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Empowering Human Ingenuity in APM with Collaborative-Driven Automation

There are many challenges facing application teams today, as they are tasked with trying to reduce administrative, support, and help desk costs through active application management; improve end-user quality of service with efficient application and upgrade delivery; and lower operational costs through automatic application self-healing.

Some companies have turned to automation to lower costs and increase efficiency, but the increasing number of distributed, virtual and cloud-based applications pose a unique challenge for Application Performance Management (APM) as processes quickly become outdated and insufficient. And to make matters worse, the complexity of application delivery environments is outstripping the ability of APM products to monitor and manage performance.

Recent headlines, such as “Person Drives 100 Miles in Wrong Direction, Following GPS,” have shown us that automating complex processes without any human touch has a high propensity to go awry. Relying 100 percent on automation without any human intervention can leave processes stale and keep businesses stuck in a holding pattern, waiting for the next major process update that could take months to years to complete.

That's why innovative companies are leveraging next-generation technologies that integrate social and collaborative capabilities at the platform layer of automation tools to create a human-centric approach to complex process automation.

More traditional APM automation tools enable users to leverage reporting and analytics to detect issues and then use static run books to remediate those issues. But rather than getting a real-time glimpse into service issues, these static procedures are only providing a snapshot in time. What if the users had access to more than analytics and static run books? What if the users were empowered with the knowledge of an organization’s subject matter experts in real-time?

Traditional runbooks typically contain static decision trees that capture a process at one given point in time. Collaborative-driven automation tools feature dynamic decision trees, which allow users to drill down to resolutions faster within the knowledge management database, based on a series of intuitive questions assessing the symptom or the reported application issue.

The effectiveness of these decision trees is enhanced when the organization's most skilled experts are updating or adding to resolutions in real time to address newly emerging and/or more prominent topics. The result is a method of dynamic knowledge capture that keeps the bank of procedures current, so that users are able to rely upon information that reflects the resolutions that work best at any given point in time.  

With this immediate access to real-time updated knowledge, innovative companies are empowering human ingenuity in their organizations and achieving the below results with the latest APM automation tools:

- End-to-End Process Automation with unified orchestration and collaboration, combining multiple automation solutions into one process with integrated workflow capabilities and end-to-end reporting across multiple and parallel workflows. 

- First level staff are enabled to perform automated diagnostics and remediation in response to both inbound tickets and analytic trends and notifications picked up by performance reporting tools.

- Associate skillsets are being normalized with automations that don’t require advanced or specialized skills to create. Relevant knowledge documents are “pushed out” based on incident/issue type, and decision tree technology guides IT/First-Level technicians to relevant information and automations based on the symptoms presented.

- Improved application availability for end-users is created by reducing downtime cycles from hours to minutes and reducing the number of emergency bridge calls required to resolve issues.

- Compliance and auditing (CoBIT/SOX) are improved with analytics for audit trails and SLA compliance.

- Reduced average Mean Time to Resolution (MTTR) can be seen through enabled engineers.

- Application teams can run tests outside of their application and assign fault to those groups without a bridge call.  

- Problem solving steps are automatically executed in parallel instead of serial manual execution. Issues are no longer fixed by the engineer logging into tool #one, executing a series of commands, interpreting results, then logging into tool #two, executing commands, interpret results, etc. Instead, the engineer runs a series of commands simultaneously at the push of a button, and gets back results in a simple to understand format.

Application Performance Management entails complex processes that can and should be automated. But rather than eliminate human touch, automation tools should empower associates to execute the best possible automations with the collective, real-time knowledge of the organization.

When organizations implement automation technologies that leave human collaboration out of the process, it isn’t difficult for the “less-than-best” process to be followed, requiring multiple teams to be thrown into fire-fighting mode. Improved collaboration on processes allows more time to be spent on strategic initiatives and proactive management of applications. Not only will the entire organization benefit, so will the customers. And making and keeping customers happy should be the top goal for every organization.

ABOUT Payal Kindiger

As Executive Vice President of Marketing and Managed Services for gen-E, Payal Kindiger leads the company’s branding and marketing efforts, inside sales operations, organizational strategy, customer care, and managed services offerings. Prior to joining gen-E in 2003, she was a member of the management team at Deloitte and Touche. She has worked with several Fortune 500 companies and has managed client-service projects in IT business process re-engineering and organizational development across a number of industries. 

Related Links:

www.gen-e.com

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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 ...

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Empowering Human Ingenuity in APM with Collaborative-Driven Automation

There are many challenges facing application teams today, as they are tasked with trying to reduce administrative, support, and help desk costs through active application management; improve end-user quality of service with efficient application and upgrade delivery; and lower operational costs through automatic application self-healing.

Some companies have turned to automation to lower costs and increase efficiency, but the increasing number of distributed, virtual and cloud-based applications pose a unique challenge for Application Performance Management (APM) as processes quickly become outdated and insufficient. And to make matters worse, the complexity of application delivery environments is outstripping the ability of APM products to monitor and manage performance.

Recent headlines, such as “Person Drives 100 Miles in Wrong Direction, Following GPS,” have shown us that automating complex processes without any human touch has a high propensity to go awry. Relying 100 percent on automation without any human intervention can leave processes stale and keep businesses stuck in a holding pattern, waiting for the next major process update that could take months to years to complete.

That's why innovative companies are leveraging next-generation technologies that integrate social and collaborative capabilities at the platform layer of automation tools to create a human-centric approach to complex process automation.

More traditional APM automation tools enable users to leverage reporting and analytics to detect issues and then use static run books to remediate those issues. But rather than getting a real-time glimpse into service issues, these static procedures are only providing a snapshot in time. What if the users had access to more than analytics and static run books? What if the users were empowered with the knowledge of an organization’s subject matter experts in real-time?

Traditional runbooks typically contain static decision trees that capture a process at one given point in time. Collaborative-driven automation tools feature dynamic decision trees, which allow users to drill down to resolutions faster within the knowledge management database, based on a series of intuitive questions assessing the symptom or the reported application issue.

The effectiveness of these decision trees is enhanced when the organization's most skilled experts are updating or adding to resolutions in real time to address newly emerging and/or more prominent topics. The result is a method of dynamic knowledge capture that keeps the bank of procedures current, so that users are able to rely upon information that reflects the resolutions that work best at any given point in time.  

With this immediate access to real-time updated knowledge, innovative companies are empowering human ingenuity in their organizations and achieving the below results with the latest APM automation tools:

- End-to-End Process Automation with unified orchestration and collaboration, combining multiple automation solutions into one process with integrated workflow capabilities and end-to-end reporting across multiple and parallel workflows. 

- First level staff are enabled to perform automated diagnostics and remediation in response to both inbound tickets and analytic trends and notifications picked up by performance reporting tools.

- Associate skillsets are being normalized with automations that don’t require advanced or specialized skills to create. Relevant knowledge documents are “pushed out” based on incident/issue type, and decision tree technology guides IT/First-Level technicians to relevant information and automations based on the symptoms presented.

- Improved application availability for end-users is created by reducing downtime cycles from hours to minutes and reducing the number of emergency bridge calls required to resolve issues.

- Compliance and auditing (CoBIT/SOX) are improved with analytics for audit trails and SLA compliance.

- Reduced average Mean Time to Resolution (MTTR) can be seen through enabled engineers.

- Application teams can run tests outside of their application and assign fault to those groups without a bridge call.  

- Problem solving steps are automatically executed in parallel instead of serial manual execution. Issues are no longer fixed by the engineer logging into tool #one, executing a series of commands, interpreting results, then logging into tool #two, executing commands, interpret results, etc. Instead, the engineer runs a series of commands simultaneously at the push of a button, and gets back results in a simple to understand format.

Application Performance Management entails complex processes that can and should be automated. But rather than eliminate human touch, automation tools should empower associates to execute the best possible automations with the collective, real-time knowledge of the organization.

When organizations implement automation technologies that leave human collaboration out of the process, it isn’t difficult for the “less-than-best” process to be followed, requiring multiple teams to be thrown into fire-fighting mode. Improved collaboration on processes allows more time to be spent on strategic initiatives and proactive management of applications. Not only will the entire organization benefit, so will the customers. And making and keeping customers happy should be the top goal for every organization.

ABOUT Payal Kindiger

As Executive Vice President of Marketing and Managed Services for gen-E, Payal Kindiger leads the company’s branding and marketing efforts, inside sales operations, organizational strategy, customer care, and managed services offerings. Prior to joining gen-E in 2003, she was a member of the management team at Deloitte and Touche. She has worked with several Fortune 500 companies and has managed client-service projects in IT business process re-engineering and organizational development across a number of industries. 

Related Links:

www.gen-e.com

Hot Topics

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

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.