Since ChatGPT launched in November last year, the larger world suddenly woke up to the prowess of generative AI. Generative AI and LLMs became the latest buzzwords, and everyone wanted a piece of the generative AI pie. We couldn’t have expected businesses to be far behind in this race, could we? McKinsey’s State of AI report states that more than half the businesses worldwide are spending about 5% of their digital transformation budgets on researching how generative AI can be utilized to its full potential. According to a recent Forbes Advisory survey, 97% business owners think ChatGPT can make a positive impact on their businesses while 64% business owners are sure that ChatGPT can improve their customer experience (CX). But can generative AI and LLMs really helps businesses? Read on to find out.
The conversational AI space had been buzzing for a while, but the advent of generative AI made things more exciting. There was a surge in efforts to meaningfully utilize it in every possible sphere. Some ways in which LLMs can contribute to CX are:
- Personalizing customer service
- Configuring analytics to specific use cases
- Automating customer centers
- Assisting human customer service agents
. This opened up a whole new world of LLM use cases for Gnani.ai’s 60+ enterprise customers while also getting the team at Gnani.ai excited. Keep reading to find out how Gnani.ai leverages generative AI to improve CX.
AI-Powered Omnichannel Bot Development
Before generative AI, bots could be developed only by those who were experts in generating NLU components. With generative AI, NLU tasks have been automated. This saves significant time in bot development. Gnani.ai powered their bot builder platform using generative AI. Now, people need only describe their desired workflows, intents, etc, and the platform generates the appropriate NLU elements instantly. Gnani.ai’s bot builder platform was already a low-code/no-code platform. Now, with generative AI, the bot building platform has been further simplified, making bot building easier than ever.
Post-Call True Voice Analysis of Customer Interactions
A unique use case of generative AI is that it can be leveraged to analyze customer interactions and extract caller sentiments, keywords, and objections. Gnani.ai made sure to optimize this use case for their enterprise customers who wanted to find out and address their customer issues. This post-call true voice analysis of customer interactions is done by feeding the call transcripts into LLMs and the end result is a downloadable report with details about the customer interaction. The LLM parses words from customer interactions to understand real customer issues and helps businesses in delivering CX that is perfectly curated for every customer. This increases CSAT scores for businesses, which translates to more profits while addressing customers’ grievances.
AI-Powered Knowledge Base
Gnani.ai’s knowledge base is a generative AI-powered, self-serve repository of internal and external documents, websites and more. Omnichannel knowledge base bots can be created to intelligently answer queries with a multimodal experience. These bots can be created and deployed within a span of 10-15 minutes.
Scenario-Based Bots
Traditionally, bots were built using NLU components which had to be built from scratch. Bot developers also had to go through the grind of building workflows and stitching NLUs into them. It would generally take a week or more to get a bot live and anyone without NLU expertise could not build bots at all. Scenario-based bots are built just by collecting persona, use case, context, and scenarios. Through LLMs, Gnani.ai collects this information in a form-based format and this data is then used to build conversational bots. They can also deal with multiple scenarios arising during customer interactions with awareness of specific contexts and customer needs.
Call Disposition, Call Summary, and Agent Notes
Gnani.ai’s generative AI-powered agent assist tool can significantly reduce after-call work (ACW) for human customer service agents by generating call disposition, call summary and agent notes which are available for agents to review after a call.
Generative AI can parse customer interactions automatically to find call disposition. This categorization of the call helps in deciding the next best action for specific callers.
Generative AI-powered agent assist tool can summarize customer interactions highlight the most important aspects of the call for convenience of agents. These call summaries can be used later for reference whenever similar customer issues arise.
Such features enable the optimization of agent performance and in turn, optimize contact centers to address more customer issues.
Real-Time Sentiment and Emotion Analysis During Call
This analysis is immediately relayed to customer service agents who can address issues more efficiently since the labor of detecting customer sentiments and emotions is done by LLMs. Gnani.ai’s agent assist tool also suggests next best actions to resolve customer issues. These features working together optimize agent performance drastically so businesses can get the most out of their contact centers. Training time for newly hired agents is also reduced using real-time generative AI-powered sentiment and emotion analysis and the suggestive next action feature.
Generative AI-Powered Omnichannel Analytics
When generative AI powers analytics, as in Gnani.ai’s customer service platform, then LLMs process omnichannel interactions to generate comprehensive reports. These reports include conversation topics, dispositions, top keywords, top objections, conversation quality analysis and more. Conversation transcripts are fed to LLMs to process customer interactions which are then parsed to extract keywords and objections and generate reports with full details of the customer calls.
Road Ahead
While generative AI massively improves many aspects of CX, some areas still need more research and development. The team at Gnani.ai is hard at work trying to improve the following aspects of generative AI in CX:
- Improved context understanding and coherence: If generative AI is equipped with enhanced comprehension and response generation that is aligned with the conversation context, then it will be better equipped to provide relevant and coherent answers to customers.
- Better finetuning capabilities: Generative AI would be able to provide more detailed and domain-specific answers if it had better finetuning capabilities.
- Customizability and personality injection: More control over generative AI’s tone and style could be utilized to customize virtual assistants according to every business’s specific voice.
- Improved handling of ambiguous queries: If generative AI had the ability to effectively respond to unclear queries, it would reduce user frustration and improve customer experience.
- Active learning and feedback incorporation: If generative came equipped with active learning techniques which can be iteratively used to improve responses, it would provide continuous feedback to improve and enhance services.
With Gnani.ai’s commitment to making the most of generative AI, the time is not far when these shortcomings of generative AI in CX will be eliminated.
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