When did the first chatbot (i.e., conversational AI) launch?
- 2000s
- 1980s
- 1960s
- 1990s
Believe it or not, the world’s first autonomous chatbot, ELIZA, was unleashed as a virtual psychotherapist in the mid-1960s! Despite being built on primitive Natural Language Processing (NLP), unsuspecting users were convinced they were talking to a real person.
We’re lightyears beyond ELIZA now. The introduction of generative AI (GenAI) has fueled the rapid transformation of conversational artificial intelligence (AI). Gone are the days of rigid, rules-based chatbots dependent on pre-programmed responses and decision trees.
Today’s conversational AI can better understand natural language and intent, making interactions feel more human-like than ever before. These advancements in conversational AI are inseparable from the GenAI revolution, which includes major breakthroughs in NLP, machine learning (ML), and large language models (LLM).
Thanks to these breakthroughs and positive reactions from both the companies deploying conversational AI and the people interacting with it, the market is anticipated to grow from $9.9B in 2023 to over $57B USD in 2032, a CAGR of 21.9%.
In this post, we will explore the latest conversational AI trends and how they improve the customer experience, save businesses money, and much more.
Benefits of adopting conversational AI
Enhanced customer experience
Many companies—including Google, Amazon, Walmart, and more—already integrate conversational AI into the customer experience. The benefits are clear. Intelligent chatbots available to customers 24/7 can provide support and troubleshoot problems for faster resolution times and a more convenient experience.
Additionally, conversational AI can personalize interactions by remembering past preferences and tailoring responses accordingly. By handling multiple customers simultaneously, conversational AI can significantly reduce wait times, improving the customer experience.
Improved customer satisfaction
The friendliness and efficiency of conversational chatbots can significantly enhance customer satisfaction by providing instant, accurate, and personalized support across channels. Conversational AI can answer customer questions in nanoseconds — much more efficiently than humans. A study by MIT Technology Review found that 90% of companies reported faster complaint resolution and improved customer satisfaction after implementing conversational AI.
Increased efficiency and cost savings
Chatbots can handle many routine inquiries, freeing human agents for more complex issues. This reduces labor costs and allows businesses to streamline their customer service operations. According to Gartner, integrating conversational AI in customer service will cut labor costs by $80 billion by 2026. Beyond customer service, conversational AI can automate HR tasks, such as answering employee questions and assisting with onboarding.
Improved accessibility
Conversational AI can make interacting with technology more natural and user-friendly. Virtual assistants like Siri and Alexa allow users to perform a wide range of tasks, access information, and control devices using natural language voice commands. This can be especially helpful for users who are uncomfortable with traditional interfaces. Users can ask virtual assistants to set reminders, make calls, send messages, play music, and control smart home devices using casual conversation.
This voice command interface is also helpful for visually or mobility-impaired people. It can assist those with visual or hearing impairments with alternative interaction methods, like text-to-speech or speech-to-text, too. Conversational AI can also provide information and support in multiple languages, making it easier for users with different language backgrounds to get the help they need.
To increase accessibility, look to integrate on various platforms, such as websites, messaging apps, and social media.
Increased sales and engagement
Chatbots can qualify leads, suggest products, and even upsell or cross-sell. They can also collect customer feedback and improve product recommendations. For example, a conversational AI chatbot could guide customers through a personalized shopping experience, asking about their preferences and providing tailored product suggestions based on their responses and past interactions. 65% of Americans say they have purchased online after interacting with a chatbot.
10 conversational AI trends 2024
The rapid advancements in conversational AI, driven by the integration of generative AI, are reshaping how businesses interact with customers and how users experience technology. As we move through 2024, several key trends are emerging that showcase the potential of conversational AI to revolutionize various aspects of our lives. Let’s explore seven that will significantly impact the coming years.
Conversational co-pilots
A prime use case for conversational AI is co-pilots or digital assistants. These AI systems are designed to facilitate tasks, provide information, and enhance productivity through natural language interaction. They can be integrated into various applications, from customer service chatbots like Ada AI Agent to productivity-enhancing tools in software like Copilot for Microsoft 365 Copilot to coding and software development assistants like GitHub Copilot.
Across use cases, co-pilots aim to help users perform tasks more efficiently and effectively — and they do. 70% of Copilot for Microsoft 365 users said they were more productive, and 68% said it improved the quality of their work, among other similarly impressive stats. The GitHub community was also blown away. 88% said GitHub Copilot made them more productive and helped them complete work faster, while 74% said they could focus on more satisfying work.
Intellias also has a co-pilot, IntellAssistant. This platform provides a framework for our clients to build custom digital assistants and AI chatbots. Its ready-to-go infrastructure allows for the swift launch and integration of digital assistants into clients’ corporate infrastructure and systems via the cloud, deploying frontends, backends, and LLM models while building specific functionality based on their business requirements. And it reduces development time from six months to one.
Seamlessly incorporated into the workplace ecosystem via Microsoft Teams, it offers numerous assistance features through a conversational interface, enabling users across diverse functions to access necessary information with transparency and efficiency. Clients in the retail, IT services, communications and media, health services, and entertainment industries are already benefiting from cost and time savings, efficiency gains, deep data insights, streamlined operations, increased loyalty and retention, and enhanced customer experiences.
Conversational AI-powered search
Traditional search engines rely on keywords to find answers relevant to a user’s query. Conversational AI-powered search creates a new way for people to find what they’re looking for and is a major conversational AI market trend. Rather than typing in a few keywords to search for each item, conversational AI search can understand intent and answer conversational queries.
Earlier this year, Walmart launched conversational AI search on its iPhone shopping app, enabling conversational searches. So next time you’re there to help someone move into a dorm room, for example, instead of wasting time entering keywords and racking your brain to think of everything that’s needed, you can ask the app, “What do I need for a dorm room?” It will return a variety of products one would need for a dorm room, like bedding, storage containers, shelves, desk lamps, shower caddies, kitchens in a box, and more. Conversational AI search saves customers time by making searching easier and returning more relevant options.
Hyper-personalized experiences
Shoppers, employees, bank customers, language app users—the list of use cases that can benefit from personalization is neverending.
Generative AI can provide a hyper-personalized experience by analyzing user browsing trends, purchase history, demographics, and more. For example, conversational AI search and chatbots can return results based on analysis of customers’ past purchases and behavior, including (or excluding) items a customer has purchased while including items more relevant to their interests.
It’s no surprise personalization is one of the top conversational commerce trends:
But it’s not just retail that benefits. In banking, personalized experience is critical, too. Bank of America launched Erica, an AI-based financial assistant, for their customers in 2018. Six years and 1.5 billion interactions later, BofA added a search bar to make the app more conversational. While they haven’t incorporated generative AI yet, they’re in the exploration phase.
Proactive conversational AI
Conversational AI gives virtual assistants the ability not just to answer questions efficiently but also to predict and anticipate a user’s needs based on the current conversation, prior interactions, and analysis of user behavior and other data. Rather than just responding to prompts and queries, proactive conversational AI will alert customers to promotions and new products based on their interests and provide updates on products a customer has purchased, just to name two examples. A conversational AI chatbot can even take the initiative to offer a discount to a customer complaining about a negative experience, thus increasing the likelihood of customer retention.
Voice assistants broaden accessibility
Voice assistants like Alexa and Siri have been around for a while. Until recently, however, they have been limited in the same ways as a rules-based chatbot — working from a set script and incapable of understanding intent. Natural language understanding and processing enable conversational AI to carry on increasingly complex, human-like verbal conversations with users, expanding the role of voice assistants and broadening accessibility and inclusivity.
Moreover, advanced conversational AI voice assistants can now communicate in multiple languages, making them accessible to a broader global audience. This multilingual capability allows users from different linguistic backgrounds to interact with voice assistants in their preferred language, enhancing the user experience and fostering inclusivity. Rather than typing to search and interact with chatbots, users can simply talk to a friendly, conversational AI voice assistant throughout their shopping experience, from search to purchase and post-purchase, making hands-free shopping a reality.
Learn about AI for language learning and see how Intellias helped Alphary build an NLP solution for language acquisition.
Customized conversational AI models
Organizations are moving toward customized conversational AI models to excel within particular domains or industries, such as finance, healthcare, or customer service, where generic AI solutions may fall short. These smaller, custom AI models can offer more accurate responses, better user engagement, and a deeper understanding of industry-specific interactions by focusing on narrower data sets and specific use cases. This specialization not only boosts the effectiveness of AI-driven conversations but also ensures that the models operate within the constraints of industry regulations and privacy standards.
Customized conversational AI models also present significant advantages in cost-effectiveness and resource efficiency. Businesses can seamlessly integrate these AI solutions into their existing operations, allowing them to automate unique tasks and processes without the extensive resource investment typically associated with larger, more general AI systems. This approach reduces operational costs and enhances the scalability of AI technologies, making it easier for companies to adapt to evolving market demands and expand service offerings while maintaining high standards of customer interaction and satisfaction.
Conversational AI as the standard
As more companies adopt conversational AI solutions for customer service, these intelligent, interactive chatbots are quickly becoming the new standard. Consumers are growing accustomed to the convenience and efficiency offered by conversational AI, and their expectations are rising accordingly. The days of simple, rules-based chatbots that often frustrate users with their limited capabilities and lack of understanding are coming to an end.
Businesses that fail to embrace conversational AI risk falling behind in customer satisfaction and loyalty. Customers now expect chatbots to understand their intent, provide personalized responses, and resolve their issues quickly and effectively. As conversational AI continues to mature and become more sophisticated, it will become the standard for customer service and a key differentiator in driving business success.
Conversational AI for employee evaluation and training
While conversational AI has many customer-facing use cases, it can also provide managers with invaluable information about how employees interact with customers. It can summarize, rate, and help improve customer service agent performance, thus improving the overall customer experience and saving managers valuable time.
Manager Assist for Amazon Connect is a prime example of conversational AI that evaluates customer service agent interactions. Manager Assist provides automated agent performance summaries for every customer interaction, including coaching and positive recognition opportunities. It saves managers the time of manually pulling together various reports, listening to recorded calls, and so on, and gives them vital insights into how their employees are performing so they can quickly help them improve their performance.
Multimodal conversational AI
The latest conversational AI technology goes beyond LLMs in the form of large multimodal models (LMMs). Multimodal conversational AI systems expand the conversation beyond words with their ability to process and respond to multiple modes of input: images, code, video, and audio. In January of this year, Google introduced multimodal conversational AI to its Lens feature, enabling users to add a written query to an image and receive an AI overview in addition to image matches. For example, upload a picture of an Australian Shepherd with the text “tell me about this breed,” and up pop several facts and links about the breed’s personality, history, physical characteristics, and more — plus a bunch of images of Aussies.
But this technology holds the promise of more than just multimodal searches, with the potential to analyze a wide range of inputs to provide over-arching insights into a company’s performance. “Today’s LMMs can see and hear the world. Tomorrow, they could also be trained on digital signals from equipment, IoT sensors, or customer transaction data—to create a complete picture of your enterprise’s health independently, without explicit instruction,” according to Leonid Zhukov, Director of the BCG Global AI Institute.
Integrating conversational AI with AR and VR
Augmented reality (AR) and Virtual Reality (VR) — like conversational AI — are rapidly advancing technologies transforming how people interact with the world via machines. They’re increasingly used for gaming, education, training, and even medical simulations.
Conversational AI can be integrated into AR and VR experiences to provide natural language interfaces, allowing users to interact with digital content using natural language. AR and VR can also be used to create more immersive and intuitive interfaces for conversational AI systems, enabling users to interact with virtual assistants or chatbots in a more natural and contextual way.
Imagine, for example, using Claude (Anthropic’s conversational generative AI tool) on your smartphone. Instead of seeing a text box, you see an image of a man, “Claude,” as if he’s standing in your living room. Instead of typing, you simply talk to Claude, who answers you right back. It’s as if you’re having a conversation with a super-smart friend.
The future of conversational AI
Emotionally intelligent chatbots
As NLU continues to improve, chatbots will not only be able to understand the intent behind a customer’s input. They’ll also be able to interpret the customer’s emotions, making chatbots even more human-like and more responsive to customers. The future of conversational AI features chatbots that can detect anger, disappointment, and the range of human emotions, responding sensitively to a customer’s emotional tone. Conversational AI technology trends continue to focus on improving understanding and communication to give users a more positive experience and increase customer loyalty.
Omnichannel conversational AI
One of the latest trends in conversational AI is omnichannel communication and marketing. Omnichannel conversational marketing trends broaden the scope of conversational AI, enabling a seamless customer experience across multiple platforms, including email, chat, SMS, social media, and more.
Advanced conversational AI chatbots can converse with customers across the apps that they use most frequently, significantly broadening reach and providing users with a more convenient and uniform experience. For example, suppose a customer compliments or makes a complaint about your company on Instagram. In that case, conversational AI chatbots can connect with and assist or thank the customer immediately, making the experience more convenient and efficient.
An omnichannel customer interaction might begin with the chatbot on a company’s site and then carry over into an email follow-up from the conversational AI. This may lead to an exchange on Facebook, with the AI remembering and building on each interaction.
The time is now
Conversational AI is already revolutionizing how businesses interact with customers and how consumers experience technology. As we’ve seen, the integration of generative AI has propelled conversational AI to new heights, enabling more natural, human-like interactions and unlocking a wide range of benefits for both businesses and consumers.
The trends we’ve explored underscore the transformative potential of this technology. By leveraging conversational AI, businesses can improve customer satisfaction, reduce costs, and gain valuable insights into customer preferences and behaviors. Meanwhile, consumers can enjoy more intuitive, efficient, and personalized experiences across various industries, from healthcare and insurance to e-commerce and education.
As conversational AI continues to evolve, driven by advancements in natural language processing, machine learning, and multimodal capabilities, we can anticipate even more innovative applications and use cases. By embracing the trends and opportunities presented, businesses can position themselves at the forefront of innovation.
Talk to one of our AI/ML experts today to see how our team can help you start innovating with conversational AI.
