Hyper-Automation: IT's Path to Edge Self-Reliance
October 06, 2020

Rex McMillan
Ivanti

Share this

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.

Rex McMillan is Principal Product Manager at Ivanti.
Share this

The Latest

November 28, 2022

Many have assumed that the mainframe is a dying entity, but instead, a mainframe renaissance is underway. Despite this notion, we are ushering in a future of more strategic investments, increased capacity, and leading innovations ...

November 22, 2022

Most (85%) consumers shop online or via a mobile app, with 59% using these digital channels as their primary holiday shopping channel, according to the Black Friday Consumer Report from Perforce Software. As brands head into a highly profitable time of year, starting with Black Friday and Cyber Monday, it's imperative development teams prepare for peak traffic, optimal channel performance, and seamless user experiences to retain and attract shoppers ...

November 21, 2022

From staffing issues to ineffective cloud strategies, NetOps teams are looking at how to streamline processes, consolidate tools, and improve network monitoring. What are some best practices that can help achieve this? Let's dive into five ...

November 18, 2022

On November 1, Taylor Swift announced the Eras Tour ... the whole world is now standing in the same virtual queue, and even the most durable cloud architecture can't handle this level of deluge ...

November 17, 2022

OpenTelemetry, a collaborative open source observability project, has introduced a new network protocol that addresses the infrastructure management headache, coupled with collector configuration options to filter and reduce data volume ...

November 16, 2022

A unified view of digital infrastructure is essential for IT teams that must improve the digital user experience while boosting overall organizational productivity, according to a survey of IT managers in the United Arab Emirates (UAE), from Riverbed and market research firm IDC ...

November 15, 2022

Building the visibility infrastructure to make cloud networks observable is a complex technical challenge. But with careful planning and a few strategic decisions, it's possible to appropriately design, set up and manage visibility solutions for the cloud ...

November 14, 2022

According to a recent IT at Work: 2022 and Beyond study, there have been a few silver linings to the pandemic ... The study revealed some intriguing trends, which will be discussed in turn ...

November 09, 2022

The absence of topology can be a key inhibitor for AIOps tools, creating blind spots for AIOps as they only have access to event data. A topology, an IT service model, or a dependency map is a real-time picture of tools and services that are connected and dependent on each other to deliver an IT service ...

November 08, 2022

A modern data stack is a suite of technologies and apps built specifically to funnel data into an organization, transform it into actionable data, build a plan for acting on that data, and then implement that plan. The majority of modern data stacks are built on cloud-based services, composed of low- and no-code tools that enable a variety of groups within an organization to explore and use their data. Read on to learn how to optimize your data stack ...