What is a Virtual Agent? Example and Applications
Curiosity, fascination, and even controversy about virtual agents are at a fever pitch in today’s modern environment. To start with the basics, a virtual agent is a software program that uses scripted rules and increasingly sophisticated artificial intelligence (AI) capabilities to automate service or offer guidance to a human user.
Technically, a virtual agent is the product of virtual agent technology (VAT)— an advance that integrates natural language processing, intelligent search, and robotic process automation (RPA) into one conversational user interface or platform.
A virtual agent is a quantum leap in Enterprise chatbot technology. The earliest chatbots were once considered almost a punch line—very structured, incapable of abstract understanding, and very literal. They could be extremely frustrating to interact with.
But that was then. Recent advances in conversational AI, speech-to-text capability, optical character recognition (OCR), and sentiment analysis, have enriched virtual agent intelligence so that these tools understand and interpret open-ended user input or “intent.” Conveniently and amazingly, they comprehend a user request and access relevant systems, such as a backend billing infrastructure, to take action and fulfill the user intent.
For years, the requisite machine learning and automation techniques behind a virtual agent were actually available—but they hadn’t yet been assembled into a single, self-sufficient platform. Now, progress in AI has opened the door to language agility, versatility, and productivity once considered science fiction.
Today, virtual agents have taken over many initial customer interactions with call centers and click-to-chat features on websites. They are ideally fitted for routine customer self-service applications, while complex, difficult, or challenging issues can be handed off or “escalated” instantly to a human.
The employee help desk is a natural go-to resource for such mundane but necessary tasks as resetting passwords, changing insurance coverage, answering frequently asked questions (FAQs), or guiding employees through work tasks and company processes. Gartner predicts that, by 2025, 37% of customers will try to use a virtual assistant (or agent) to interact with a customer service department.
Virtual Agent vs Virtual Assistant: Understanding the Differences
There is some overlap among the uses and features of virtual agents, chatbots, and virtual assistants. People tend to use them informally and interchangeably, but there are differences. AI virtual assistants (VAs) leverage AI and semantic natural language understanding technology to comprehend user input, locate resource(s), and respond quickly and accurately. While chatbots deliver the answers they’ve been programmed with, VAs can access multiple systems to answer a user’s question or fulfill their request.
People rely on virtual assistants to perform routine tasks quickly and efficiently. In the United States alone, there are over 110 million virtual assistant users, and Google Assistant now has 500 million users worldwide. Virtual assistants are also gaining in the business world, with applications ranging from benefits management to IT support ticketing.
A virtual agent is a type of virtual assistant that is specially designed to handle customer service interactions—and they’re revolutionizing the way businesses support current and potential customers. A virtual agent solution offers immediate help with some of the most common customer service issues such as resetting a password, checking on an order, or learning warranty coverage. They do this 24/7, without the need for around-the-clock staffing.
A virtual agent uses conversational AI to identify the intent of ‘freeform’ text or speech from users. They can then automate steps to fulfill that intent—and continuously improve their ability to understand it. So while a chatbot can only respond, a virtual agent can understand, learn, and perform.
How Virtual Agents Work: The Mechanism Explained
The human brain is unique in its ability to turn abstract thought and perception into language. Virtual agent technologies are moving closer to that capability. At the most basic level, virtual agent technologies work on a preprogrammed, scripted model. Organizations created virtual agents that were instructed to respond in set ways to specific human requests. They instructed the agent on what to do, based on every request or inquiry made by a human user.
A virtual agent can now take care of most routine tasks addressed by a customer inquiry. They can automatically identify customer sentiment and smoothly transfer conversations to a live agent with conversation logs when needed. This both improves customer satisfaction and raises agent productivity.
The Role of Natural Language Processing in Intelligent Virtual Agent
Virtual agent platforms encompass machine learning technology, natural language processing (NLP), and conversational AI technology to create conversational AI. Intelligent virtual agents surpass the limitations of traditional chatbots by using technology to analyze human interactions and generate responsive text that mimics human conversations. AI-powered is sophisticated enough to deal with complex queries that do not rely on pre-scripted responses and solve requests that fall outside the script.
AI-powered virtual agent platforms, as mentioned above, connect with back-end systems, which access the user’s specific concerns and make personalized responses available to customers or employees.
Most exciting and encouraging, they actually learn as they work, applying new facts, resources, news, actionable data, and customer experiences to address a wider and more demanding range of applications.
Capabilities of Virtual Agents
Bounding ahead of early interactive voice response, or IVR systems, virtual agent software can now employ, and benefit from, fast-evolving advances in AI and cognitive computing programs.
For instance, Generative AI for ServiceNow enhances these capabilities by leveraging cutting-edge generative AI technologies to automate and optimize workflows within the ServiceNow platform. This not only improves the efficiency of service delivery but also enriches the customer and employee experience with more intuitive and personalized interactions.
Steady progress plus technological leaps have given virtual agents the insight and ability to understand customer intent, providing personalized answers in a manner so authentic that users sometimes wonder if the system is conscious. This level of intelligent, intuitive interaction summons comparison with the epochal Turing Test.
At this point, however, virtual agents typically communicate with customers real time via email or live chat on corporate websites. These frequently include a mobile app version. Companies use an avatar, often with a unique corporate nickname and personality, to provide a visual representation of the virtual agent.
This makes it seem even more “alive” and intrigues potential customers enough to serve as a sales resource. The ability to deliver an exceptional customer journey while resolving contact center requests is a powerful benefit. However, even as most companies employ a virtual agent to take over highly repetitive tasks in contact centers, the need for live customer service agents is endured when handling complicated tasks.
Robotic Process Automation in Virtual Agents
Closing the circle around robotic process automation (RPA) and Virtual Agents offers necessary insight into the value of these linked technologies. RPA employs software “robots” to automate digital activities traditionally performed by human workers.
Handing these types of tasks off from human employees, to a team of bots is a breakthrough and a transformation in managing low-value, repetitive jobs. The organization sees results faster, saves costs, and perhaps most importantly, enables employees to focus on higher-value work, while feeling more fulfilled and appreciated—a win/win.
RPA is sometimes conflated with artificial intelligence (AI) itself, but they are quite different. AI combines cognitive automation, machine learning (ML), natural language processing (NLP), reasoning, hypothesis generation, and analysis to create intelligent automation or hyper-automation. The tools can tackle far more complex tasks—even cognitive processes like speech comprehension and responsive communication. Adding natural language processing (NLP) helps attain end-to-end automation for considerably more processes.
Also, RPA is process-driven, while AI is data-driven. RPA bots can follow only defined paths. By contrast, AI bots use machine learning to recognize patterns in data—particularly unstructured data and teach themselves over time.
RPA software tools include the following abilities:
- Low-coding ways to build automation scripts require no developer to configure them;
- Easy integration with enterprise applications
- Orchestration and administration include configuration, monitoring, and security
RPA adds the useful ability to access information through legacy systems and integrate through the front end with applications quickly and simply—a major value. Back-end connections to databases and enterprise web services also support automation. Programming RPA robots to follow specific workflows and rules reduces human error, while RPA’s audit trail simplifies monitoring and quickly resolves arising issues.
The Benefits of a Virtual Agent vs Human Agent
As virtual agents become more sophisticated and ubiquitous, they are substantially improving customer service experiences across industries. According to a 2021 survey by Statista, the chatbot market exceeded $83 million at the end of 2021. Voice-based automation is expected to grow to more than $450 million by the end of 2027.
The benefits of this trend are evident and numerous:
Improved response times and 24/7 availability
An IBM study reveals that nearly 80% of consumers want an “immediate” response to their inquiries. AI-driven virtual agents, needing no breaks or sleep, cut labor costs by reducing reliance on human intervention—leading to as much as a 30% decline in customer support service fees.
Personalized Interactions at scale
An AI-powered virtual agent can analyze nearly unlimited data volumes to inform custom responses. They remember past interactions, recall user preferences, and offer relevant suggestions, enhancing customer loyalty.
Reduced Operational Costs
Virtual agents reduce customer service costs by handling routine inquiries and transactions. As mentioned, this frees up human representatives to address issues needing human judgment and creativity.
Improved Customer Satisfaction
With faster response times, personalized interactions, and reliable service, virtual agents contribute to a higher level of customer satisfaction. Chatbots are expected to facilitate cost savings of many billions of dollars annually in the coming years and increase customer retention and loyalty.
Efficiency in Handling Large Volumes of Queries
Virtual agents can simultaneously handle multiple inquiries, a feat beyond the capacity of human agents. This capacity for scalability not only means that customers are served faster, but it also allows for efficiency during peak times or seasons without additional staffing costs.
Key Use Cases of Virtual Agents
Industry-specific use cases for virtual agents are broad and prove their utility. Intelligent virtual agents offer a span of opportunities to improve the customer experience, internal productivity, and collaboration, including:
Customer service
Virtual agents, in the form of text-driven chatbots or call-based interactive voice response (IVR) systems, work as automated service and customer service representatives across many channels—ranging from smaller websites to massive social media and messaging platforms like Slack and WhatsApp.
Human Resources
Enterprises use virtual agents to provide employees with integrated access to internal platforms and self-service portals and find quick answers to FAQs.
E-commerce and Sales
Virtual agents enhance sales funnels and lead generation by qualifying leads and completing transactions in diverse retail environments.
Workforce Productivity
Virtual agents help boost employee productivity by automating rote tasks and inquiries, streamlining collaboration, workflow automation, and project management. They automate scheduling, manage and transcribe meanings, and optimize workplace communication tools like Slack and Microsoft Teams.
Real-World Examples of Virtual Agents
Virtual agents help with payment collections, cross-selling, upselling, customer retention, and customer acquisition. They can tackle the following:
Surveys and customer feedback. Unlike years ago, human-to-human interaction is no longer necessary for collecting customer feedback; today’s virtual agents can conduct surveys and record responses.
Insurance claims. Virtual insurance agents field basic questions on coverage, policy updates, and filing claims. These types of inquiries are often simple enough to avoid escalation to human agents.
Order fulfillment and management. Virtual assistants respond quickly to requests and customer questions regarding order status, delivery dates, and related concerns that can be addressed without human involvement.
Event scheduling and reservations. This is important in the hospitality industry, where many virtual agents now can handle routine event planning and reservations.
Troubleshooting and technical support. Virtual agents can guide customers or product end users through routine fixes to technical difficulties. This use frees tech professionals to focus on more complex problems.
Future Trends in Virtual Agent Technology
The era of the simplistic bot is over. Innovations have brought the virtual agent into a whole new landscape. Enterprises have increasingly integrated chatbots into their communications process to please customers and grow their base by improving the omnichannel experience across social media platforms and company apps.
Experts anticipate many exciting new virtual assistant trends, such as that nearly a quarter of all browsing sessions will include voice search by the end of 2024. The omnichannel is more important than ever for benefiting from an individual’s preferences. Expect omnichannel capabilities to keep increasing and create a strong, consistent, and seamless customer experience.
Improved and increased self-service on any device
During the pandemic, self-service grew into a major new competitive way to acquire and please customers. Self-service solutions will become increasingly smartphone-centric, and organizations need to ensure that their virtual agent solutions are integrated into the smartphone experience.
Growing trust and reliance on virtual agents
Many consumers are watching for chatbots to become even more accurate and intuitive. Major technologies that will push them forward are artificial intelligence, machine learning and natural language processing. NLP eases and improves customer interaction with virtual agents. With NLP will come trust, increased customer satisfaction, enthusiasm—and revenue.
Increased capabilities to individualize the customer experience
Customer service teams always seek to further improve customer satisfaction through personalization. Data integration and the increasing accuracy of customer profiles and past choices allow chatbots to access high-value intelligence on a customer’s brand journey.
Emotional intelligence
This will continue to be a priority based on user text or voice inputs. Enhanced emotional intelligence vastly improves the quality of interactions, making them feel less transactional and more human-like. This includes virtual agents that can communicate in multiple languages smoothly and easily understandably, providing an optimal customer experience.
Integration with augmented and virtual reality (AR/VR)
Advances in this integration can revolutionize the digital customer experience. Integrating these technologies allows businesses to create increasingly immersive and interactive customer experiences. A customer could potentially use a virtual reality headset to enter a virtual store; a virtual agent could guide them through the shopping experience and offer personalized recommendations.
Predictive capabilities
Future virtual agents will learn even more precisely to anticipate customer needs and provide automated service based on previous interactions and behavior patterns. As we move through 2024, these emerging trends will shape the architecture and direction of virtual agents.
Conclusion
By staying ahead of these trends, businesses can maximize the power and potential of virtual agents to improve customer and employee experiences. That means optimizing operational efficiency, driving revenue, boosting competitive strength, and achieving growth.
The rise of virtual agents has dramatically reshaped customer service, modifying an innovative era where AI-driven customer interactions are the expected means of communication. Virtual assistants are major influences on the modern business world. These constantly evolving tools can ensure that a business placed in the right position can save time, money, and effort.
Emerging trends point to a future of even more powerful, capable, and human-like virtual agents. Book a custom AI demo to explore the power of Aisera’s Enterprise AI Copilot.