Conversational AI Examples & Use Cases for Modern IT Support

Conversational AI Examples & Use Cases for Modern IT Support

Conversational AI Examples & Use Cases for Modern IT Support

Conversational AI Examples & Use Cases for Modern IT Support

Unlike traditional chatbots operating on scripts and canned answers, conversational AI advances IT service support. 

Conversational AI bots are AI-powered chatbots that provide users with an enhanced, human-like experience. They use deep learning, machine learning (ML), and natural language processing (NLP) to understand the problem better and provide a personalized experience. 

The most common example of conversational AI is Google Assistant or Siri, which assists you with complex queries without human intervention. It asks follow-up questions, understands user sentiment, and identifies urgency to provide effective solutions.

This post explores conversational AI use cases and examples for IT support. It shows how AI-powered chatbots help IT teams improve user experience, productivity, and engagement. 

Conversational AI Use Cases in IT Support

A traditional chatbot can resolve mundane tasks like password reset, basic troubleshooting, accessing the knowledge base, etc. 

However, modern IT support issues require contextual clarity, interaction with multiple systems, and personalization. So conversational AI platforms surpass traditional bots.

For example, let's say there is a Wi-Fi connectivity issue. A traditional chatbot provides basic suggestions like restarting the laptop and router. Contrarily, a conversational AI inquires about specific error messages or recent network changes to provide personalized assistance.

Here are some conversational AI use cases with examples to understand its capabilities:

Knowledge Management 

IT support uses static repositories, such as knowledge bases or documentation, where users must manually search for solutions. This approach is both time-consuming and frustrating for users and support agents. Not only is it difficult to find a solution, but also challenging to update this information. 

Contrarily, conversational AI employs ML algorithms to understand user queries and provide personalized solutions. Instead of rigidly following predefined paths, it analyzes vast repositories of up-to-date information, including real-time data feeds and internal databases. 

You can define the trusted sources to ensure the accuracy of the information and even manually upload as many data sources as possible. The model is capable of learning from not just data sources but also the conversations to create an agile knowledge base

For instance, a user may ask, “How do I configure my email settings for Outlook?” The AI provides step-by-step instructions and considers past interactions for contextual understanding. It may ask clarifying questions about the specific version of Outlook or any unique preferences to provide a tailored solution that meets the user's needs.

Workload Optimization 

Agents manually review many tickets, making it difficult to pinpoint which tasks are important. Often, they get stuck in mundane, repetitive tasks. Agents receive some assistance with traditional bots but don't get as much value due to the lack of personalization and NLP. 

However, conversational AI doesn't rely on specific keywords or rigid scripts. It uses NLP along with conversational metrics such as sentiment spectrum, tone assessment, and urgency identifier to provide effective problem triage and issue prioritization. 

Moreover, when multiple tickets are present, it identifies commonalities and group-related issues and assigns tasks to appropriate agents. 

Intelligent Ticket Triage 

Whenever a ticket is raised, agents must manually categorize, prioritize, and assign the right resources. A traditional bot only assists with basic information, after which users must go to a different platform (emails, website, etc.) to raise tickets. This leaves agents with even more tickets.

Conversational AI helps you promptly manage tickets. With its NLP capabilities, it analyzes the tickets, categorizes and prioritizes them, and assigns them to relevant agents. Moreover, it asks follow-up questions and interacts with the user to get more information for prompt resolution and accurate categorization. 

For instance, a system update triggers multiple tickets from different departments. The conversational AI analyzes and gathers more information to categorize and prioritize tickets based on severity. This helps IT to resolve high-priority issues promptly.

Incident Identification and Management

For prompt incident management, IT teams proactively analyze the system and detect issues to minimize downtime and reduce potential disruptions. Neither is it possible to monitor systems 24/7, nor is it productive to manually analyze logs, correlate data, and identify root causes. 

Conversational AI uses NLP and advanced analytics capabilities to interpret user inquiries. It analyzes incoming alerts, correlates data, and detects patterns to accurately triage incidents and prioritize them based on their impact and severity. This ensures that incidents are addressed in a timely manner to avoid potential issues.

For example, during a server outage, conversational AI automatically detects the incident, assesses its severity, and notifies the appropriate support team. It also provides contextual information about previous similar incidents, enabling faster resolution.

Conversational AI guides support agents by recommending relevant solutions or escalation paths and tracking incident resolution progress. This enhances support teams' productivity and reduces mean time to repair (MTTR).

Invisible Ticketing System 

Traditional IT support uses chatbots and a dedicated platform for raising tickets. However, users must accurately describe their issues with specific keywords to get assistance from the chatbots. They can email or raise a ticket to resolve the issues leading to delays and workflow disruptions. 

Contrarily, agents must manually review the tickets to prioritize and address incoming requests. They must also go through emails and ensure additional information is added to the ticket.

Conversational AI transforms your service desk with the concept of an invisible ticketing system. Instead of relying on users to create tickets through a dedicated interface, AI enables users to interact through chat to solve their issues or raise tickets if required.

This conversational approach simplifies the user experience and also ensures that tickets are captured accurately with complete information.

For instance, a user is having difficulty accessing the company VPN from their home. The AI analyzes the issue and provides troubleshooting steps. If successful, the user's query is resolved. However, if they need further assistance, the AI can raise a ticket within the platform, capturing all relevant details without interrupting the user's interaction.

Suggest Reads: How Investors Community Bank used AI to auto-resolve 34% of tickets within three months. 

Personalized Conversations

Traditional IT support that relies on basic chatbots is impersonal and insufficient. Users struggle to find solutions as the bot depends on scripts and keywords, leading to frustration and delays.

Conversational AI uses NLP to understand the nuances of user queries and respond in a human-like manner. Users express their concerns in their own words. AI uses conversational metrics such as sentiment analysis, tone assessment, and urgency identification to provide personalized solutions. 

For example, users encounter repetitive issues while configuring a new software tool. Instead of sticking to a static knowledge base or providing a generic support ticket, AI asks questions to understand their specific requirements and previous troubleshooting attempts. 

Based on the inputs, it provides personalized solutions, including step-by-step instructions or links to relevant resources.

Take IT Support up a Notch With

We have long aimed to automate IT support, but the existing chatbots are not your best bet. They are insufficient for complex issues, and the effort required to implement them isn’t worth it. 

With conversational AI tools like, users can resolve their issues by talking to an AI-powered chatbot within Microsoft Teams. They can provide as much or as little detail as they want, and the AI provides a relevant solution. With virtual assistant, you solve 60% of L1 issues without human intervention.  

Your IT team gets a GenAI sidekick available 24/7 to solve user queries and simplify incident management.  


What is an example of conversational AI?

One of the most common examples of conversational AI is Google Assistant. You can use it to schedule a meeting just by using a command. It understands natural language commands, retrieves your calendar, suggests available time slots, and confirms the appointment. All this happens through a conversational dialogue without requiring you to hop between menus or interfaces.

What is conversational AI also known as?

Conversational AI is also an AI-powered chatbot, virtual assistant, or conversational agent. It uses NLP and other advanced models to create human-like conversations, assist users with tasks, answer questions, and provide information.

What are the benefits of conversational AI?

Conversational AI offers several benefits, including personalized assistance 24/7 for enhanced user engagement and streamlined customer support. It helps companies automate mundane tasks and interact with users in natural language to solve up to 60% of L1 queries without human intervention and a satisfactory user experience.

What is the difference between conversational AI and chatbots?

A chatbot uses rigid scripts and predefined rules to solve queries. In contrast, conversational AI employs advanced algorithms to comprehend better and respond to a wide range of user inputs in a human-like manner.

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