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Want to Keep Your Employees? Prioritize Their Digital Experience

Mike Marks
Riverbed

Companies have historically relied on tools that warn IT teams when their digital systems are experiencing glitches or attacks. But in an age where consumer loyalty is fickle and hybrid workers' Digital Employee Experience (DEX) is paramount for productivity, companies cannot afford to retroactively deal with IT failures that slow down employee productivity.

What if, instead, companies considered proactive solutions to their digital experience that provided greater visibility into the potential pitfalls of their IT systems? Rather than reactively dealing with the aftermath of digital downfalls, companies can take matters into their own hands by assessing how their employees are reacting to the applications in play.

With 74% of Millennial and GenZ workers taking over the workforce, employees have an increased expectation on the efficiency of their digital work experience. The digital landscape is quickly evolving, and companies have to keep up if they want to deliver a digital experience that improves employee productivity, customer experience, and business performance. Below are three ways that real-time employee feedback allows for greater IT effectiveness, uncovers what's working well, and flags potential pitfalls.

Creating Tailored Solutions for Employee Feedback Helps Proactively Improve a Company's Digital Experience

Tailored surveys that span across devices and locations can provide companies with targeted areas of IT optimization and improvement. Companies need to also consider an employee's digital buy-in and how that affects productivity and retention. Employee buy-in is a key component for successful digital transformation within a company.

By implementing real-time feedback that pairs qualitative telemetry with contextual user data, companies can gain a more accurate assessment of how their applications are performing and where they can improve. Prioritizing real-time feedback ultimately leads to a better employee experience, customer satisfaction, and optimal business outcomes.

Integrating Real-Time Employee Feedback Tools Like XLAs Aid in Employee Digital Buy-in

Implementing experience-level agreements (XLA) that integrate both employee and customer insights can help companies assess users' levels of satisfaction and identify trends. XLAs add significant value to companies by providing insight into the employee digital experience through quantitative feedback and qualitative metrics.

The key differentiator between a traditional SLA (service level agreement) and an XLA is that XLAs not only provide transactional metrics of a department but also provide IT and LOB leaders with metrics on the level of satisfaction and preferences users have with a given application. Additionally, XLAs help business leaders analyze trends in the context of their overall processes and provide guidance on improving policies, prioritizing investments and identifying skills gaps.

Optimizing the Employee Digital Experience Will Lead to Better Business Outcomes

Companies need to first assess their business goals, particularly around customer success, and then determine how a stronger employee digital experience is helping or hurting their bottom line. Companies must consider how to practically integrate real-time employee feedback that helps them gain visibility into the employee digital experience and proactively address potential IT problems and solutions.

Proactive engagement with employees allows greater insight into digital acceptance and overall satisfaction with the IT experience. Furthermore, knowing how the day-to-day operations of the digital experience are affecting employees allows companies to know where to improve their IT systems and applications. What companies must understand is that placing a high value on digital visibility and employee success is critical to retaining top talent.

The Way Forward

As companies seek to implement actionable steps toward real-time feedback, they will begin to improve employee satisfaction and productivity that leads to optimal business success. Additionally, providing tailored solutions for employee feedback serves to create a more positive and streamlined experience for their employees. When employees are happy, companies realize increased customer success, and this combination is key to yielding positive business outcomes for companies both now and in the future.

Mike Marks is VP of Product Marketing at Riverbed

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

Want to Keep Your Employees? Prioritize Their Digital Experience

Mike Marks
Riverbed

Companies have historically relied on tools that warn IT teams when their digital systems are experiencing glitches or attacks. But in an age where consumer loyalty is fickle and hybrid workers' Digital Employee Experience (DEX) is paramount for productivity, companies cannot afford to retroactively deal with IT failures that slow down employee productivity.

What if, instead, companies considered proactive solutions to their digital experience that provided greater visibility into the potential pitfalls of their IT systems? Rather than reactively dealing with the aftermath of digital downfalls, companies can take matters into their own hands by assessing how their employees are reacting to the applications in play.

With 74% of Millennial and GenZ workers taking over the workforce, employees have an increased expectation on the efficiency of their digital work experience. The digital landscape is quickly evolving, and companies have to keep up if they want to deliver a digital experience that improves employee productivity, customer experience, and business performance. Below are three ways that real-time employee feedback allows for greater IT effectiveness, uncovers what's working well, and flags potential pitfalls.

Creating Tailored Solutions for Employee Feedback Helps Proactively Improve a Company's Digital Experience

Tailored surveys that span across devices and locations can provide companies with targeted areas of IT optimization and improvement. Companies need to also consider an employee's digital buy-in and how that affects productivity and retention. Employee buy-in is a key component for successful digital transformation within a company.

By implementing real-time feedback that pairs qualitative telemetry with contextual user data, companies can gain a more accurate assessment of how their applications are performing and where they can improve. Prioritizing real-time feedback ultimately leads to a better employee experience, customer satisfaction, and optimal business outcomes.

Integrating Real-Time Employee Feedback Tools Like XLAs Aid in Employee Digital Buy-in

Implementing experience-level agreements (XLA) that integrate both employee and customer insights can help companies assess users' levels of satisfaction and identify trends. XLAs add significant value to companies by providing insight into the employee digital experience through quantitative feedback and qualitative metrics.

The key differentiator between a traditional SLA (service level agreement) and an XLA is that XLAs not only provide transactional metrics of a department but also provide IT and LOB leaders with metrics on the level of satisfaction and preferences users have with a given application. Additionally, XLAs help business leaders analyze trends in the context of their overall processes and provide guidance on improving policies, prioritizing investments and identifying skills gaps.

Optimizing the Employee Digital Experience Will Lead to Better Business Outcomes

Companies need to first assess their business goals, particularly around customer success, and then determine how a stronger employee digital experience is helping or hurting their bottom line. Companies must consider how to practically integrate real-time employee feedback that helps them gain visibility into the employee digital experience and proactively address potential IT problems and solutions.

Proactive engagement with employees allows greater insight into digital acceptance and overall satisfaction with the IT experience. Furthermore, knowing how the day-to-day operations of the digital experience are affecting employees allows companies to know where to improve their IT systems and applications. What companies must understand is that placing a high value on digital visibility and employee success is critical to retaining top talent.

The Way Forward

As companies seek to implement actionable steps toward real-time feedback, they will begin to improve employee satisfaction and productivity that leads to optimal business success. Additionally, providing tailored solutions for employee feedback serves to create a more positive and streamlined experience for their employees. When employees are happy, companies realize increased customer success, and this combination is key to yielding positive business outcomes for companies both now and in the future.

Mike Marks is VP of Product Marketing at Riverbed

The Latest

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...