Are We Ready to Ride the Wave of Disruption by Conversational AI?
Published on : Thursday 06-07-2023
The future of conversational AI is exciting and full of possibilities, says Chintan Oza.
Conversational AI is not a new concept. In fact, the first chatbot, ELIZA, was created in 1966 by Joseph Weizenbaum at MIT. This was almost 57 years ago. ELIZA was a computer program that could talk to people in natural language by interpreting their questions and providing scripted responses. However, it wasn’t until the late 1990s that chatbots started to gain in popularity. Since then, conversational AI has come a long way and we can’t afford to ignore it.
Today, Conversational AI is part of the intelligent automation spectrum of technologies used to digitise and transform operations, customer support, sales, and interactions with enterprise technologies. It is a technology that enables machines to understand and respond to natural language inputs. Conversational AI is used in chatbots, virtual assistants, voice assistants, conversational appliances, smart speakers and more. It has the potential to revolutionise the way we interact with technology and each other.
Some examples of conversational AI we have already experienced in our daily life include:
Chatbots: Chatbots are programs that interact with and imitate human conversation using Artificial Intelligence or AI. Chatbots can perform a wide variety of tasks related to customer service, marketing, sales, and even IT support, i.e., chatbot of a bank.
Virtual assistants: Virtual assistants like Cortana found on smartphones, tablets, and computers. Cortana is an excellent add on to the Windows OS and a must try for all.
Voice assistants: Voice assistants like Siri found on Apple devices, including iPhones and iPads; Android users can just say Hey Google and the assistant gets in action.
Conversational appliances: Conversational appliances like Amazon Echo’s virtual assistant activated by the “Alexa” voice command.
Smart speakers: Smart speakers like Google Home and Amazon Echo.
Difference between Large Language Models & Conversational AI
ChatGPT is a language model that is capable of engaging in human-like conversations. While it is not a conversational AI, it can be used as one. ChatGPT is trained on a massive text dataset, including books, articles, and websites. This allows it to understand various topics and engage in complex conversations. Conversational AI platforms construct and deploy chatbots and virtual assistants that allow users to interact with them through natural language conversations in order to provide a solution. While ChatGPT and conversational AI platforms share some similarities, they are designed to serve different purposes and are not directly interchangeable.
Factors driving adoption of Conversational AI
Conversational AI is becoming popular because it enables a more human-like interaction and, thus, a better consumer experience. It is described as ‘more personal, predictive, and complex than traditional rule-based chatbots’, which makes it a powerful tool in sales where tests have shown it significantly increases close rates in difficult areas such as insurance. With the tremendous rise in customer interactions, conversational AI as a tech innovation comes to their rescue. For both online and offline selling, most brands have deployed conversational AI-based chatbots to streamline customer support ranging from presales, sales assist and after sales activities. Below are few reasons driving the adoption:
1. Increased productivity: Conversational chatbots are available 24/7 and can handle simple requests, allowing customer service representatives (CSRs) to respond to concerns more quickly and lowering overall resolution times.
2. More sales: Providing customers with the correct information and updates through a conversational chatbot on time will boost your sales.
3. Reduced costs: Conversational AI can automate tasks that are currently performed by humans and thereby reduce human errors and cut costs.
4. Better customer experience: Conversational AI can deliver better customer experience, achieve higher customer engagement and satisfaction.
5. Increased customer acquisition: Conversational AI can help you acquire more customers by providing a more personalised and engaging experience.
6. Faster response time: Conversational AI can provide faster response times than human agents.
7. 24/7 customer support: Conversational AI can provide 24/7 customer support.
8. High accuracy: Conversational AI can provide high accuracy in responses.
9. Reduced human errors: Conversational AI can reduce human errors and cut costs.
10. More personalised experience: Conversational AI can provide a more personalised experience by remembering customer preferences and history.
11. Contactless customer service: Conversational AI can provide contactless customer service.
12. Increased efficiency: Conversational AI can increase efficiency by automating mundane tasks and freeing up agents to focus on more relevant tasks.
Conversational AI in Enterprise
Hereon, no Enterprise can afford to ignore conversational AI. Organisations have been quick to adopt conversational AI in front-end applications — for example, to answer routine service queries, support live call centre agents with alerts and actionable insights, and personalise customer experiences. Now, they are also discovering its potential for deployment within internal enterprise systems and processes. Chatbots currently represent the top use of AI in enterprises, and their adoption rates are expected to almost double over the next two to five years. Deloitte’s insight report mentions that the global conversational AI market, including chatbots and intelligent virtual assistants, is expected to grow at a CAGR of 22% during 2020–25, reaching almost US$14 billion by 2025. Let us discuss a few statistics and use cases of conversational AI in an Enterprise. This would give you an overview of how each of the functions can benefit by deployment of conversational AI.
Use cases in various functions of an Enterprise
1. Customer Service – According to a report by Grand View Research, the global conversational AI market size was valued at USD 4.2 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 24.3% from 2021 to 2028. The report also states that the customer service application segment is expected to hold the largest share of the market over the forecast period.
2. Sales – Conversational AI can help businesses increase sales by providing personalised recommendations and upselling opportunities. According to a report by MarketsandMarkets, the global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a CAGR of 21.9% during the forecast period.
3. Marketing – Conversational AI can help businesses improve their marketing efforts by providing personalised content and targeted advertising. According to a report by MarketsandMarkets, the global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a CAGR of 21.9% during the forecast period.
4. HR – Conversational AI can help businesses streamline their HR processes by automating tasks such as scheduling interviews and answering employee questions. According to a report by Grand View Research, the global conversational AI market size was valued at USD 4.2 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 24.3% from 2021 to 2028.
5. IT – Conversational AI can help businesses improve their IT support by providing automated troubleshooting and support. According to a report by MarketsandMarkets, the global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a CAGR of 21.9% during the forecast period.
6. Finance – Conversational AI can help businesses improve their financial processes by automating tasks such as invoice processing and payment reminders. According to a report by MarketsandMarkets, the global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a CAGR of 21.9% during the forecast period.
7. Supply Chain Management – Conversational AI can help businesses improve their supply chain management by providing real-time updates on inventory levels and shipping status. According to a report by MarketsandMarkets, the global conversational AI market size is expected to grow from USD 4.8 billion in 2020 to USD 13.9 billion by 2025, at a CAGR of 21.9% during the forecast period.
8. Product Development – Conversational AI can help businesses improve their product development process by providing real-time feedback from customers.
Challenges in deployment
However, there are some challenges faced during the development of conversational AI systems:
1. Language barriers: Languages, accents, dialects, slang, and jargons will create a communication barrier between a customer and the system.
2. Simultaneous conversations: Conversational AI is not capable of handling several speakers at a time as it may not be able to identify who is speaking and in what context.
3. Noise cancellation: Noise cancellation has to be taken care of while developing a conversational AI.
4. Security and privacy: Conversational AI must ensure that the information sent is safely processed and stored.
5. Lack of context: Conversational AI may not be able to understand the context of the conversation.
These challenges during deployment need attention and can be resolved to make the use case successful. Here are my recommendations:
1. Select the right problem and evaluate RoI – It is important to select the right business problem to solve using AI. This can help you avoid wasting resources on a problem that may not be worth solving.
2. Bridge the gap between business and technical leaders – Conversational AI projects require collaboration between business and technical leaders. It is important to ensure that both groups understand each other’s needs and goals.
3. Align technical teams – Technical teams need to work together to ensure that the conversational AI system is properly integrated with other systems.
4. Invest in data – Conversational AI systems require large amounts of data to train effectively. It is important to invest in data collection and management.
5. Be prepared to iterate – Conversational AI systems require ongoing maintenance and improvement. It is important to be prepared to iterate on the system as needed.
Startups in Conversational AI domain
Multiple startups from India have mastered conversational AI deployments and rolled out products/services for their customers. I am glad to share 7 Indian startups with products/services in conversational AI along with an overview of their product/service, headquarters, and year of incorporation:
1. Uniphore – Uniphore offers a conversational customer service platform that is powered by AI and automation technologies. The company was founded in 2008 by Ravi Saraogi and Umesh Sachdev and is headquartered in Chennai.
2. Senseforth.ai – Senseforth.ai provides conversational AI solutions for businesses across various industries. The company was founded in 2017 by Shridhar Marri, Krishna Kadiri, and Ritesh Radhakrishnan and is headquartered in Bengaluru.
3. LimeChat – LimeChat provides an AI-powered chatbot platform for businesses to automate their customer support. The company was founded in 2019 by Gaurav Singh and is headquartered in Gurugram.
4. Gupshup – Gupshup provides a conversational messaging platform for businesses to engage with their customers across various channels. The company was founded in 2004 by Beerud Sheth and is headquartered in Mumbai.
5. Wysa – Wysa provides an AI-powered mental health chatbot that helps users manage their stress and anxiety. The company was founded in 2015 by Jo Aggarwal and Ramakant Vempati and is headquartered in Bengaluru.
6. Yellow.ai – Yellow.ai provides an AI-powered conversational CX platform for businesses to engage with their customers across various channels. The company was founded in 2016 by Raghu Ravinutala and Rashid Khan and is headquartered in Bengaluru.
7. CropIn – CropIn is a brilliant Indian conversational AI start-up that offers agricultural companies cutting-edge farming solutions. It offers a variety of decision-making capabilities, including real-time reporting, data analysis, and data interpretation. Solutions for managing farms using AI include SmartFarm, SmartRisk, SmartWare, and RootTrace.
The future of conversational AI is exciting and full of possibilities. From voice interfaces to hyper-personalisation to integration with other technologies, there’s no doubt that chatbots and voice assistants will continue to play an increasingly important role in our daily lives. According to experts, the global conversational AI market, including chatbots and intelligent virtual assistants, is expected to grow at a CAGR of 22% during 2020–25, reaching almost US$14 billion by 2025. This is the time for technology and management leadership of MSMEs and large enterprises to start the next wave of pilots using conversational AI.
Note: this article was first published on 5th May 2023 in the Reader’s Blog section of Times of India (https://timesofindia.indiatimes.com/readersblog/lets-chintan/are-we-ready-to-ride-the-wave-of-disruption-by-conversational-ai-53422/)
Chintan Oza is an Entrepreneur & Mentor, and Founder, Anantam Ecosystems, He is also: Regional Director India, Founder Institute; India Region Lead at IEEE Entrepreneurship; and Member, IISEC at IEEE TEMS.
Chintan has 23 years of intrapreneurship experience in the telecom and ICT sectors. Chintan is an alumnus of IIT Mumbai, UC Berkeley, and Oxford University. Recently, he also completed the Innovation and Technology Commercialisation Professional certification from Georgia Tech.He spent an equal amount of time in his career with Reliance and Tata Group, deploying strategic telecom projects on a national and international scale.