By Lianne Dehaye, Senior Director, TDCX AI

Picture this: a new customer agent is on the phone with a potential buyer. As the issue is being described, the agent realizes that the issue is complex. Struggling to ask the right questions, they eventually pass the call to a senior agent. While a resolution is finally provided, it would not be surprising if the customer is already frustrated and formed a negative impression of your brand.

This scenario reflects one of the many reasons for incorporating Conversational AI — to improve the efficiency, productivity, and consistency in operations while not losing the human connection factor. It makes great sense as Conversational AI can analyze and understand linguistic patterns to generate humanlike responses by learning from large datasets of conversations. While Conversational AI is promising, the technology itself is complex and can be overwhelming to implement on your own as there are many factors to juggle. As an experienced CX services provider with a team of experts specializing in Generative AI, machine learning, and robotic process automation, these are our findings to help you make an informed decision as you venture into Conversational AI.

• Data Quantity
  For Conversational AI to achieve humanlike communication, the system needs to be trained on large amounts of data such as text and speech. Therefore, it is important to know if your collected data is enough for training the AI model or if a new data collection strategy is needed. When adequate data is available to train the AI model, we use the Reinforcement Learning from Human Feedback (RLHF) method. RLHF involves training the AI model with direct human feedback that teaches it to understand human language and nuances while reinforcement learning further optimizes the performance of Conversational AI. 
• Quality of data 
  It is often said that “garbage in, garbage out” which simply means that if you feed the AI poor data, it will learn to produce unsatisfactory output. This is often due to poor data organization —  inconsistent labeling on the same dataset, duplication of data, or missing details. Invalid data is another issue when data is not updated until utilized. Our experts will validate the effectiveness of existing data and improve the quality with data labeling and data visualization techniques. It is also vital to remember that data is not set in stone as customer behaviors evolve and your AI model needs to be constantly retrained for consistent performance. 
• Your AI readiness 
  Implementing AI is not as easy as installing a plug-in and assessing readiness is a vital step to get started. It requires the active involvement of senior leaders to develop action plans that ensure effective deployment. We will carry out an assessment for your company that includes a comprehensive benchmarking approach to assess capabilities across all dimensions of your business. This benchmarking provides insight into your company’s current temperature to adopt AI, improvement opportunities as well as the most suitable initiatives for each use case.  
• Best use of Conversational AI 
  There are different types of Conversational AI but it doesn’t mean that you will benefit from having all of it in your arsenal. It depends on the organization’s readiness and the goals it wants to achieve. Some use cases of Conversational AI include: 
  • Conversational analytics
    The root of customer pain points is not always immediately identifiable, and the situation gets worse with the high volume of calls. Conversational AI can be the bridge between efficiency and customer frustration with its Natural Language Processing (NLP) component that uses rule-based approaches to define a set of patterns indicating the sentiments of a conversation. For example, a customer is frustrated but may not use words that directly match their feelings and agents can miss picking up on the sentiment cue. With PeopleQX, our quality assurance platform, such conversations are analyzed for sentiments and help pinpoint areas for improving customer experience in future similar scenarios.
  • Quick self-serve knowledge portals for employees and customers
    Customers want immediate answers yet more often than not, they are kept waiting longer than necessary. This isn’t surprising as agents have too much to handle with 61% of customer care leaders reporting growth in interactions since the Covid-19 pandemic. Conversational AI in knowledge management can assist employees in providing quicker resolutions with readily available answers, thus reducing the average hold time. Self-service knowledge banks provide a single repository for companies to house all their answers and for employees to contribute content when encountering new queries. It can also be utilized for customer self-serve as it can pick up commands in natural language to pull the relevant information or route complex issues to human agents for targeted resolution. 

• KPIs with Conversational AI 
  There must be reasons and goals for the decision to use Conversational AI. Stakeholders need to align their understanding and expectations from the get-go. Results with using AI need to be forecasted and metrics to measure KPIs need to be set. All these serve as master guides for any action to be taken and an expert would be able to help you set realistic targets to keep this endeavor sustainable. 

Companies prioritizing customer experience may have Conversational AI on their radar yet getting it implemented and running smoothly is not a walk in the park. It is an extensive procedure with many moving parts to be considered. With TDCX’s experience in customer service and AI expertise, we can help you get on the right track by understanding where you stand in these areas: 

• Data you have and need, alongside a data strategy for training of AI models
• Readiness to integrate Conversational AI in terms of systems and human resources 
• Potential to maximize Conversational AI 
• Expectations of stakeholders from implementing the technology 

Consult us today to chart new possibilities with Conversational AI.