AITSM: How AI is redefining IT service desk automation

Ivanti’s Digital Experience Research Report Series

Using AI and automation in enterprise service management (ESM) isn’t new, but excitement is growing about emerging use cases that make organizations more efficient, agile and responsive. Here we break down the latest research on this topic for IT leaders.

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Introduction

Ivanti surveyed over 16,200 office workers, IT professionals and organizational leaders across the globe to uncover:  

  • The current state of the IT skills gap — and how the Everywhere Work movement is putting pressure on overtaxed teams.  
  • Use cases for AI and service desk automation in ESM — including emerging applications.  
  • How asset management can maximize operational efficiencies and improve service delivery.  
  • How highly integrated, interoperable systems are the building blocks of the so-called “augmented, connected workforce.”  

More information about our research study and methodology can be found in the final section

Key Finding #1

Boosting productivity

The Everywhere Work movement has improved employees’ work lives — but it’s also inadvertently added stressors for high-value IT talent. Can service desk automation and gen AI restore order to enterprise service management (ESM)?

Problem today

Most organizations have evolved to allow at least some of their employees to work remotely. Ivanti’s research shows that, as of 2023, 53% of office workers and 78% of IT workers globally work virtually at least part time.

This shift to Everywhere Work — where office employees have the tools to work productively anytime, anywhere — has also increased IT’s workload due to multiple stressors:  

  • Growing volume/complexity of services: With so many people working either partially or wholly off-site, the services required to meet their expanded needs are growing more complex — from new tech-enabled options for employees to access HR services, to tools that monitor remote workers’ productivity and cyberhygiene. By introducing these tools and devices, there is a significant rise in cybersecurity risk. And nowadays, the ways end users access the IT service desk run the gamut, including web-based applications, digital assistants, chatbots‌ and workflow automations. All this complexity affects IT; 39% of IT professionals report too many logins, 47% report too many digital notifications and 42% report using too many tools/platforms.
     
  • Expanding ecosystem of endpoints: Everywhere Work has also created a ballooning ecosystem of devices logging into the network from off-site work locations — only some of which are authorized and/or managed.

    Ivanti’s research shows 81% of office workers admit they are using some type of personal device for work. Of those, half are logging in to work networks and software on their personal devices. And 40% say their employers don’t know about their activities.
     
  • Challenges to employee digital experience: Both IT professionals and office workers at large say the tools they use at work are not always adapted to the Everywhere Work landscape — but the problem appears to be most acute for IT. Fully 31% of IT professionals say they have difficulty connecting from remote work locations, and 39% complain about slow network connections — among a wide array of tech challenges they experience.  


Why it matters

Stress and burnout are at all-time highs. A global study from Gallup found 44% of workers worldwide reported feeling stressed during “a lot of the day” over the previous work day. And tech teams — the people powering the Everywhere Work revolution — appear to be particularly at risk. 

  • More than 3 in 4 IT professionals say work stress is affecting their physical or mental health, and 68% say they feel burnt out by their work. These mental stressors can drive up turnover and put pressure on productivity. Case in point: Ivanti’s research finds IT professionals are 1.4x more likely to “quiet quit” than other knowledge workers.  
  • 1 in 5 of those who switched jobs in the last 12 months say that inadequate/ineffective employer-provided technology was a part of their decision.  
  • Given the growing complexity of the IT ecosystem — and the tools required to manage it — tech workers are feeling the pressure. IT professionals are nearly 2x more likely than other office workers to say they have difficulty connecting to the network from a remote location, and significantly more likely to report a variety of other tech-specific problems — a largely hidden point of friction.

What if IT service desk automation and generative AI could increase helpdesk throughput, make IT teams more product‌ive and lower the overall security risk posed by Everywhere Work? These improvements may also relieve stress and improve job satisfaction for overworked IT teams.

Advances in AI, machine learning and automation can chip away at the IT-specific challenges of Everywhere Work — and IT professionals largely agree. Roughly half of IT professionals say AI will boost employee productivity and reduce the share of mundane, routine work they need to complete. (Keep in mind, IT workers rated “repetitive tasks” as a top-three challenge related to tech support in their organizations.)



Key Finding #2

Remaking workflows

Let’s take it a step further. AI-powered ESM will revolutionize IT teams — remaking workflows, throughput and agility. It will also change the mix of skills and experience needed in IT.

Problem today

Despite rising levels of unemployment, most companies are still struggling to find highly qualified IT talent.

Research from MIT Tech Review Insights found that most tech leaders (64%) say IT candidates “lack necessary skills or experience,” and 56% say the lack of qualified candidates is a cause for concern.  

For IT teams, it’s a one-two punch:  

  • Helpdesk volume is up due to the heavy demands of Everywhere Work. 
  • Attracting and retaining qualified IT professionals is extremely challenging. 

What if AI and machine learning could (a) reduce ticket volume by addressing lower-level queries via IT service desk automation and (b) reduce escalations by empowering frontline helpdesk analysts with greater knowledge and reach — and, by doing this, ease the pressure caused by a persistent IT skills gap in the talent market?  

  • Reducing ticket volume: AI-powered ESM technology can reduce workloads by proactively addressing issues before they register as problems, notifications, complaints, etc. Example: monitoring the health of individual devices on the network, then acting on signals to automatically create a ticket and initiate maintenance — all done before a problem arises or a human needs to intervene.
  • Expediting tickets: Companies can also improve throughput by automating how tickets are routed — ensuring the right person gets the request the first time. No more tedious, time-wasting handoffs that frustrate both IT teams and end users.
  • Intercepting low-level queries: Virtual support agents (VSAs) are chatbots purpose-built for a service management environment. They can do things like reset passwords or manage software updates — handling repetitive tasks that free IT employees to focus on more complex problems. In essence, VSAs are chatbots that can take preprogrammed ITSM actions.   
  • Reducing escalations: A purpose-built knowledge management AI can automatically update the organization's knowledge base using existing helpdesk data, then turn the knowledge base into a helpful copilot for IT analysts, arming them with relevant information to handle higher-order tasks.  


Why it matters

We know AI and automation can deliver productivity (e.g., eliminating rote tasks, prioritizing). But what if AI and service desk automation can also help address the IT skills gap?

  • Move existing IT employees up the value chain and empower less experienced employees to take on higher-order tasks.
  • Drastically lower the amount of time needed to onboard and train new IT employees.
  • Give leaders the flexibility to choose the right mix of skills/people to drive desired performance outcomes.

The latest trends analysis report from Gartner® finds that “through 2027, 25% of CIOs will use augmented, connected workforce initiatives to reduce time to competency by 50% for key roles.”*

AI will remake knowledge management as we know it today due to two important developments:

  • Organizations will increasingly use AI to build and improve the knowledge base automatically. Considering that updates to knowledge bases are often deprioritized when IT teams are overwhelmed with work, this is a big advantage.
  • IT professionals will use AI solutions to query the knowledge base, get a clearly phrased response in return‌ and resolve tickets faster. As these AIs improve over time, organizations may find they can rely on IT talent with less experience or expertise to handle complex or nuanced questions.

Using AI-powered knowledge management, organizations will be able to increase efficiency, while also being more resilient to talent shortfalls and turnover for IT roles. 

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Handing over grunt work to machines lets people focus on more fulfilling parts of their jobs. The tech also seems to level out skills across a workforce: early studies ... suggest that less experienced people get a bigger boost from using AI.

 

Will Douglas Heaven
Senior editor for AI at MIT Technology Review

Key Finding #3

Prioritizing visibility

Poor visibility still plagues many organizations’ IT teams — and this has ripple effects across a wide range of ESM quality factors, from response times and employee experience to data quality and interoperability. 

Problem today

Poor visibility remains an intractable problem for many organizations and, left unaddressed, can seriously slow AI adoption, high performance and innovation.

The visibility shortfall shows up in two primary areas:  

1. Inventory: Organizations don’t have a complete inventory of all of the devices and endpoints on their networks — and the problem has been magnified by a proliferation of devices empowering the anytime, anywhere workforce, including:  

  • Nontraditional devices like IoT sensors and wearable tech.  
  • Unapproved and/or unmanaged devices, called “bring your own device” or BYOD.  
  • A poor understanding of the number and reach of tech/licenses on the network.  
  • Few solutions that provide a single source of truth (i.e., mapping relationships across an organization’s tech ecosystem and providing shared visibility).   


2. Asset management: With an incomplete inventory, organizations struggle to aggregate and act on device data.  

  • Incomplete contextual data about devices, such as specs or warranty information, makes it harder for the IT team to provide service at the individual device level. 
  • Without an accurate inventory of devices and licenses, organizations cannot make informed decisions about IT expenditures, cost optimizations or device refresh schedules.  
  • Leveraging proactive, self-healing solutions is all but impossible when organizations lack deep asset intelligence across their tech ecosystems.

Why it matters

Without enterprise-wide visibility, organizations can’t innovate effectively or leverage the high potential of AI. 

Organizations are struggling under the weight of tech complexity — perhaps nowhere more acutely than in the tech and data silos that permeate the modern enterprise. These silos not only prevent the widespread adoption of AI/automation, they also perpetuate the feelings of burnout that are so common for IT professionals.  

Modern ESM and AITSM require a connected, interoperable system that can detect signals across a wide-ranging ecosystem — pinpointing issues in real time and responding through a combination of AI and automation, even outside IT workflows.  

And as an added bonus, these scalable, agile systems improve employee productivity and experience for your IT team.  

What does the future of ESM look like?

  • Deep IT asset discovery and inventory: Determining what users and endpoints connect to the network, when they connect and what software is installed on devices. 
  • Proactive service management: Reducing incidents by solving problems before they occur. For example, predicting a slowdown and automating device maintenance — well before an employee detects any problem.  
  • Cost optimization: Scrutinizing software spend and redundancies using high-powered AIs; finding ways to maximize investments across the entire organization.  
  • ESM for ecosystems: Expertly managing services outside traditional IT workflows, such as security operations management.  



Action steps

Experts weigh in on the most important elements of your 2024 game plan to transform IT service desk automation.

1. Establish responsible AI policies and governance

 

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But don’t stop there. Establish your company's ethical view of AI’s broader impacts — on the company, its employees and its customers‌ — ‌and develop practices aimed at addressing them. For example, to ease employees’ anxiety about AI implementation, set up trainings to help them understand the technology, how it’s likely to be applied to their roles and what they can do to stay relevant in those roles.  

On an organization-wide basis, foster discussion of what AI could ultimately mean to the business. Surface use cases and explore them to help understand what new jobs might be created and what other jobs might have to be transitioned elsewhere. Go systematically through the organization’s key functions to get a picture of AI’s likely impact across the organization."

 

Robert Grazioli

Robert Grazioli
Chief Information Officer, Ivanti

2. Improve data accuracy/accessibility for better asset management and services

 

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Your organization absolutely needs accurate data. Inaccurate or incomplete data will hinder your AI adoption, because we know AI requires large amounts of high-quality data for training and operation. What’s more important, without complete, accurate data, you can really only be reactive; you can't be proactive.

Example: If you don't know what software and hardware you have in the organization, you're most likely overspending. Eliminate those blind spots. Improve your asset visibility, and then take steps to monitor those assets. With that granular monitoring data, you can identify hidden or unnecessary costs or look for proactive ways to reduce costs. 

Second, combine the data silos you have in your organization. Some of those data silos would be your asset inventory, your asset management software inventory, your data center inventory service maps, tickets, information, knowledge bases, or monitoring logs. By combining that data, you don't have just a singular view of an asset; you get a 360-degree view of it — and you can use it to achieve greater outcomes."

 

Daren Goeson 

Daren Goeson
Senior Vice President of Product Management, SUEM, Ivanti

3. Connect the dots faster

 

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Think of AI as a mining tool that can uncover patterns and connections that point the way toward improving a whole host of your company's operations — from customer support to system performance to decision making. 

For example, AI can quickly identify patterns in customer tickets that might not be noticeable to IT team leaders. This can help surface solutions that allow for faster, more effective responses to customer problems, or even suggest preventive measures for avoiding those problems in the first place.   

Similarly, AI’s capacity for speedy analysis can be put to work adjusting — in near-real time — the resources needed to meet performance standards. This can improve customer service, support fulfillment of contractual obligations, and help manage costs by scaling resources based on demand."

 

Robert Grazioli 

Robert Grazioli
Chief Information Officer, Ivanti

4. Leverage AI for knowledge management

 

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AI knowledge management programs streamline the way you retrieve, organize, rank, and render information — and then they personalize the information served up, based on what’s relevant to the user’s needs and preferences. This not only reduces the time employees spend looking for information, but it also helps eliminate knowledge silos, making teams and processes more resilient when there are staffing turnovers or shortfalls.  

When it comes to creating content — for example, a knowledge base article — gen AI can do that in minutes or even seconds, whereas it might take a service-desk agent hours. But beyond that, AI can also manage the lifecycle of that content, suggesting ways to revise or replace it based on usage data, or ways to increase its relevance to your users. 

Centralize the knowledge repository and make it a single source of truth by collecting and consolidating knowledge from various sources. The goal here is really to break down barriers and promote more inclusive, accessible knowledge-sharing environments."

 

Sirjad Parakkat 

Sirjad Parakkat
Vice President of Engineering, Ivanti

Methodology

This report is based in part on two surveys conducted by Ivanti in the first half of 2023: Elevating the Future of Everywhere Work and New Imperatives for Digital Employee Experience. In total, these two studies surveyed 16,200 executive leaders, IT professionals and office workers. The report also cites research from third-party sources.  


 

*Gartner, Top Strategic Technology Trends for 2024, By Bart Willemsen, Gary Olliffe, Arun Chandrasekaran, 16 October 2023
GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. 

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