engineering approach to be focused around delivering a great customer experience.

■ Use effective automation tools to build an enabling test environment to further the cause of CX

How Software Quality Engineering Can Enhance the CX

With an increase in the complexities of software applications, especially the ERP ones, software quality engineering can help build a tool-agnostic platform to facilitate software releases. By taking the Agile and DevOps approaches, QE services can help enterprises to design, build, and test software applications, quickly and consistently. In fact, the AI-enabled tools can detect glitches that are preventing the delivery of great CX, quickly. AI can leverage tools such as chatbots and social networking accounts to analyze and verify if the applications are functioning as expected. To execute and verify customer experience testing, a holistic digital quality engineering process should address a range of issues. These include automating the value chain by incorporating Agile and DevOps methodologies. To drive an effective CX, QE services should consider the following types of testing:

Compatibility: It validates the seamless run of any software application across digital mediums comprising laptops, smartphones, tablets, notebooks, and desktops. For example, a fixed deposit policy can be initiated on a smartphone and completed on a notebook without any hiccups.

Usability: In this type of testing, aspects like error rates, task times, and human-computer interactions are looked into. For example, is the customer getting the information he or she asked for or the CTAs on the webpages are easy to follow? Also, there should be a visual consistency across pages in situations such as the Black Friday sale. Here, the navigation for an eCommerce portal should be fast and seamless across web pages to achieve great CX.

Accessibility: According to the World Wide Web Consortium’s accessibility guidelines, there should be inclusiveness in offering web content to the users. For example, a mobile app should have features like alternative text (describing objects on the screen), visible focus indicators (enabling a customer to use a keyboard rather than a mouse), and good contrast ratios.

Performance: In this quality engineering approach, tests are conducted to verify whether the features and functionalities of a software application perform to their optimum when subjected to a certain traffic load. For example, an eCommerce portal needs the fastest load and response times from its digital assets during peak demand. In the absence of performance testing, the load threshold will remain unknown leading to system crashes.

Security: Security has emerged as the biggest challenge confronting the digital ecosystem. And unless system vulnerabilities and glitches are dealt with during the development and testing process, the consequences can be disastrous — for all stakeholders. For example, an eCommerce app with a payment gateway should disallow the autofill option for passwords and other sensitive fields. If such practices are incorporated in the value chain, the risk of security breaches and any non-compliance with regulatory protocols can be eliminated.

Conclusion

By implementing enterprise quality engineering, organizations can detect and fix flaws or glitches in the software application before it is delivered to the end customers. It helps to design an application with minimal scope for glitches thus saving time, effort, and cost in rework. In the competitive business landscape of today, software quality engineering can help enterprises to rationalize the cost of operations, eliminate errors, and deliver superior customer experiences, quickly and consistently.

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...