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Conversational AI for Sales in 2024

The sales landscape has evolved dramatically with technology playing a critical role in modern conversational sales strategies. Sales managers, however, face a complication: Managing a growing number of leads while maintaining the personalized approach required for successful conversions. 

Conversational automation provides a resolution to this challenge by integrating AI-powered systems that engage prospects, answering their questions and guiding them through the sales funnel. Nearly 50% of the customers state that they are willing to close a sale only by using a conversational marketing bot.1

This article explores the use cases of conversational AI for sales, demonstrating its transformative potential for businesses and sales managers alike.

What is conversational AI?

A conversational AI is a type of computer program designed to understand and respond to human language, both written and spoken, in a manner that simulates human-like conversation. It is built using natural language processing (NLP) techniques, machine learning, and other advanced computational technologies to interpret, analyze, and generate responses to human inputs.

Conversational AI aims to improve user experiences, provide efficient communication solutions, and reduce the workload of human staff in various industries.

What is the difference between conversational AI and chatbots?

Conversational AI is a broad term referring to technologies that enable human-like conversations through text or voice. When the input is spoken, automatic speech recognition (ASR) is activated to convert the speech into written text. After that, natural language understanding (NLU) is employed by the conversational AI to analyze the conversation, comprehend the context and interpret the meaning behind the customer’s words.

On the other hand, a chatbot is a specific application of conversational AI focused on text-based interactions, usually for customer service, information provision, or general conversation.

Comparison of chatbot and conversational AI chatbot in practice

Figure 1. Comparison of chatbot and conversational AI chatbot in practice


For more on the differences between the two, check our article comparing conversational AI and chatbots.

Conversational AI can be found in various applications, such as:

  • Chatbots: AI-powered chatbots can assist with customer service, answer questions, and provide information on websites, messaging platforms, or social media.
  • Virtual assistants: Devices like Amazon’s Alexa, Apple’s Siri, and Google Assistant use conversational AI to perform tasks, provide information, and engage in conversation with users.
  • Messaging platforms: AI systems can facilitate messaging between users, help businesses automate responses to customer inquiries, and even perform language translations.
  • Customer support: Conversational AI can handle routine inquiries, resolve issues, or escalate complex matters to human support staff, helping them to meet customer expectations.

These applications lower operational costs while fostering customer loyalty, which boosts sales productivity.

What are the use cases of conversational AI for sales?

Conversational AI can be applied in various ways to enhance the sales process, improve customer experience, and drive revenue. Some applications include:

1- Booking appointments

AI-powered chatbots or virtual assistants can schedule sales meetings, product demos, or consultations with sales representatives, streamlining the process and minimizing manual effort.

HDFC Bank uses a conversational chatbot named “Eva”, developed with Google Assistant, to facilitate some of the bank’s services such as bill payments, ticker purchases, cab/bus bookings for the bank’s card holders (see Figure 2). This enabled the response efficiency, by replying 5 million user queries with 85% accuracy.2

Figure 2. HDFC’s chatbot “Eva”

Source: Chatbot Guide

Another example is KLM, which uses Google Assistant powered by AI to provide customers with a conversational setup for scheduling, tips, destination information, and so on.

2- CRM integration

Conversational AI can be integrated with CRM systems, automatically updating lead or customer information, ensuring sales teams have accurate and up-to-date records for better relationship management.

3- Customer support

Conversational AI can handle common customer queries, resolve issues, or guide customers through the buying process, providing timely assistance and enhancing the customer experience. 

For example, eBay uses a conversational Facebook Messenger bot to enable customers to have a personalized shopping experience both by written text and voice message.

4- Lead generation

Conversational AI can engage with website visitors or social media users, identify potential leads, collect contact information, and pre-qualify them before forwarding them to the sales team.

5- Product recommendations

By analyzing user preferences, browsing behavior, or asking questions, conversational AI can suggest relevant products or services, improving personalization and increasing the chances of making a sale.

6- Sales training and coaching

Conversational AI can be used to train sales reps by simulating customer interactions, providing feedback, or offering guidance on best practices for handling different sales situations.

7- Upselling and cross-selling

By analyzing customer interactions, past purchases, or responses to promotional offers, conversational AI can identify opportunities to upsell or cross-sell products or services.

How does the use of conversational AI differ from the use of chatbots in sales?

While chatbots are a specific application of conversational AI, there are some differences in their capabilities and applications in the context of sales.



  • They are primarily designed for text-based interactions, and their primary focus is on customer support and lead qualification
  • They can handle common customer queries, provide basic information, and pre-qualify leads before transferring them to a sales rep.


  • Chatbots are typically limited in their ability to handle complex sales conversations or provide more personalized recommendations

Conversational AI


  • Conversational AI, with its more advanced capabilities, can provide more personalized recommendations based on user preferences and browsing behavior.
  • Conversational AI can identify specific words or phrases in a conversation that can be utilized to anticipate when the prospect or customer is ready to make a purchase.
  • Conversational AI can be used for CRM integration, booking appointments, and even sales training and coaching, which cannot be achieved by chatbots alone.


  • Since conversational AI can be a more expensive tech solution, unless your enterprise is data-heavy, a chatbot is a better choice than a conversational AI application. 

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Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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