You've likely encountered virtual agents as a consumer (knowingly or unknowingly), but their applications within enterprise environments go far beyond run-of-the-mill customer service agent chatbots.
By 2027, virtual agents that use AI to support users via chat will become the primary customer service channel for about 25% of organizations. Given that businesses using them reportedly see average cost savings of up to 30% in support operations and substantially faster resolution times, the quick rise of AI-powered virtual agents is no surprise.
If your teams aren’t leveraging virtual agents for support, you could be at a competitive disadvantage.
Today's sophisticated virtual agents can completely elevate how organizations handle IT support, HR inquiries, and employee services, creating more efficient workflows while helping to dramatically reduce operational resourcing.
But what exactly is a virtual agent? How does it use artificial intelligence and how does it differ from the simple chatbots you may be familiar with? And perhaps most importantly, how can your organization successfully deploy this technology to drive real business value?
Let's answer these questions and see how AI-powered automation is reshaping enterprise support.
What is a virtual agent?
A virtual agent is an AI-powered software program designed to interact with users in a conversational manner. It can provide real-time assistance, answer questions, and perform tasks, typically through chat interfaces, messaging platforms, or voice-activated devices
It can also handle a variety of tasks: answering questions, offering technical support, and even performing transactions, all to boost your support efficiency and greatly enhance your user experience.
Advanced virtual agents do way more than just respond to questions, though.
They proactively solve problems by connecting with other systems and automating complex workflows. Using natural language understanding (NLU), machine learning, and process automation, virtual agents can understand user requests, access relevant information, and take action without intervention from human agents.
Unlike traditional support resources like static FAQ pages or knowledge bases, virtual agents are able to have conversations with users and clarify understanding.
They ask follow-up questions when needed and can tailor responses based on the user's role, permissions, and history. This makes it easier and more intuitive to access information and services across the organization.
They can also use agentic AI to take independent actions and complete tasks like software provisioning, room booking, or IT troubleshooting on their own, helping to greatly reduce support workloads.
Virtual agent vs. chatbot: What's the difference?
While people often use the terms interchangeably, there are distinct differences between virtual agents and chatbots, largely due to their levels of technological sophistication:
Chatbots generally follow predefined scripts and decision trees, recognizing specific keywords or phrases and responding with templated answers.
While useful for simple interactions, traditional chatbots aren't advanced enough to understand context, learn from past interactions, or take autonomous actions beyond their programming.
For example, a standard IT help desk chatbot might provide a link to password reset instructions when it detects the phrase "forgot password." However, it can't actually verify the user's identity and reset the password on its own.
Virtual agents typically use more advanced AI and NLP to understand the context and intent behind user questions, regardless of phrasing. They can also connect with different backend systems to share even more information.
Let’s jump back to our password reset example. In this scenario, an advanced agentic virtual agent would recognize and understand the request, then verify the user's identity through integrated security protocols.
Then, it would reset the password according to your IT password policies, and communicate the new credentials and next steps securely — all without a human team member ever lifting a finger.
Virtual agent vs. virtual assistant
Both virtual agents and virtual assistants use conversational AI interfaces. However, virtual assistants like Siri or Alexa aim to help individuals stay productive, while enterprise oriented virtual agents focus on meeting your organizational needs at scale, working across different departments and systems.
To put this into context, virtual assistants are great for personal tasks like setting reminders or answering general questions like, "How's the weather," but most are built for personal (rather than professional!) productivity.
Virtual agents, however, are built for business. They can handle things like routing IT support tickets or answering HR and benefits questions from employees.
Virtual agent vs. AI agent
And then there are AI agents, which are the next step up in the evolution of autonomous systems.
Unlike virtual agents (which focus on responding to human requests through chat or voice interfaces), AI agents are capable of acting on their own, making decisions and taking actions without human prompting along the way.
For instance, Vituity uses AI agents to take care of IT and HR support for physicians, cutting help desk workload by 40% and solving issues instantly. By moving from reactive to proactive automation with AI agents, organizations can avoid delays, streamline workflows, and boost overall efficiency.
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Virtual agents in ServiceNow
It's common for organizations to get introduced to virtual agents through ServiceNow’s IT self-service management product known as ‘Virtual Agent’ (Maybe that's even how you became familiar with them!)
Virtual Agent is primarily used for IT service management (ITSM) workflows. It is built for the ServiceNow ecosystem it often requires integrations and technical know-how when tackling needs across other enterprise apps.
How virtual agents work: AI, NLU, and automation in action
Now that you have a feel for what a virtual agent is, let’s walk through exactly how they work. Virtual agents use several different components to deliver intelligent, automated support:
- Natural language understanding (NLU) allows virtual agents to interpret user inputs and requests expressed in everyday language, pulling out the key information and intent even when worded in different ways.
For example, the phrases "I can't log in," "password not working," and "locked out of my account" could all route to the same resolution.
- Machine learning lets virtual agents improve over time by analyzing patterns in customer interactions. By recognizing common issues and repeated, successful resolutions, the system continuously refines its responses and recommendations.
- Robotic process automation (RPA) provides the "hands" that let virtual agents perform actions across different systems. When integrated with other business applications like ITSM platforms, HR systems, and knowledge bases, this automation offers end-to-end resolution across systems without human intervention.
- Integrating with systems and services: The best enterprise virtual agents break through platform limits by offering a single, unified interface for employee support, no matter where the information or tools may be located.
In the same vein, enterprise integrations link virtual agents to the systems where work gets done and information lives. With the right integrations, a basic Q&A bot can evolve into a powerful automation tool that can access data across the organization.
Consider this example: A Fortune 500 company in the software space uses the Moveworks AI Assistant to streamline its IT support.
With Moveworks' Reasoning Engine, this AI Assistant can handle almost any question an employee sends via Slack or email. It can provision software, share policy updates, take action on tickets, update permissions, request or fix hardware, look up people or places, pull knowledge base articles, and more.
Even better? The entire workflow for a request can take place in less than 60 seconds.
Examples of virtual agents
Wondering how else virtual agents can elevate your support ops? Let’s examine some high-impact applications for agent support across multiple enterprise functions:
- Customer support: Virtual agents can handle common customer inquiries 24/7, providing instant answers to product questions, order status updates, and troubleshooting guidance without wait times or human involvement.
- ITSM: In ITSM, virtual agents can automate common requests like password resets, software provisioning, and system status updates, freeing up technical staff for more complex issues and high-priority projects while providing faster resolution for employees.
- HR: A human resources department might employ virtual agents to assist with benefits questions, policy clarifications, and common processes like time-off requests or updating personal information.
- Finance: Finance teams can use virtual agents to automate expense approval processes, budget inquiries, and procurement processes, which has the dual benefit of improving compliance while reducing processing times.
Key benefits of virtual agents for enterprises
While process automation across customer support, ITSM, HR, and finance can significantly improve your employee and user experiences, it’s just one of many benefits virtual agents bring to an enterprise. Here are some others:
Lower costs & faster resolutions
Intelligent virtual agents can handle routine questions without human help.
If your IT team no longer has to field thousands of password reset requests manually, they have bandwidth for pressing issues. Fewer tickets generally require fewer resources, meaning you can keep the headcount flat without sacrificing support quality or speed.
24/7 availability
Unlike human support teams, virtual agents provide consistent service around the clock without breaks, vacations, or shift changes. This continuous availability is especially valuable for global businesses supporting employees across different time zones.
Mercari US teamed up with Moveworks to bring its AI-powered assistant into Slack, making IT support faster and easier. Their AI Assistnat uses advancements in NLU and natural language processing (NLP) to handle over 74% of employee issues on its own — everything from password resets to troubleshooting.
With 94% of employees turning to the assistant, there’s a huge load off the IT team’s shoulders. Response times decreased and allowed Mercari’s support to grow without sacrificing quality.
Data-driven insights for continuous improvement
Virtual agents generate valuable analytics about common issues, resolution paths, and user/customer satisfaction. These insights help organizations spot recurring problems, knowledge gaps, and optimization opportunities that might otherwise remain hidden.
For example, take Albemarle, a global chemical company. Ablemarle uses Moveworks AI Assistant to identify inefficiencies and provide actionable insights to improve employee experiences.
The result is an innovative solution that has transformed Albemarle’s support processes, delivering a 2x more productive and data-driven workplace.
How to implement a virtual agent in your organization
You’re convinced: Virtual agents can give your business a competitive edge, and you’re ready to bake them into your processes. However, successful virtual agent implementation takes thoughtful planning and execution.
Here’s what to do to make sure your virtual agents will serve your business the way you want them to:
1. Identify the right use cases for your business
Start by looking at your support data to spot high-volume, repetitive requests that have simple, consistent solutions. These are the "low-hanging fruits" where virtual agents can make an immediate impact:
- Password resets and account unlocks
- Software access and setup
- Questions about policies and procedures
- Common HR benefits inquiries
- System status updates
At this beginning stage, you'll want to work closely with IT, HR, and other support teams to align with business goals and set clear success metrics.
Getting early buy-in from these teams is a key step in implementing and adopting virtual agents effectively (and company-wide).
2. Prepare your data and optimize your knowledge bases
Your virtual agent’s effectiveness can depend on the quality and structure of your knowledge resources. Bad data in means bad data out.
Before implementation, review and update your knowledge bases, making certain that the information is:
- Accurate and current
- Written in clear, concise language
- Properly tagged with relevant keywords
- Structured for easy retrieval
Funnily enough, you may even discover that there are significant gaps or inconsistencies in your employee knowledge management during this process — which is a prime opportunity to improve information quality for both virtual and human agents.
3. Choose the right virtual agent solution
What works for your competitors may not be what’s right for your organization. When evaluating virtual agent platforms, consider these key capabilities:
- Enterprise integration: The solution should easily connect with your existing systems — ITSM platforms, HRIS, communication tools, and knowledge bases — without requiring extensive custom development.
- Conversational intelligence: Look for advanced NLU features that can pick up on natural language differences, handle complex requests, and keep track of context and question intent during interactions.
- Automation capabilities: As we've discussed, the most effective solutions go beyond answering questions to actually resolving issues through integrated automation.
- Scalability: Wherever possible, try and find a solution that can grow with your organization and extend to new use cases over time.
The Moveworks AI Assistant stands out in these areas by offering pre-built integrations with leading enterprise platforms, sophisticated language understanding powered by its Reasoning Engine, and proven scalability across global organizations.
4. Train & optimize your virtual agent over time
Rolling out a virtual agent isn’t a "set and forget" project. Plan for ongoing optimization through:
- Regular analysis of user interactions to identify improvement opportunities
- Continuous knowledge base updates to address new questions
- Periodic review of automation workflows to identify any opportunities for expansion
- End-user satisfaction monitoring to ensure the virtual agent is meeting, if not exceeding expectations
Smarter virtual agents: What's next for AI-powered automation?
It's easy to see that virtual agents are becoming smarter and more sophisticated. But what's next for AI-powered automation?
Autonomous AI agents
With agentic AI, virtual agents have started to transition away from responding to requests and more towards proactively solving problems and optimizing workflows.
While traditional AI models can be powerful within their narrow domains (like data analysis and content generation), agentic AI goes beyond by providing a higher level of flexibility, actionability, and scalability in diverse applications.
When properly implemented, it can help automate routine tasks and simplify certain processes with zero human intervention.
These systems are able to handle entire workflows while still sticking to the right rules and staying under proper supervision. This shift marks a big change in how we view workplace automation — moving from tools that simply help us out to smart systems that act more like thoughtful digital coworkers.
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Virtual agents in enterprise AI strategies
By providing an intuitive messaging interface to complex business systems, virtual agents are making enterprise technology more accessible while dramatically improving operational efficiency.
The most successful implementations take a holistic approach, considering how virtual agents fit within the broader employee experience and contact center ecosystem.
Rather than creating yet another siloed tool, leading organizations are putting support platforms in place that connect employees to information and services across the enterprise.
The Moveworks AI Assistant takes this approach head-on, providing powerful agentic AI assistant to find answers and automate tasks across all enterprise applications. Available where employees already work — like Slack or Teams— it delivers 24/7 personalized support in 100+ languages.
By supercharging everyone's capacity to get work done, AI-powered virtual agents reduce costs while transforming how employees interact with other systems and access support services.
The result? A happier, more productive support team that can focus on meaningful work instead of getting bogged down by repetitive hassles.
Discover the 4 Big Shifts IT Teams are Making with AI in 2025.
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