Operational Excellence

Redefining contact center experiences with Generative AI

Adopt the GenCARE Framework to raise efficiency, streamline processes, and reduce Opex in contact centers

Contact centers deploying AI tools for customer engagement continue to report low satisfaction scores and delayed resolution. A lack of deeper understanding of human languages and inability to comprehend nuances in text and audio messages conveying the need for support are the key reasons. This deficiency results in customer dissatisfaction and, eventually, tremendous damage to the CSP’s brand value. Key challenges faced by contact centers include:

  • Natural Language Understanding (NLU): Inability to accurately understand and interpret human languages. For example, misinterpreting customer inquiries (billing, service complaints) containing colloquial expressions leads to inaccurate responses
  • Context Retention: Struggle to retain context, leading to disjointed and frustrating exchanges, especially in longer or more complex conversations
  • Multilingual Support: Requires additional resources, training, and coordination, especially for languages with limited training data
  • Emotional Intelligence: Empathy and emotional understanding are challenging to replicate in AI systems

These challenges significantly raise multilingual support costs (20%-30%) for contact centers, with low chat containment (<20%) necessitating more live agents. This contributes to customer dissatisfaction and increased churn risk, mainly due to prolonged wait times.

As per McKinsey, generative AI can reduce the volume of human-serviced contacts by up to 50%, depending on a company’s existing level of automation. Use our GenCARE framework to enhance contact centers through Generative AI and achieve a 40% cost optimization while boosting customer satisfaction. The key components of the framework are:

  • NLU-based intent identification-Integrate the chat platforms with domain customized Generative AI models for quick and accurate query handling
  • Context-enhanced agent support-Leverage context retention capabilities to quickly identify customer issues and generate automated notes to boost agent productivity
  • Real-time sentiment analysis -Classify and score sentiments using the sentiment analysis module. Respond as per the customer’s emotional status
  • Multilingual query resolution-Use language translation to achieve zero wait time with a language-independent unified team

As per an McKinsey estimates that generative AI can reduce the volume of human-serviced contacts by up to 50 percent, depending on a company’s existing level of automation.