Chatbots vs. virtual assistants vs.
AI agents in ITSM

AI-powered interactions have now become common in our lives. From food delivery apps and e-commerce platforms to workplace IT support, there is a high probability that you have interacted with an AI-powered interface to help you out. Now, there arises a question. What do we call the AI-powered interface (or chat window) that pops up? A chatbot? A virtual assistant? Or the latest buzzword, AI agents?

The answer is: it depends.

Within ITSM parlance, these terms are now being used interchangeably, but each of them are different in terms of the underlying technology and the challenges they solve. Let’s break down what sets them apart and how they function within ITSM.

Chatbots: The digital front-line support agent

Chatbots are software programs that are predefined with specific conditions, conversation flows, and decision trees. They simulate conversational exchanges based on the users' responses, provided those responses are built into the predefined rules or flows within the chatbot. IT service desks typically deploy a chatbot for FAQs, password resets, ticket creation, and basic troubleshooting.

For example, let's say Santhosh, an employee of Zylker, struggles with VPN connectivity. Instead of reaching out to IT support, he turns to Zylkbot, the chatbot integrated into Zylker’s self-service portal. Zylkbot provides clear, step-by-step guidance, helping John resolve the issue on his own. This removes the necessity for human involvement in basic support tasks.

Limitations of chatbots

• They cannot complete tasks that are not pre-defined or autonomously tackle multi-step problems.
• They lack the ability to personalize interactions; they don’t learn from previous conversations, so they often fall short in delivering tailored or context-sensitive help.

Virtual assistant: A more intelligent personal assistant

Virtual assistants are a step up from the typical chatbots. They can grasp natural language, which makes conversations feel much more fluid and relevant. Instead of merely answering basic queries, they connect with IT systems to handle more sophisticated tasks.

Unlike traditional rule-based chatbots that follow predefined scripts, virtual assistants leverage natural language processing (NLP), intelligent search, and robotic process automation (RPA) to deliver more dynamic, context-aware interactions. This allows them to handle a broader range of requests and deliver answers that are relevant and helpful. Businesses are using virtual assistants for IT support tasks like reporting incidents, generating reports, analyzing trends, and detecting anomalies—lightening the load for human support agents.

Imagine you're an IT service desk manager who needs to track key metrics and generate reports often. Instead of manually sifting through data, a virtual agent can handle it for you. Simply ask, "Show me support tickets by channel this month," and the virtual assistant will instantly generate a bar chart, giving you a clear visual breakdown.

Limitations of virtual assistants

• Unlike advanced AI solutions, virtual assistants still rely on users to initiate commands and interactions, limiting their ability to operate autonomously.
• Their NLP capabilities—while being more advanced than those of chatbots—still do not discern the user's actual intent.

AI agents: Autonomous and decision-driven

The launch of ChatGPT has led to a surge in the use of GenAI within ITSM. Companies have started using GenAI capabilities to take care of tasks like summarizing incident resolutions, conducting post-incident reviews, and generating content for IT support responses. But now, we’re entering a new era in AI’s journey within ITSM, one that features AI agents with agentic capabilities.

What are AI agents?

An AI agent is an intelligent model that can detect a user's intent from a ticket, via email, or through conversations and autonomously gather contextual data, make decisions, and perform tasks. AI agents can be deployed for specific purposes like hardware troubleshooting agents, software provisioning agents, password reset agents, etc.

These AI agents perceive their environment, reason out situations to understand the intent of the user, and then plan a set of tasks to fulfill their goals. By using advanced learning techniques like reinforcement learning, they continuously refine their decision-making, adapt to new scenarios, and optimize workflows for better efficiency.

AI agents become significantly more powerful when they collaborate with other AI agents that are specialized in specific tasks. By working together, these intelligent agents create what’s known as an agentic AI system—a dynamic network that:

• Delegates tasks efficiently among specialized AI agents
• Adapts strategies in response to real-time data
• Streamlines workflows with minimal human input
• Operates with a high level of autonomy
• Continuously improves through experiential learning

For example, let's say a large IT service organization manages a vast inventory of hardware and software assets. To ensure uninterrupted operations, critical assets like laptops, servers, and software licenses must be replenished when their stock drops below a predefined threshold.

An AI-driven procurement agent can automate asset replenishment by continuously monitoring stock levels and initiating purchase requests when necessary. This procurement agent:

• Integrates with asset management systems to track inventory levels continuously.
• Triggers alerts when stock drops below a predefined limit.
• Evaluates vendors based on price, delivery time, and past performance.
• Generates and submits procurement requests based on predefined policies.
• Ensures purchases align with budget constraints and policies.
• Monitors order progress and provides real-time updates.
• Analyzes past procurement trends to optimize future decisions.

Here are a few more use cases where AI agents can be impactful

• AI agents accelerate incident responses by automating the diagnosis, resolution, and prevention of issues. They dig through system logs, network traffic, and user reports to find the root cause and take action to minimize downtime. They look at historical data and real-time activity to see patterns that could indicate an incident and take action to prevent disruptions before they occur.
• To keep IT service operations running smoothly, AI agents can evaluate the risk and impact of changes before they are deployed. They can also check system functionality after deployment to ensure stability and prevent unexpected failures.
• AI agents boost user experiences by analyzing user behaviors, preferences, and previous interactions. They suggest services that fit just right, gauge the mood in conversations, and fine-tune their responses based on what users say, making support feel more intuitive and responsive.
• When it comes to preventing asset failures, AI agents monitor system performance data and plan maintenance ahead of time. By anticipating software hiccups or wear and tear to hardware before they happen, AI agents help reduce service interruptions and prolong the life of IT assets.

The greatest advantage of AI agents in all of these scenarios is their ability to reason and take action without a human providing step-by-step guidance. In the absence of AI agents, human technicians would have had to expend a lot of time and energy performing these work-intensive tasks.

Limitations of AI agents

AI agents are only as good as the data that they are trained on. In the case of AI agents for IT service management, if the underlying ticketing data is incomplete, outdated, or biased, the AI agent’s decisions and responses can be flawed. This can lead to inaccurate recommendations, unfair outcomes, or even security risks if the AI cannot adapt to new, unforeseen situations.

What does your IT service desk need? A chatbot, a virtual assistant, or an AI agent?

The table below breaks down the differences between chatbots, virtual assistants, and AI agents.

The right choice depends on your IT needs. If your IT team handles a lot of FAQs, then a chatbot would suffice to answer basic support queries. But for repetitive, yet work-intensive processes that eat up your technicians' productivity—like employee onboarding and offboarding—AI agents come in handy.

This doesn't mean that organizations will completely replace chatbots with AI agents. Instead, IT teams will choose the right capabilities based on the applicable use cases, implementation complexity, and cost-benefit trade-offs. Rather than making a full transition, businesses will blend chatbots, virtual assistants, and AI agents to strike the right balance between automation, cost, and efficiency.