AITSM – The Next Evolution in ITSM

AI Service Desk
AI & Automation
AITSM – The Next Evolution in ITSM

AITSM – The Next Evolution in ITSM

AI Service Desk
AI & Automation
AITSM – The Next Evolution in ITSM

In the second article in this series, we described how a maturity model can help you assess where your organization is on your service management journey. There are two main thrusts described:  

  1. The organization’s ability to improve (CMMI model) 
  1. Progression from Cost Center to Business Partner (Service Excellence model) 

There may be other maturity models that your organization prefers to use, but essentially, you want to progress from being seen as a cost center to being a business partner and trusted advisor with the ability to continually improve. The big question then becomes, “How can we accomplish those goals, and what tools are there to enable us?”  

There is currently huge interest in, and impact being felt from, the widespread availability of Generative AI. Chris Howard, Chief of Research at Gartner said, “…generative and other types of AI offer new opportunities and drive several trends. But deriving business value from the durable use of AI requires a disciplined approach to widespread adoption along with attention to the risks.” 

It’s worthwhile, then, to explore some of the ways GenAI can bring real change to ITSM – and it can be truly labeled as AITSM. We’ll take advice from the ITIL® guiding principle, start where you are. 

What is KTLO, and Why Does it Matter? 

The reasons IT is often seen as a cost center are many, but “keeping the lights on,” (KTLO) work is certainly one of them. KTLO work includes the service desk work of responding to incidents and requests, but also broader core IT functions such as data backup and maintenance, network administration, operating system and application patching and updates, and monitoring; in short, all the work it takes to keep the information systems running and available. ITSM good practices help us understand how these IT functions work together to keep our organizations going. This work is not trivial, and it tends to be expensive.   

But KTLO is generally reactive work; it only maintains the status quo. It is not innovative and does not bring improvements in goods or services. This is why IT has had such difficulty both understanding and explaining how it brings added value to the organization or helps reach business or institutional goals.  

Reducing IT expenditures through the common practices of reducing or freezing staff levels and training or lengthening the replacement or upgrade cycles of technologies tends to increase KTLO time as employees struggle to do the same job with fewer resources, and aging tech suffers more frequent interruptions. 

The work of keeping the lights on saps the IT organization (whether it is considered a department) of time and resources that could otherwise be applied to the kinds of innovation businesses and other organizations want and expect.  

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The more time IT is wrapped up in doing KTLO work, the less time it has for “technology experimentation” and the fewer resources it must drive “technology-fueled change,” i.e., the type of change that brings tangible value to the organization and its customers. 

One keyway to reduce KTLO is to reduce the number of incidents that impact business processes. The second article quoted HDI’s The State of Service Management in 2022 as saying that one-third of organizations saw an increase in incidents resolved without business impact through the practices of service management. If we can improve the ways service management works, we should see a further decrease in incidents that impact the conduct of business. 

Here are some ways good service management practices can reduce the impacts of unplanned interruptions: 

  • Problem management: Since a problem is “a cause, or potential cause, of one or more incidents,” effective problem management can directly affect the frequency and severity of interruptions. 
  • Availability management: Ensures that services meet agreed levels of availability to customers and users. 
  • Capacity and performance management: Ensures that services achieve agreed and expected levels of performance. 
  • Service continuity management: In case of unexpected events such as natural disasters, this practice aims to ensure that services are not interrupted. 
  • Service level management: This practice ensures that accurate information about services and the systems (configuration items) that support them is available. 
  • Service level management: This practice sets clear business-based targets for service performance. 
  • Change enablement: This practice aims to ensure that risks are properly assessed before changes are released. 
  • Release management: This practice makes new and changed services available for use. 
  • Incident management:: If and when incidents do occur, this practice aims to restore services as quickly as possible with minimum impact. 
  • Monitoring and event management: This practice refers to observing services and service components and recording and reporting selected changes of state identified as events. 

These descriptions are based on the ITIL® 4 glossary. (ITIL® is a registered Trademark of Axelos Limited. All rights reserved.)  

As we can see, the appropriately adopted and adapted practices listed here are intended to reduce—or even eliminate—unplanned interruptions to IT services and the impact of those interruptions on the organization’s normal operations as well as on the time and resources necessary for technology-driven innovation.  

AITSM in Action – 

The speed and power of Generative AI & modern automation can be used in many ways to accelerate and simplify service management. Take a look. 

Conversational Interface within Microsoft Teams 

For more than a decade, Service delivery was often done with the help of a portal. Employees were supposed to come to a portal for self-help, creating tickets, reporting issues, finding knowledge, requesting service or managing change requests. However, as detailed in the segment above, this never worked out the way ITSM companies and ITSM managers envisioned. And now, there is a better service delivery channel which might be the death knell for legacy-style portals.  

Most research shows that only about 35% of all employees contact the service desk for assistance, meaning that many IT issues, questions, and even requests go unaddressed because they are unknown. In legacy systems, almost 85% of the tickets never even got recorded – simply because the end users are often not willing to endure the hassle of logging into the service desk portal, creating a ticket, tracking it, and waiting endlessly for the resolution. Another contributing factor is that certain IT-related assets are not connected to a specific end user; think about a printer used by everyone on the floor. If the printer fails, whose responsibility is it to report it, track the ticket, and test the fix? Many employees would rather walk to the next available printer rather than become involved in the process, even if the printer remains broken for weeks. 

Email used to be the go-to channel for communicating any issue 1-on-1, and so it was often adopted as a support channel as well. Employees and customers, rather than trying to find portals, then often having to log in and find the correct way to report and issue or ask a question, preferred to start a chain of emails. From the consumer or user perspective, this made perfect sense, but it created some issues for the support staff.

  • Shared email boxes (like “support@__” or “IT@__”) are not easy to manage 
  • Service desk analysts find it difficult to know which emails have been read and moved into the ticketing system
  • Tracking the time spent on an issue is almost impossible when it is being discussed in email 

Despite the difficulties, 76% of organizations still have email available as a support channel, and report that 33% of all tickets are created through email, according to HDI’s The State of Technical Support in 2023.  

However, in recent times, email as a primary support channel has lost its charm. In addition to the reasons cited above, 90% of the Americans alone report that ‘Email fatigue’ is a drag and one of the most unpleasant aspects of their daily work. Sifting through the inbox, processing work-related, personal, promotional, and critical email takes a toll on most people. Relying on email as a service support channel, hence, is no longer a viable option for ITSM let alone an effective one.  

Augmenting the support services also witnessed the use of Text and SMS service. People still prefer to drop instant text messages to customer support in hopes of faster resolution. This only drives the growing need for a platform/channel that could help them connect 1-on-1 with a capable support resource and get their issue resolved ASAP.  

Modern AITSM has risen to this challenge through Conversational AI Interface. It is already being rolled out for business tools like Microsoft Teams and thousands of others and will be a go-to GenAI integration in the days to come. 

The capability of machines to understand natural human language with incredible accuracy through Natural Language Processing (NLP), and providing a smart output is unprecedented. GenAI technology is far superior to the older generation solutions. IVR and Chatbots were far from perfection, were difficult to create, and did not fulfill the purpose they were created for on the end-user side. (If you’ve been on an IVR line with a credit card department, you know!). Despite being flawed, companies spent millions of dollars, time, and energy on deploying these service solutions – both on the customer side and the employee side. Even the classic AI could not outperform its predecessor solutions, simply because it wasn’t ‘smart enough’ to understand what a human counterpart wanted to communicate with it. 

Now, GenAI-based bots are rewriting the tableau for servicing customers and employees. With Conversational AI models, end-users are essentially communicating with a genius information powerhouse that can: 

  • Understand what the human counterpart is communicating with 99.99% accuracy 
  • Provides accurate answers based on the available knowledge and data 
  • Can execute simple and complex tasks 
  • Is intelligent enough to make decisions and knows when to loop-in a human counterpart for decision-making 
  • Learns and evolves with new knowledge and data  

Older service desks and support systems do not even come close to what GenAI can execute.  A conversational AI ticketing system not only simplifies the process of ‘accessing’ service desk, but also elevates user experience. Here is why Conversational AI Interface within tools like MS Teams trump legacy ticketing portals at any time: 

  • Better and Faster Ticketing: Enables better and much faster ticket creation experience for end users. They do not have to wonder about the next steps on some outdated portal. All they must do is ‘talk’ to the bot within Microsoft Teams and it will handle the rest.  
  • Simplified IT Service Desk  Access: This is potentially a boon for many employees. They can get access to the IT support right within their work environment (say MS Teams), and not wonder about how to get an issue resolved or find some specific destination like a portal. Additionally, GenAI enables multi-lingual support to cater to a diverse set of users.  
  • No Platform Switching: End users do not have to juggle with multiple portal logins and bounce from one support platform to another. Typical ticketing chaos starts from email, redirects to a legacy portal, leads to multiple calls and ultimately ends up untracked for days. With conversational AI, there is no need to switch between different support platforms.  
  • Access Knowledge on Tap: No matter what piece of information, data, or organizational knowledge a user wants, they can simply ask the AITSM bot to pull it out for them. From IT policies to project handbooks, just name it and it is done.  

Thanks to the sophisticated Large Language Models (LLMs), GenAI can “understand” almost anything you have to say with high accuracy and produce desired output. You can use it to generate unique long text paragraphs, short sentences, complex ideas, and simple ones. This is a massive upgrade from classical AI models which were not fit for such tasks. LLMs (like ChatGPT), can understand context, pattern-match their logic, and provide a sound output fast.   

Conversational AI interface is a key differentiator between AITSM and legacy ITSM. And clearly, with AITSM, everything comes to the user for resolution, not the other way around.

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GenAI-enabled ITSM is based on a profound vision. It is actively building a future where GenAI will be woven into the very fabric of ITSM. And, by no means we ought to say that GenAI is only fit for ITSM. Most of us have already seen what ChatGPT can do. More and more software tools are integrating this incredible technology to create phenomenal outcomes. Take MidJourney and Dall-E for instance. These are visual content generation models based on LLM (Large Language Models of AI). You simply describe any visual or abstract concept to them – and voila! They will create exactly what you imagined in your head.   

Generative AI has truly elevated what legacy ITSM used to be and furthered its capabilities by a thousand-fold. AITSM is the innovation that is already transforming IT service desk from cost centers to strategic partners for business continuity and agility-driven enterprise service management. Here is how :

1. GenAI-enabled Knowledge Management 

Traditional document management systems are often called the ‘graveyard of knowledge’ where good usable knowledge, once documented, goes and dies. In many organizations, knowledge documents are usually scattered across multiple siloes. It will not be an overstatement that most legacy IT systems have less than useless knowledge management capabilities. Many times, it seems like this feature is just built-in for the namesake.  

Even if an organization somehow manages to use the documented knowledge for efficient problem-solving and work, users often do not read them. The usability is very low. IT and support agents have to share repetitive information because of lack of the usability of the system.  

Moreover, the existing siloed knowledge documents could be duplicated, outdated, and many times it is hard to tell if the user is accessing the updated version of the knowledge document. It was all over the place – without a single source of truth. 

With an unreliable and outdated knowledge system, self-service gets thrown out of the window. In fact, L1 support agents had no way of adding first-hand value to such a scattered knowledge base – simply because there is no standardization, low usability, and lack of common access layer to it. 

In fact, the experiential knowledge of L1 agents had no easy, rapid way to be formalized so that it can be reused in a future instance (think rebooting a laptop, fixing OneDrive, generating passwords, etc.). For end users, digesting this disparate knowledge is often next to impossible because there is no reliable ‘vehicle’ for them to access the right chunk of knowledge in the right context at the right time!   

GenAI-enabled knowledge management changes all this. 

GenAI can become a single source of truth for the entirety of organizational knowledge. It can ingest information from websites, SharePoint folders, URLs, PDFs, Word Documents, etc. Then, through the conversational interface, provide accurate answers to the end users based on their query. 

Employees and teams can truly access self-help support through the GenAI knowledge bots, which can also execute tasks and processes for them (think software installation, password regeneration, etc.). An outstanding benefit (and relief) here is how polished the GenAI bot’s output knowledge is. LLM’s are trained to pattern-match the information, learn from it, and ‘understand’ the context very well.  

The GenAI bot can even give admins an insight into knowledge clutters, trends, and an overview of how users are interacting with the knowledge base. Users are literally empowered to ‘talk’ to the organization’s knowledge base and access contextually accurate information on-demand.  

2. Conversational Incident Management

Before a ticket is created, AI can deliver the information end-users need to solve issues for themselves without having to go to a destination (portal) and search for answers. In some cases—and likely more as time goes on—any needed fix will also be provided. (More on this below.) 

  • Ticket assignment and routing can be made more accurate, consistent, and a great deal faster, saving analysts time and effort. 
  • Ticket categorization can be done with increased consistency, taking ad-hoc opinions out of the workflow,, and making reporting and measurement more accurate. 
  • Generative AI’s summarization capabilities can save valuable time on every interaction, making the comments and notes in tickets more consistent and complete and automating the extraction and creation of knowledge, which feeds back into the first point about self-solving. 
  • One amazing benefit of AITSM for IT technicians is the accurate summarization of tickets. Tickets are usually handled by many hands and pass through various stages during their lifecycle. At every stage, technicians leave notes and comments on these tickets to describe the remedial steps taken or clarify the persistent issue. For busy agents, going through the summary of each ticket (say during audit or resolution) can be time consuming. However, with GenAI, IT technicians can extract an accurate summary of the ticket with the push of a button. For instance, with, agents can press ‘summarize’ button on the ticket and access the summary of its history instantly.  
  • Many times, IT technicians do not write complete comments or notes on the tickets owing to shortage of time. This is not so user-friendly for the end user. Though, with the ‘Auto-Complete’ feature of GenAI service desk,, the bot writes detailed notes for the ticket, and presents it in a user-friendly format. In addition to this, if agents get in the habit of creating clean and lucid notes for every ticket, it can be used to train the GenAI bot for further accuracy improvements. 

3. Problem Management 

  • Because AI is exceedingly good at summarization and anomaly detection, problem management, with its large component of root cause analysis, can be accelerated and improved. 
  • Known errors can be presented to analysts so that time is not wasted doing rework. 
  • GenAI can pattern-match high-level issues and map the relevant tickets. For instance, tickets created for an internal ERP during a given timeframe can be automatically linked by the bot accurately under a single ticket and queued for further resolution.  
a. Availability Management 
  • Availability of services and demand for them can be analyzed in real-time, enabling adjustments and rapid response.  
b. Capacity and Performance Management  
  • AI can assist in the monitoring and reporting of performance as well as offer some predictive assistance and analysis of user experience. 
  • Keeping users working without interruption or degradation is the goal; optimum performance of the IT infrastructure is the means to reach that goal.  
c. Service Continuity Management 
  • Drawing on data from past events as well as the current state, AI can help assess and mitigate the potential impacts of natural or man-made disasters or other broad interruptions.  

4. Human- in- the -Loop Live Chat in MS Teams

It is only natural to think that many employees and customers prefer to talk to a human agent for resolution. Unsurprisingly, a leading analyst firm revealed that 71% of the employees still preferred to talk to a service desk agent to move further with their issue resolution. Live Chat is therefore a meaningful and necessary part of modern AITSM. is designed with the Human-In-The-Loop (HITL) circuit in mind, so that agent resources can be looped in on any ticket/query when the bot alone cannot process it further. The best part here is that the Live Chat feature is also available right within the conversational interface of Microsoft Teams.  

Moreover, GenAI bot can summarize live chat between users and agents. Based on the conversation, it can create the right ticket, categorize and sub-categorize it accurately, and then auto-resolve it or route it to a suitable resource. It automatically creates a title and summary of the ticket before putting it in the right queue. 

5. Service Configuration Management 

  • AI excels at digesting large amounts of data and presenting it in ways that humans can understand. Configurations now can be incredibly complex, with on-premises, SaaS, and Cloud services being delivered through a large number of device types, from IoT to mobile to desktop.  

6. Streamlined Service Level Management (SLA)

Real-time reporting on actual and potential service level agreement (SLA) breaches is now possible with AI. 

 7. MS Teams Accessible Change Management

  • Again, the ability of AI to analyze large amounts of data can assist in ensuring that changes to various parts of the environment will not have counterproductive affects. 
  • Some of the costliest business failures due to IT have come because of changes (see Facebook’s, for example).   
  • Moreover, all communication happens inside a familiar tool like Microsoft Teams. Users can simply ask questions related to change management and the bot will readily provide the same. For instance, an agent might want to reboot the server. There may already be a change management process related to the same, but the agent might not be aware of it. By simply asking the bot, the agent will get the updated steps and procedure for rebooting the server. In such a scenario, AITSM solutions like may even ask the agent to initiate the server rebooting process and offer to assist the agent in the steps to come.   

8. Insightful Dashboard and Reporting 

Legacy service desk systems lack advanced analytics (and even useful dashboards) for the IT support agents. GenAI service desks like provide powerful analytics with an intuitive power-packed dashboard that provides a bird’s eye view of the service delivery and SLAs to the support agents and helps them make informed decisions to remain agile and responsive. IT teams can even create custom dashboards and charts to track the metrics most important to them. can help automate these reports via email on the defined schedule and reduce the pressure of accurate service desk reporting. 

Another standout feature of is its conversational reporting capability. Admins and managers can talk to the bot to pull out reports, summarize information, and instantly find out meta information related to any aspect of the service desk – right within MS Teams.    

9. Conversational Service Request and Catalog 

Traditionally, a user must go to the service catalog to see what requests they can make. GenAI service requests are quite different. With GenAI, a user must simply make the request via the conversational interface and the bot matches it to any existing service catalog. For instance, the user can ask for a new laptop and bot will present them with the eligible options (within the interface of your choice) along with approval or no approval requirements.   

ITSM Automation  

Automation is a key part of AITSM and is wildly different from legacy automation used for IT service desk ops. AITSM empowers service desk admins with incredibly intelligent and blazingly fast automated processes through :

1. Automation Integrated with GenAI 

GenAI bot can initiate and execute many processes through simple conversations. This type of automation is a key identifier of a modern AITSM system. For example, if you want to onboard a new employee, you simply need to ask’s bot within MS Teams to do it, and it will initiate the process for you.  

2. Easy-to-Set Up 

Forget coding complex automation sequences which took many days of deploying and testing. AITSM automation is as easy as it gets. For instance, offers a No Code Automation Studio where IT teams can create, test, and deploy custom automations in just a few hours! Organizations can create custom automations to power their workflows, ticket resolution, and collaboration to save tons of time.  

3. Automate Everyday Tasks and Allocate to End Users 

Automation is becoming a backbone for many organizations as it tackles many key repetitive processes. Though, the effectiveness of traditional automation was limited. This is because automation creation and automation adoption are two very different things. Firstly, coding automation took days, then many more to deploy. Once deployed, it was hard to train employees to stick to them and adopt it at enterprise level. For instance, the DevOps team may automate emails for professionals if their resume is marked ‘shortlisted.’ However, the HR team must always push the resume to the recruitment ERP and mark it ‘shortlisted’ for the automation to work. 

Creating and adopting automation used to be a hard problem until the advent of GenAI.    

From sending everyday data analysis reports to concerned teams, to allocating project-specific tasks to the users, GenAI automates everyday tasks like a champ. In fact, with an AITSM platform like, a whole bunch of tasks related to emails, reporting, execution, etc., can be automated through the simple no-code automation studio. Teams can create useful automations in hours and deploy them instantly within a conversational interface like MS Teams.  

4. Automate Repetitive Processes 

GenAI is an excellent tool to automate repetitive processes with incredibly high accuracy. And this can be done across any department. Complex tasks, even those that require multiple approvals, can be automated with ease. Think employee onboarding, resource allocation/reallocation, etc. A modern AITSM solution like can orchestrate such complex processes without any manual intervention.  

5. MS Teams Accessible Approvals

There can be several processes and procedures that require approvalv from the agents/managers concerned. It can be a simple request for issuing a new laptop that requires approval of the IT Manager, or employee onboarding initiation that requires multiple approvals (say from IT, HR, and Accounts). Imagine being the service desk managerv and managing multiple IT assets and resources for employees. An organization with 3,000 people could flood your inbox with requests for dozens of new laptops, mouse replacement, BYOD protocol requests, etc.   

With AITSM, these approval requests can be handled right within an interface of your choice like MS Teams – without any delays or the need for switching to another platform. One can simply get the request within Microsoft Teams and approve/disapprove it within seconds!   

6. Unique Desktop Automation 

Presently, desktop automation looks like a distant dream for many organizations. Typically, agents have to login to a user’s desktop remotely and get things done. There is a need to run multiple PowerShell scripts for even simple automations. Complex automations with traditional methods require even more effort for desktop automation. But, with, the picture changes. Desktop automation is one of the outstanding features of this modern GenAI service desk. Need a system update? Just ask Want to install OneDrive on your laptop? Just ask Want to troubleshoot an error on your desktop? Simply ask’s desktop automation can perform repetitive tasks like

  • Software Install 
  • Network Printer Install 
  • OneDrive Troubleshooting 
  • Enable/Disable USB Drives 
  • Cache Cleaning 
  • Device Settings Configuration 
  • New Laptop Setup Assistance
  • Fix File System Issues 
  • Proactively Monitor Devices and Create Tickets 

With AITSM, service management professionals have a much better view and control of everything that is occurring in the IT environment as well as the potential consequences for business productivity. 

In addition to the Service Management practices described by ITIL (see the first article in this series), AI holds great promise of helping with another of the General Management practices, Information Security Management, through its anomaly detection. We’ll explore the Knowledge Management practice below. 

Overall, AI-assisted IT can make specific, targeted improvements to increase stability and reduce negative impact to other parts of the enterprise. As we’ve discussed elsewhere, preventing business impact is a very desirable outcome. Taken together, service management professionals have a much better view of everything that is occurring in the IT environment as well as the potential consequences for business productivity. There are also advantages for providing real-time information across the IT organization and to end-users. 

Positive Consequences for End-users and for IT 

When services are degraded or unavailable, end-users can’t accomplish their work properly. Meanwhile, IT is attempting to keep up, but often winds up with an ever-increasing backlog of work. Some services have a direct effect on the ability to serve the organization’s customers (think of contact centre technology and websites, for example), but there are also indirect effects. Sales outreach, marketing campaigns, inventory monitoring, ordering, accounts receivable and payable all may be affected by service availability issues, since information technology underpins almost every aspect of businesses and institutions. There is a financial impact to the organization, which is the least desirable outcome. 

When the power of AI is put to work, not only is time saved on each procedure and interaction, but reduction of incidents through the optimization of services and vastly improved understanding of the entire IT landscape means that end-users don’t have to contact the service desk, don’t have to go fishing for answers in a portal, and don’t have to wait for a response. 

To paraphrase an old customer service adage, “The best tech support is no tech support,” meaning that when things don’t break, they don’t need to be fixed.  

The Positive Impact of Knowledge Delivery 

One of the pain points of IT service and support has been the difficulty of making self-service and self-help work to the advantage of both end-users and the support organization. The primary reason for the pain has been that end-users say they want to be able to help themselves, but then do not adopt self-help when it is made available. This indicates that it is not the concept that fails, but rather the implementation.  

Traditionally, self-help has meant that knowledge articles written by support personnel were posted in a user-facing knowledge base, often as part of a self-service portal. This has required end-users to stop what they are working on, go to the knowledge base, and search for an answer. Some of the stumbling blocks here are:  

  • Long knowledge articles written as technical documentation 
  • Articles written in “IT-speak” rather than the language of end-users 
  • Substandard knowledge base search 
  • Out-of-date information 
  • Duplicate information  

This model, while intended to contact volume at the service desk, has been impractical for users to adopt, especially when we stop to consider certain groups of users, who should not be expected to stop and spend time trying to find answers or solve their own issues. Consider: 

  • Medical professionals  
  • Highly paid executives 
  • Professionals such as lawyers, accountants, and consultants whose time is billable 
  • Essential non-technical employees  

AI can change the paradigm and deliver knowledge to the user as needed, where needed. End-users can ask questions and get answers in plain language, as well as receive video or graphical content in addition to written responses. Users can get the self-help they want in the context of the work they are doing, without going to a destination portal. This capability greatly expands the scope of what we know as Level 0 support, which provides answers and solutions for issues that have already been solved and documented.   

Further, since generative AI’s summarization capabilities can produce full and consistent notes on every case, the amount and types of information available as user-facing knowledge will—without extreme effort on the part of support staff—be greatly expanded.  

Another Knowledge Benefit 

Most of us know the difficulties of getting a new employee up to speed when an experienced staff member departs the organization. Again, knowledge bases are often riddled with out-of-date articles and duplication. Generative AI can create accurate summaries of existing articles as well as add current resolutions to the knowledge base so that it is kept current.  

Another benefit for all analysts, but especially newer and less experienced ones, is real-time assistance and coaching. This type of assistance can help ensure both quality and compliance, especially important in organizations in the medical, legal, and financial verticals, where missteps can result in penalties and liability. Time to competency can be reduced, getting newly onboarded staff to work independently more quickly. 

A robot with a computerDescription automatically generated with medium confidence

Note: In the recent past, chatbots were introduced for self-help. Unfortunately, many of these non-AI powered chatbots were incapable of answering questions unless they were asked in a certain way, and they had very limited answers or suggestions. The bots did not meet user expectations. End-users may, therefore, be skeptical of the capabilities of generative AI-powered chatbots until they are proven to be truly helpful, fast, and easy to use. With generative AI and natural language processing (NLP), the new generation of chatbots will be far more useful, but there will still have to be some education and marketing around the new tools

Increased self-help means that less time will be spent repeating answers to end-users, which is the least productive form of KTLO work, and adds little, if any, value for the organization.  

Self-healing is the Next Step 

Many of the unplanned interruptions organizations experience are not due to failures at the endpoint (hard drive or other equipment failures or desktop software) but are the result of network connectivity interruptions or application failures due to lack of adequate storage, failed changes and the like.  AI’s anomaly detection capability can recognize when conditions indicate that a failure is imminent and apply corrective actions to mitigate or eliminate the impact. 

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