What is Conversational Intelligence AI?

This article examines what exactly Conversational Intelligence AI is, how Conversational Intelligence AI works, some of its main benefits and challenges, top use cases and tools, and what is next for the field.

What is Conversational Intelligence AI?

With the massive amount of digital conversational data available–from virtual meetings to call centers to chatbots–it’s no surprise that enterprises are looking to AI to help make sense of this information overload. One area that is rapidly growing to meet this demand is Conversational Intelligence AI.

This article will examine what exactly Conversational Intelligence AI is, how Conversational Intelligence AI works, some of its main benefits and challenges, top use cases and tools available, and what is next for the Conversational Intelligence AI field.

What is Conversational Intelligence AI?

Conversational Intelligence AI refers to using state-of-the-art Artificial Intelligence (AI) research to build tools that derive intelligent, actionable insights from conversational data at scale. Then, sales and marketing teams use these insights to flag key sections of conversations, automatically identify risks or opportunities, coach representatives on best practices, identify buying patterns or other trends, and more.

What’s the difference between Conversational Intelligence AI and Conversational AI?

Conversational Intelligence AI is different from Conversational AI, which refers to technology that imitates human conversations, such as virtual agents or website chatbots. However, some similarities exist: they both process large amounts of data and are built using the latest Machine Learning and Natural Language Processing (NLP) research.

If you’re interested in learning more about how to build with Conversational AI, check out this YouTube video.

How does Conversational Intelligence AI work?

Conversational Intelligence AI is typically implemented via a tool on a platform or software that integrates with popular platforms to record audio and video conversations, whether the conversations occur via phone calls, virtual meetings, or presentations. 

Then, the tool transcribes the conversation and applies additional advanced AI models like summarization or Sentiment Analysis to the transcribed conversational data. This process provides users with a readable text and a detailed analysis of the conversation.

For example, AI Summarization models can distill large bodies of text, like transcribed phone calls, into their most important parts. The most robust AI Summarization models are trained on data highly relevant to their use case–such as conversational data–to provide useful summaries at scale. 

Companies then build tools and features on top of these summaries that use this data to more efficiently review large amounts of call data, increase customer and representative engagement, and enable more informed context sharing.

Read Now: 3 easy ways to add AI Summarization to Conversation Intelligence tools

Sentiment Analysis AI models can detect and label positive, negative, and neutral sentiments in a text. Product teams use Sentiment Analysis models to build tools that flag changes in sentiment during a conversation that could suggest potential red flags, buying indicators, and more. Sentiment Analysis models are also often used to label the overall sentiment associated with each interaction.

Source: Aloware‌ ‌

In addition, Topic Detection AI models can identify topics in a conversation such as clothing or weather and Entity Detection AI models can identify and classify recurring entities such as occupation or location that occur during a conversation. Product teams use Topic Detection and Entity Detection models to build Conversational Intelligence tools that identify trends, inform strategy, and increase ROI.

Finally, product teams are also building Generative AI tools with Large Language Models (LLMs) to make even more robust and customizable Conversational Intelligence AI tools. For example, a framework for applying LLMs to speech data, LeMUR, includes a Custom Summary endpoint that allows users to fully customize their meeting summaries instead of outputting a one-size-fits-all summary.

What are the benefits of Conversational Intelligence AI?

For teams that compile and analyze conversational data, Conversational Intelligence AI has a wealth of benefits.

Top benefits include:

  • Automated note taking, so agents and representatives can be more attentive during calls
  • Accurate and fast reviewing of conversations at scale
  • Identifying key areas of conversation, including buying indicators and risks
  • Coaching new agents and representatives on conversational best practices
  • Gaining visibility across all touchpoints to make data-driven decisions and to inform goal setting
  • Fine-tuning keyword and marketing strategies
  • Scoring, tagging, and qualifying calls automatically
  • Identifying industry trends
  • Creating custom summaries to highlight what went well/didn’t go well during a call

And more.

What are the top use cases for Conversational Intelligence AI?

CallRail is a call tracking and marketing analytics software company that uses an AI-first approach to inform its product strategy, including its Conversational Intelligence tools. CallRail partnered with AssemblyAI, an AI company that integrates production-ready AI models built using the latest state-of-the-art AI research, including models for call transcription, Sentiment Analysis, and Summarization. The call tracking company then built robust Conversational Intelligence AI tools on top of these AI models that provide high value for their numerous customers.

As a Contact Center Software as a Service, Aloware uses Conversational Intelligence AI to help companies more efficiently convert leads. The company’s Conversational Intelligence offering consists of features built using AI models for smart call transcription and AI Summarization, which together help their customers more quickly and intelligently perform QA. Aloware also built features using Sentiment Analysis models to help their customers more easily track their users’ opinions and feelings across various metrics.

Screenloop, a Hiring Intelligence Platform, uses Conversational Intelligence AI to automate candidate review processes, helping its customers spend 90% less time on manual hiring and interview tasks and realize a 20% reduced time to hire for open roles. The automated tools also enhance the company’s customers’ ethical hiring initiatives by increasing transparency throughout the hiring process.  

AI-powered meeting recorder Grain uses Conversational Intelligence AI such as speech-to-text transcription that creates highly accurate transcripts for each meeting or call processed through its platform. This integration results in significant time savings and more powerful intelligent insights for its core customer base

What are the challenges of Conversational Intelligence AI?

Conversational Intelligence AI is informed by some of the most cutting-edge, state-of-the-art research available. However, the field is innovating rapidly and some challenges do remain.

One of the biggest challenges for companies looking to build Conversational Intelligence tools is keeping up-to-date with the latest innovations in AI. The field of AI is moving at an unprecedented pace and companies will need to prioritize creating Conversational Intelligence tools built using state-of-the-art research, or risk customer churn.

Thankfully, companies can often mitigate this potential challenge by partnering with an AI company that provides scalable, secure, production-ready AI models built using the latest AI research. Companies can then rely on this partner’s internal AI research team to iterate based on new research developments and innovation. In turn, companies can then know that the AI models that they integrate into their products will be continuously state-of-the-art.

Looking forward: What’s next for Conversational Intelligence AI?

The field of Conversational Intelligence AI will continue to innovate and progress rapidly, in line with concurrent progress in the field of AI in general. Advances in AI research will provide further opportunities for companies to build competitive Conversational Intelligence AI products that unlock enormous amounts of value for their customers.

As this research progresses, AI models that inform Conversational Intelligence, such as transcription, Summarization, Sentiment Analysis, Topic Detection, and Entity Detection, as well as LLMs, will become more accurate as well. These advances will importantly increase the utility of Conversational Intelligence tools, as companies strive to sift through and make sense of the large amounts of digital data available and make intelligent, informed decisions.

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