Leveraging AI Assistants

The Age of the Digital Assistant

We’ve all heard of a virtual assistant – someone who works on tasks from a remote setting and completes their tasks virtually, from a computer in another location. However, with the integration of AI and Machine Learning, we now have functions built into software that provides  assistance via a virtual AI agent. This form of virtual assistant does not entail a live physical body working from another computer at all. Actually it is transforming the way we work. Virtual assistants are becoming mainstream, just think Alexa, Cortana, or Siri.

What we have seen transform in technical support, customer service and contact center software as a whole is phenomenal. We now have chatbots and conversational agents(CA) that can assist a customer directly, satisfying the customer with quick answers to common issues and providing service delivery updates. This saves the human agents time that can be better applied to further assisting other customers. These virtual assistants can also aid a human agent by offering suggested resolutions from a knowledge base that retains and matches common symptoms to the best remedy. These improvements in the technology result in more efficiently handled workloads when communications are fed through the latest AI, machine learning and dialogue systems. There are two different forms of virtual assistants, chatbots and conversational agents.

How Chatbots and Conversational Agents Work

Chatbots and conversational agents differ in how they communicate. For example, the chatbot can take text that has been entered by the customer and use those keywords to match to a question in the FAQs. Then, it returns the the answer to the question. This appears as if the user is having a discussion with a person that has lightning quick, accurate response time. A conversational agent utilizes a dialogue system, which uses an input recognizer/decoder with Automatic Speech Recognition(ASR) that transfers the sound from analog to digital. The digital version converts the speech to text, which is determined using Natural Language Understanding(NLU). The semantic information is then analyzed by a dialog manager, which keeps a history of dialog and manages the general flow of the conversation. The dialog manager then uses an output generator such as a natural language generator and finally renders the output through a text-to-speech engine(TTS), talking head, robot or avatar. If the conversation is text-only like in a chat, the first step involving the ASR is not applied. However, the dialog still flows through the other steps and that is what makes the conversation appear more genuine than a basic chatbot. Conversational agents recognize speech, understand the language and establish dialog with the end user. Hence, you are using your voice to communicate with the robot and the interaction mimics a human conversation.  

Leveraging AI to Improve Customer Satisfaction

Now that we have an understanding of how AI can be used to provide us with digital assistants, let’s discuss the opportunities available to leverage this new technology. AI agents can be applied to improve both customer experience and agent experience. It is said that 81% of customers try to resolve their issues without contacting support. I can relate, and I think we would all like to fix things ourselves quickly when having technical difficulties so we can get back to work. Providing the customer with knowledge and steps toward resolution in a timely manner is the best customer service. In most cases, when you call support, you can expect to be on hold awaiting someone to assist for some time, depending on volume of calls. We may opt to just send an email and await feedback but that downtime can really set us back. So we prefer quick and easy resolutions to technical difficulties. That is where a knowledge base that can be quickly scanned by a bot to provide us direction is such a great feature! With this feature, customer issues can be resolved while on hold by interacting through an AI powered IVR(Interactive Voice Response) system. The customer can also visit the website and solve their problem in a quick chat using an AI powered chatbot. These methods provide an easy to use option for the customer that saves them valuable time and efforts. 

 

Leveraging AI to Improve the Agent Experience

As we mentioned above, the best way to speedy resolution for the customer is to reach an agent that has knowledge and provides easy to follow steps to resolution. Again, a quick scan of the knowledge base by a bot using an algorithm can produce the suggestion. In this case, the AI agent can quickly aid the human agent in finding the best solution for the customer. With machine learning applied, these virtual assistants can dig into deep learning capabilities and quickly draw reference points from data already gained from previous interactions with customers, resulting in higher probability of correct answers and a faster response, leading to higher first call resolutions(FCR) and lower agent handling time(AHT). Utilizing predictive analysis to analyze large data sets and using predictive modeling to take a deeper dive into the issues reduces the time agents spend researching the issue to provide an accurate resolution. This also provides coaching opportunities and learning capabilities for the agents to better understand common issues, building their confidence while in production. ROI is ongoing as AI adds data to its algorithms, continuous machine learning expands the AI knowledge base and common issues and best resolutions are stored into the database. 

The use of AI and machine learning has been around for quite some time. However, it is just becoming the mainstream method of improving the lives of both the customer and the agents as it is implemented into our daily tasks. The benefits of using AI for your contact center operations has exponential value as its usage is further integrated into your daily business operations. With the latest technologies, we can empower the agents and better assist customers, improving the overall experience for both users.