As businesses strive to improve customer experiences and streamline their operations, many are looking towards artificial intelligence (AI), specifically conversational AI solutions. Because conversational AI leverages natural language processing (NLP), machine learning (ML), and voice recognition technologies, it’s especially powerful for companies that want to adopt proactive customer service. But even for typical customer service calls, advanced voice AI technology can be a gamechanger. Voice AI bots can provide human-like interactions, helping businesses scale operations exponentially.
But not all conversational AI solutions are created equal. Different features and factors can define the success of your AI adoption. Let’s look at these in two parts – information processing and workflow integration.
Start with language understanding and dialogue
The fundamental features that can make or break a voice AI solution is how the machine processes and responds to information, which in this case, is spoken language. There are multiple factors to look into here, which are listed below.
Natural Language Understanding
Natural Language Understanding (NLU) enables machines to process and comprehend human language and respond appropriately. Without NLU, machine-powered conversations would be limited to predefined answers, almost like recordings made for interactive voice response (IVR). NLU enables conversational AI solutions to accurately interpret the intent behind customer requests, regardless of how they are phrased. NLU capabilities also enable machines to gather insights into customer needs and preferences by analyzing conversations, helping businesses understand their customers better.
Dialogue Management
Dialogue Management enables the smooth and natural flow of conversation between callers and the automated system. This involves keeping track of the conversation’s context and guiding the dialogue towards a resolution. Without effective Dialogue Management, a voice conversation with an AI engine may become disjointed or repetitive; the machine may end up providing unrelated comments or repeating itself unnecessarily.
A well-designed dialogue management enables AI to provide a natural, human-like experience for customers. For example, a customer may start a conversation with a chatbot to report an issue with a product. The bot then guides the customer through a series of questions to determine the root cause of the problem. Once in a while, the customer might interrupt with a seemingly related detail. The bot, if designed with good Dialogue Management like WIZ Talkbots, will be able to determine if this is something to explore further or if it needs to redirect the conversation to the previous point. As the conversation progresses, the bot may suggest a solution or escalate the issue to a human agent for further assistance.
Voice Capabilities
Voice Capabilities refer to a conversational AI system’s ability to accurately recognize and interpret voice input and respond through natural-sounding speech. Well-developed technical features like automatic speech recognition (ASR) and text-to-speech (TTS) language capabilities are critical in this area. High-level voice capabilities are needed to provide a natural and human-like customer experience.
Talk about integration and escalation
The next set of factors to consider affect operations – how well the solution integrates with your existing technology stack, and how it works with humans. Below are some of the operational considerations for selecting a voice AI solution.
Channel Integration
Channel Integration determines where and how your AI solution will be used – the touchpoints that customers will encounter it. In today’s digital age, customers expect to communicate with businesses through a variety of channels, including web, mobile, messaging apps, and voice assistants. A conversational AI solution that is not integrated with one or more of these channels may be challenged to deliver a seamless customer experience. Customers may If handoffs are not easy, customers may abandon interactions instead of completing a transaction. A conversational AI solution that offers a wide range of channel integrations provides customers choice – enabling end-users to go through their preferred channels. Because of these factors, the fewer channels a voice AI solution can accommodate, the potentially less powerful the solution can be.
Back-end Integration
Back-end Integration ensures that the system can seamlessly interact with a company’s exisiting business systems and processes. New solutions are taken up because they promise to make things better – either improve efficiency, save costs, or other optimizations. When a solution can’t mesh well with what’s already there, then the experience becomes more frustrating and troublesome than the original setup.
One of the systems that conversational voice AI may need to integrate with is a customer relationship management (CRM) solution. The new AI solution may need to access the CRM database to initiate customer outreach. Without effective back-end integration, the conversational AI solution may not be able to do what it was designed to do. Robust back-end integration ensures effective and efficient interactions with other business systems and processes, helping things move along smoothly.
Agent Escalation
Agent Escalation is what enables the AI bot to seamlessly transfer a conversation to a human agent when necessary. Conversational AI solutions can handle a wide range of customer requests and inquiries, but there may still be situations where human intervention is required. If the customer’s request is too complex for the system to handle, or if the customer is expressing frustration or dissatisfaction, it may be appropriate to transfer the conversation to a human agent. Effective agent escalation systems ensure that the customer does not have to repeat their request or context to the agent once the call is handed off. A robust hand-off system ensures that the agent taking over has access to all relevant information from the conversation so far. This helps reduce frustration and improve the overall customer experience.
So which conversational AI features are most important?
There are other factors or features that determine the performance of a conversational voice AI solution. The list of features provided here is not exhaustive, but can serve as a quick checklist to see if a product meets your requirements.
To come up with a more structured way of evaluating your options, we suggest taking the above factors and assigning a weightage to them. Then rate the voice AI solution against each factor from a scale of one to four. Multiply each rating against the weightage then add the scores together to get the final rating of the solution being considered. Do this for every option you are considering. Below is our suggested weightage for each of the features or factors discussed, based on how easy it is to get right.
| Feature | Weightage |
| Natural Language Understanding | 20% |
| Dialogue Management | 25% |
| Voice Capabilities | 25% |
| Channel Integration | 15% |
| Back-end Integration | 10% |
| Agent Escalation | 5% |
Bear in mind that each business will have specific priorities when it comes to choosing a voice AI solution. You may want to adjust the weightage according to what you believe to be important in your operations. If you need a solution that can work across all your customer communication channels, then Channel Integration must have a higher weight in your evaluation. If all six factors seem to be equally important, then divide the scoring equally.
By carefully assessing factors that are critical to your business success, you can select a conversational AI solution that offers the right mix of capabilities to meet your requirements. The right conversational AI solution will help you transform your call operations and differentiate your brand from competitors in today’s increasingly digital and customer-focused marketplace.
