Virtual Agents

 

WHAT IS A VIRTUAL AGENT?

A virtual agent is, as described by Chatbots.org, "... a computer generated, animated, artificial intelligence virtual character that serves as an online customer service representative. It leads an intelligent conversation with users, responds to their questions and performs adequate non-verbal behavior."

In other words, if you have ever used online customer support to resolve an issue with your phone bill or chatted with a service desk agent at your job to reset a password, then you most likely interacted with a virtual agent. A shorter definition by Tech Target explains that "a virtual agent is a program based in artificial intelligence (AI) that provides automated customer service.” This concept, however, should not be confused with call center agents that work remotely, who can also be called “virtual agents”.

Essentially, this technology can provide basic information to customers or employees, help guide the users through questions, and automatically reroute complex conversations or issues to an actual human agent if needed.

USES OF A VIRTUAL AGENT

It is no secret that one of the main uses for virtual agents is to automate processes and reduce workload for support agents. Two common places to do this is in customer service and in the IT service desk.

IN CUSTOMER SERVICE

From providing insights about a product order to automatically cancelling a financial transaction and issuing a refund, agents can provide customers with all the information they need so your customer service agents can dedicate more time to work complex issues.

Many retailers like 1-800-Flowers.com are now using virtual bots to assist customers during the buyer’s journey. Through the use of artificial intelligence (AI), which we will discuss later in this blog, the agents can study the customer’s behaviors and dislikes in order to personalize the experience during the buying process. They can then give customers product suggestions or recommendations and assist with tasks, such as we see in the image below.

                         


      

IN THE IT SERVICE DESK

Similar to customer service, virtual agents can be used to help reduce the ticket volume of the IT service desk and help provide support to employees 24/7. They can also facilitate employees with IT services at an enterprise level and to any department within the organization.

For example, let’s say that one of your employees is having issues with their computer audio and uses a virtual agent to solve this issue. The agent should be able to ask basic questions such as “are you using headphones or your computer speakers?” and then determine what to suggest depending on the response. If the employee says that the headphones are the problem, then the agent could potentially send a request order for new headphones automatically without the employee needing to call the service desk.

While these agents can help reduce the workload of support teams, it is essential for them to also enhance the user experience. Let’s look at how AI helps achieve this.



Comments

Post a Comment

Popular posts from this blog

Deep Learning

Image
  Deep Learning in AI Deep learning is based on the branch of machine learning, which is a subset of artificial intelligence. Since neural networks imitate the human brain and so deep learning will do. In deep learning, nothing is programmed explicitly. Basically, it is a machine learning class that makes use of numerous nonlinear processing units so as to perform feature extraction as well as transformation. The output from each preceding layer is taken as input by each one of the successive layers. Deep learning models are capable enough to focus on the accurate features themselves by requiring a little guidance from the programmer and are very helpful in solving out the problem of dimensionality. Deep learning algorithms a re used, especially when we have a huge no of inputs and in the deep learning has been evolved by the machine learning  which itself is a subset of artificial intelligence and as the idea behind the  artificial intelligence  is to mimic the human behavior, so same
Read more

Decision Management in AI

What is AI decision making? AI and decision management systems can help companies in making valid decisions by providing up-to-date and relevant information and performing analytic functions. Companies base their decisions on data available from management information systems as they reflect information that comes from the operations of their company. Combining AI and decision management systems can take decision-making to different levels. AI’s capabilities help these decision management systems in translating customer data into predictive models of key trends. This has helped marketing and consumer departments in customizing their efforts according to key demographics. AI AND DECISION MANAGEMENT: BENEFITS  Since its beginning, AI has played a revolutionary role in automating both knowledge based and non-knowledge based activities. Today, a huge amount of data is used for generating various models on a weekly basis or even on a daily basis, that help an organization in making decision
Read more

Expert Systems in AI

Image
  What is an Expert System? An expert system is a computer program that is designed to solve complex problems and to provide decision-making ability like a human expert. It performs this by extracting knowledge from its knowledge base using the reasoning and inference rules according to the user queries. The expert system is a part of AI, and the first ES was developed in the year 1970, which was the first successful approach of artificial intelligence. It solves the most complex issue as an expert by extracting the knowledge stored in its knowledge base. The system helps in decision making for complex problems using  both facts and heuristics like a human expert . It is called so because it contains the expert knowledge of a specific domain and can solve any complex problem of that particular domain. These systems are designed for a specific domain, such as  medicine, science,  etc. The performance of an expert system is based on the expert's knowledge stored in its knowledge base
Read more