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Hyper-Automation: IT's Path to Edge Self-Reliance

Rex McMillan

A distributed, remote workforce is the new business reality. How can businesses keep operations going smoothly and quickly resolve issues when IT staff is in San Jose, employee A is working remotely in Denver at their home and employee B is a salesperson still doing some road traveling? The key is an IT architecture that promotes and supports "self-healing" at the endpoint to take care of issues before they impact employees. The essential element to achieve this is hyper-automation.

According to Gartner, "Hyper-automation refers to the combination of multiple machine learning, packaged software and automation tools to deliver work."

Businesses, notably IT and help desk administrative staff, are fully aware that becoming more self-reliant at the endpoint by integrating hyper-automation is one of the paths to stabilizing business productivity in the new work reality.

Getting to hyper-automation means evolving from basic workflow automation to augmented Artificial Intelligence (AI) and machine learning [conversational bots], then to a confluence of hyper-automation with deep learning capabilities. AI and machine learning, for example, enables self-healing by predicting and proactively fixing an issue at the endpoint before it disrupts performance.

The Autonomous Worker

Hyper-automation benefits all employees, wherever they are working, by supporting a consumer-grade experience at the endpoint. All devices employees use can be detected, diagnosed and auto-remediated for any security or compliance issues, without interrupting the employee's work.

It also is a significant budget lowering and time saving benefit to IT staff faced with managing a more diversified work environment without having to add more employees. Most importantly, it takes an enormous burden off of help desk teams as self-healing solutions can enable endpoints to heal themselves. Given the more flexible hours around-the-clock that remote workers tend to follow, self-healing lets a night owl work trouble-free at midnight, or an early bird finish a sales report at 5 a.m. without an IT hiccup preventing them from shipping the report.

Autonomous working really can only be achieved with moving IT problem resolution via self-healing to the edge. In fact, today hyper-automated platforms can self-heal close to 70% of edge and endpoint device issues — protecting users, securing data and optimizing user experiences without any human intervention.

The Secure, Autonomous Edge

IT and help desk staff need to also ensure data security along with providing self-healing for the autonomous edge. An uptick in remote working, and endpoints put into service that may not have been properly vetted, may have helped contribute to more security headaches: 66% of IT professionals reported an increase in security issues during the spring of 2020.

To maintain tight security, regardless of device or location, hyper-automation can accomplish this with "adaptive security." Using AI and machine learning — continuously sensing, discovering, and detecting security issues — IT can prevent rogue devices, for example, from disrupting the network. Issues can be prioritized based on machine learning enabled, predictive cognition. Self-healing then kicks in, remediating issues proactively before the end user even realizes there was an issue.

The Rationale for Hyper-Automation

AI. Machine learning. Automation. Bots. Is it worth it for IT and help desk teams to embrace more technology solutions in a business environment already crowded with hybrid-cloud computing, BYOD devices, and a host of data applications?

There are many reasons why the answer is yes. Apart from performance enhancement, security benefits and user productivity, there is a quantifiable ROI business value to deploying hyper-automation. When using hyper-automation to discover, manage, secure and service devices across an enterprise, we've seen customers reduce unplanned device outages up to 63%, reduce time to deploy security updates by 88% or even resolve up to 80% of endpoint issues before users report them. Business continuity is the ultimate ROI and hyper-automation directly contributes to an uninterrupted workflow.

The Path to the Autonomous Edge

Integrating hyper-automation into the IT architecture needs to start with looking at the need. What issues consume the most help-desk time?

How many of those could be proactively resolved using AI and machine learning to troubleshoot and fix the problem?

Then IT needs to have a complete picture of all the endpoints under their supervision, along with associated software and peripherals.

With the goal of a satisfying, secure user experience, the next step is to identify the optimal configuration and performance settings. Even more ideal is to personalize the experience for the end-user to make their workspace familiar and productive. Once the optimal settings to keep a device and user workspace secure and productive are identified IT staff can then automate detecting if the device drifts from that optimal state and return it back, keeping the workspace secure and productive.

Hyper-automation, using built-in AI with bots, can also take more sophisticated actions that contribute to ROI. IT can work with finance and operations teams to identify where integrating more AI can improve budget control and performance. Possibilities include assessing asset inventory in real-time, validating security configurations across a broadly dispersed or remote device estate, or even self-heal issues such as configuration drift, performance or compliance issues.

The Self-Reliant Future

Today's hyper-automated platforms, based on our experience, are delivering up to 70% self-healing for edge and endpoint devices. However, that may reach 100% autonomy over the next few years. The autonomous worker, using a device remotely, will no longer rely on a help desk to fix issues but feel confident the hyper-automation tools in place will be handling an issue before it gets to their personal workspace.

IT teams, with hyper-automation, AI and machine learning technology to support them, can shift to more strategic initiatives that will enhance business value and begin thinking about the next operational advancement.

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Hyper-Automation: IT's Path to Edge Self-Reliance

Rex McMillan

A distributed, remote workforce is the new business reality. How can businesses keep operations going smoothly and quickly resolve issues when IT staff is in San Jose, employee A is working remotely in Denver at their home and employee B is a salesperson still doing some road traveling? The key is an IT architecture that promotes and supports "self-healing" at the endpoint to take care of issues before they impact employees. The essential element to achieve this is hyper-automation.

According to Gartner, "Hyper-automation refers to the combination of multiple machine learning, packaged software and automation tools to deliver work."

Businesses, notably IT and help desk administrative staff, are fully aware that becoming more self-reliant at the endpoint by integrating hyper-automation is one of the paths to stabilizing business productivity in the new work reality.

Getting to hyper-automation means evolving from basic workflow automation to augmented Artificial Intelligence (AI) and machine learning [conversational bots], then to a confluence of hyper-automation with deep learning capabilities. AI and machine learning, for example, enables self-healing by predicting and proactively fixing an issue at the endpoint before it disrupts performance.

The Autonomous Worker

Hyper-automation benefits all employees, wherever they are working, by supporting a consumer-grade experience at the endpoint. All devices employees use can be detected, diagnosed and auto-remediated for any security or compliance issues, without interrupting the employee's work.

It also is a significant budget lowering and time saving benefit to IT staff faced with managing a more diversified work environment without having to add more employees. Most importantly, it takes an enormous burden off of help desk teams as self-healing solutions can enable endpoints to heal themselves. Given the more flexible hours around-the-clock that remote workers tend to follow, self-healing lets a night owl work trouble-free at midnight, or an early bird finish a sales report at 5 a.m. without an IT hiccup preventing them from shipping the report.

Autonomous working really can only be achieved with moving IT problem resolution via self-healing to the edge. In fact, today hyper-automated platforms can self-heal close to 70% of edge and endpoint device issues — protecting users, securing data and optimizing user experiences without any human intervention.

The Secure, Autonomous Edge

IT and help desk staff need to also ensure data security along with providing self-healing for the autonomous edge. An uptick in remote working, and endpoints put into service that may not have been properly vetted, may have helped contribute to more security headaches: 66% of IT professionals reported an increase in security issues during the spring of 2020.

To maintain tight security, regardless of device or location, hyper-automation can accomplish this with "adaptive security." Using AI and machine learning — continuously sensing, discovering, and detecting security issues — IT can prevent rogue devices, for example, from disrupting the network. Issues can be prioritized based on machine learning enabled, predictive cognition. Self-healing then kicks in, remediating issues proactively before the end user even realizes there was an issue.

The Rationale for Hyper-Automation

AI. Machine learning. Automation. Bots. Is it worth it for IT and help desk teams to embrace more technology solutions in a business environment already crowded with hybrid-cloud computing, BYOD devices, and a host of data applications?

There are many reasons why the answer is yes. Apart from performance enhancement, security benefits and user productivity, there is a quantifiable ROI business value to deploying hyper-automation. When using hyper-automation to discover, manage, secure and service devices across an enterprise, we've seen customers reduce unplanned device outages up to 63%, reduce time to deploy security updates by 88% or even resolve up to 80% of endpoint issues before users report them. Business continuity is the ultimate ROI and hyper-automation directly contributes to an uninterrupted workflow.

The Path to the Autonomous Edge

Integrating hyper-automation into the IT architecture needs to start with looking at the need. What issues consume the most help-desk time?

How many of those could be proactively resolved using AI and machine learning to troubleshoot and fix the problem?

Then IT needs to have a complete picture of all the endpoints under their supervision, along with associated software and peripherals.

With the goal of a satisfying, secure user experience, the next step is to identify the optimal configuration and performance settings. Even more ideal is to personalize the experience for the end-user to make their workspace familiar and productive. Once the optimal settings to keep a device and user workspace secure and productive are identified IT staff can then automate detecting if the device drifts from that optimal state and return it back, keeping the workspace secure and productive.

Hyper-automation, using built-in AI with bots, can also take more sophisticated actions that contribute to ROI. IT can work with finance and operations teams to identify where integrating more AI can improve budget control and performance. Possibilities include assessing asset inventory in real-time, validating security configurations across a broadly dispersed or remote device estate, or even self-heal issues such as configuration drift, performance or compliance issues.

The Self-Reliant Future

Today's hyper-automated platforms, based on our experience, are delivering up to 70% self-healing for edge and endpoint devices. However, that may reach 100% autonomy over the next few years. The autonomous worker, using a device remotely, will no longer rely on a help desk to fix issues but feel confident the hyper-automation tools in place will be handling an issue before it gets to their personal workspace.

IT teams, with hyper-automation, AI and machine learning technology to support them, can shift to more strategic initiatives that will enhance business value and begin thinking about the next operational advancement.

Hot Topics

The Latest

An overwhelming majority of IT leaders (95%) believe the upcoming wave of AI-powered digital transformation is set to be the most impactful and intensive seen thus far, according to The Science of Productivity: AI, Adoption, And Employee Experience, a new report from Nexthink ...

Overall outage frequency and the general level of reported severity continue to decline, according to the Outage Analysis 2025 from Uptime Institute. However, cyber security incidents are on the rise and often have severe, lasting impacts ...

In March, New Relic published the State of Observability for Media and Entertainment Report to share insights, data, and analysis into the adoption and business value of observability across the media and entertainment industry. Here are six key takeaways from the report ...

Regardless of their scale, business decisions often take time, effort, and a lot of back-and-forth discussion to reach any sort of actionable conclusion ... Any means of streamlining this process and getting from complex problems to optimal solutions more efficiently and reliably is key. How can organizations optimize their decision-making to save time and reduce excess effort from those involved? ...

As enterprises accelerate their cloud adoption strategies, CIOs are routinely exceeding their cloud budgets — a concern that's about to face additional pressure from an unexpected direction: uncertainty over semiconductor tariffs. The CIO Cloud Trends Survey & Report from Azul reveals the extent continued cloud investment despite cost overruns, and how organizations are attempting to bring spending under control ...

Image
Azul

According to Auvik's 2025 IT Trends Report, 60% of IT professionals feel at least moderately burned out on the job, with 43% stating that their workload is contributing to work stress. At the same time, many IT professionals are naming AI and machine learning as key areas they'd most like to upskill ...

Businesses that face downtime or outages risk financial and reputational damage, as well as reducing partner, shareholder, and customer trust. One of the major challenges that enterprises face is implementing a robust business continuity plan. What's the solution? The answer may lie in disaster recovery tactics such as truly immutable storage and regular disaster recovery testing ...

IT spending is expected to jump nearly 10% in 2025, and organizations are now facing pressure to manage costs without slowing down critical functions like observability. To meet the challenge, leaders are turning to smarter, more cost effective business strategies. Enter stage right: OpenTelemetry, the missing piece of the puzzle that is no longer just an option but rather a strategic advantage ...

Amidst the threat of cyberhacks and data breaches, companies install several security measures to keep their business safely afloat. These measures aim to protect businesses, employees, and crucial data. Yet, employees perceive them as burdensome. Frustrated with complex logins, slow access, and constant security checks, workers decide to completely bypass all security set-ups ...

Image
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In MEAN TIME TO INSIGHT Episode 13, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses hybrid multi-cloud networking strategy ...