AITSM: How AI is redefining IT service desk automation

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.  

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.

1. Establish responsible AI policies and governance

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
Chief Information Officer, Ivanti

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

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
Senior Vice President of Product Management, SUEM, Ivanti

3. Connect the dots faster

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
Chief Information Officer, Ivanti

4. Leverage AI for knowledge management

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
Vice President of Engineering, Ivanti