Feature
The Gist
- Technological evolution. Conversational AI has transitioned from simple scripted responses to complex systems using ML and NLP.
- Interaction quality. Today’s conversational AI offers nuanced, adaptive interactions far beyond basic chatbots.
- Daily relevance. Conversational AI is crucial in enhancing everyday digital communication and efficiency.
Editor's Note: This article has been updated to include new data and information.
When people think of conversational artificial intelligence (AI) their first thought is often the chatbots they might find on enterprise websites. Those mini windows that pop up and ask if you need help from a digital assistant.
While that is one version, many other examples can illustrate the functionality and capabilities of conversational artificial intelligence technology.
What is conversational artificial intelligence, exactly, and how did it come to be? What does a conversation with artificial intelligence look like? And what impact will this technology have on business-consumer relationships?
The History of Conversational AI: From Chatbot to Present
The standard definition of conversational AI is a combination of technologies — machine learning (ML) and natural language processing (NLP) — that allows people to have human-like interactions with computers. To understand this further, let’s look at the evolution of conversational AI.
1960s: The Rise of the Chatbot
Chatbots made their debut in 1966 when a computer scientist at MIT, Joseph Weizenbaum, created Eliza, a chatbot based on a limited, predetermined flow. Eliza could simulate a psychotherapist's conversation through the use of a script, pattern matching and substitution methodology.
Although Eliza could pass a restricted version of the Turing test — a test that determines if a machine can display intelligent behavior indistinguishable from a human being — and fool people into thinking they were talking to another human, it was simply following rules and simulating the conversation with no real level of understanding.
1970s: New Natural Language Understanding
A decade later, Kenneth Mark Colby at the Stanford Artificial Intelligence Laboratory created a new natural language processing program called PARRY. Although it was the first AI program to pass a full Turing test, it was still a rule-based, scripted program.
1990s: Optimized Natural Language Generation
In 1995, Richard Wallace created the Artificial Linguistic Internet Computer Entity (ALICE). It used what was called the Artificial Intelligence Markup Language (AIML), which itself was a derivative of Extensible Markup Language (XML).
Like its predecessors, ALICE still relied on rule-matching input patterns to respond to human queries, and as such, none of them were using true conversational AI.
Related Article: The Importance of Conversational Intelligence for Customer Experience
The Advancement of Conversational AI
What is conversational AI? It relies on NLP, automatic speech recognition (ASR), advanced dialog management and ML, and can have what can be viewed as actual conversations.
Conversational AI uses deep learning to continuously learn and improve from each conversation. It is flexible and able to jump from one topic to another, much like actual human speech and unlike traditional chatbots, which are limited to pre-defined scripts and rules and cannot respond with anything not originally inserted into its conversational flow.
Recent advancements in generative AI, such as OpenAI’s GPT models and Google’s Gemini, have transformed conversational AI by enabling more context-aware, creative and adaptive interactions. Unlike traditional scripted responses, generative AI is built upon large language models (LLMs) trained on huge datasets to understand context, predict user intent and generate human-like dialogue in real-time.
This technological advancement in AI has unlocked new possibilities across various industry domains.
- In customer service, generative AI-powered chatbots can handle complex queries with nuanced, conversational responses while escalating only the most intricate issues to human agents.
- For content creation, businesses are using these models to draft personalized emails, write marketing copy and even develop user manuals with remarkable efficiency.
- In professional support, they are able to assist employees by providing real-time insights, generating reports or drafting responses to clients, saving time and enhancing productivity.
As opposed to rule-based chatbots, these new capabilities represent a fundamental shift in what conversational AI can achieve, allowing for interactions that are not only efficient but also engaging and tailored to individual user needs.
“Rule-based or scripted chatbots are best suited for providing an interaction based solely on the most frequently asked questions. An ‘FAQ’ approach can only support very specific keywords being used,” said Eric Carrasquilla, CEO at Vendavo.
“Conversational AI is ingesting the customer feedback and learning in real-time that value, which can be applied to the same question at a different point of a client’s journey,” he added.
By using conversational AI chatbots, basic contact queries such as delivery dates, tracking numbers and shipping fees can be easily and quickly taken care of, while more complex or serious customer service inquiries can be passed on to live customer service representatives.
“The appropriate nature of timing can contribute to a higher success rate of solving customer problems on the first pass, instead of frustrating them with automated responses,” said Carrasquilla.
Multimodal AI: Expanding Conversational Capabilities
The latest advancements in conversational/generative AI include the rise of multimodal AI models, which can process and respond to inputs across multiple formats, including text, images and voice. OpenAI’s ChatGPT-4o, the multimodal version of ChatGPT, is a prime example, capable of interpreting images, understanding spoken language and generating coherent responses that integrate multiple modalities. This breakthrough enhances conversational AI’s versatility, making it more relevant across industries.
In retail, multimodal AI is poised to enhance customer experiences by allowing users to upload photos for product recommendations or seek assistance through voice commands. For customer service, multimodal models can streamline interactions by integrating text and visual cues — for example, helping troubleshoot issues by analyzing a photo of a defective product. In accessibility, these models can provide inclusive solutions by converting speech to text for individuals with hearing impairments or offering voice-based navigation for visually impaired users.
Multimodal AI is not just a technical leap; it represents a shift toward more intuitive and user-centric interactions. By integrating different formats, conversational AI can deliver richer, more dynamic experiences that meet diverse user needs.
Related Article: How AI in Ecommerce Is Getting Talkative
People Trust Conversational AI Solutions
One top use of AI today is to provide functionality to chatbots, allowing them to mimic human conversations and improve the customer experience.
Webinar
Oct
16
Agentic AI Playbook: Real-World Customer Service Use Cases You Can Deploy Now
Boost self-service by 30% and slash call volume by 63% with agentic AI.
Register
Webinar
Oct
22
Beyond Storage: Smarter Content, Bigger Impact with DAM + AI
Discover how the DAM + AI duo makes content smarter, stronger and more accessible.
Register
Webinar
Nov
6
Fix the Content Bottleneck: Build a Better WebOps Strategy
Content stalled? Dev overloaded? You’re not the only one. Learn how streamlined WebOps bridges the publishing gap.
Register
Webinar
On demand
CMS Briefing: A Live Look at What’s Next in AI-Driven Platforms
Learn how leading organizations are using AI‑driven tools to publish faster, personalize smarter and stay secure.
Watch Now
Webinar
On demand
Ready or Not: How Data-First Organizations Are Unlocking Agentforce Potential
Learn how to cut through the noise, activate Agentforce and build a Salesforce AI strategy that actually delivers.
Watch Now
Webinar
On demand
AI in Customer Service: Faster Resolutions, Happier Customers
Don’t let rising demand burn out your team. See how to build a smarter, more resilient support org.
Watch Now
A Statista report revealed that process automation and customer service were the two most popular applications of AI in American and European companies. AI chatbots exemplify this trend by automating routine tasks like answering FAQs, tracking orders and scheduling appointments, while simultaneously improving customer service through real-time, personalized interactions. Their ability to blend efficiency with a human-like conversational touch makes them a cornerstone of AI-driven strategies in both areas.
A Pew Research survey found that 27% of Americans interact with AI multiple times a day, while 28% engage with it daily or several times a week. More importantly, 65% of respondents reported using a brand's chatbot to answer questions, highlighting the growing role of AI in everyday customer interactions.
According to a Forbes Advisor article, despite ongoing concerns about AI, 65% of consumers express trust in businesses that use AI technology. Among them, 33% are very likely to trust such businesses, and another 32% are somewhat likely, reflecting a growing acceptance of AI-driven solutions.
It’s not just customers that are beginning to trust conversational AI. Those established in their careers also use and trust conversational AI tools among their workplace resources. Oracle and Future Workplace’s annual AI at Work report indicated that 64% of employees would trust an AI chatbot more than their manager — 50% have used an AI chatbot instead of going to their manager for advice.
Twenty-six percent of those polled said bots are better at providing unbiased information and 34% said they were better at maintaining work schedules. Not only that, but 65% of employees said they are optimistic, excited and grateful about having AI bot "co-workers" and nearly 25% indicated they have a gratifying relationship with AI at their workplace.
The Washington Post reported on the trend of people turning to conversational AI products or services, such as Replika and Microsoft’s Xiaoice, for emotional fulfillment and even romance.
Amid growing advances in AI, many people have turned to AI-driven chatbots and voice bots for meaningful interactions that mimic human connection. Platforms like Xiaoice, which claims to have communicated with 660 million active users since its release in 2014, are a good example of how conversational AI has evolved to fulfill not just practical needs but also emotional and social ones, becoming an integral part of daily life.
Conversational AI Is Trusted — but Is It Safe?
People trust conversational AI solutions and they find the technology helpful when they need to search for information. But does that mean its safe to use?
With the two examples of conversational AI above, where people have private conversations with a bot, perhaps even share personal information, the question of privacy and security might come to mind. How safe is conversational AI to use?
Like most things, conversational AI is as safe as it’s built to be. Users not only have to trust the technology they’re using but also the company that created and promoted that technology. Finding out if a specific conversational AI application is safe to use will require a little bit of research into how the bot was made and how it functions.
You’ll want to look for three things when it comes to finding a safe AI bot:
- End-to-end encryption: Encryption increases application security by ensuring no one else but the sender and receiver can access a conversation. It’s also necessary to comply with the General Data Protection Regulation (GDPR) in the European Union.
- Authentication processes: Processes that help verify a person’s identity — like using your thumbprint to gain access or sending a verification code to your phone that you then type in.
- Privacy policies: Companies will have written privacy agreements on their website or application that covers how they collect information and what they do with it. The ideal application would not share or sell private information to any other entity.
Conversational AI users should also ensure they have a fundamental understanding of internet safety measures, including:
- Using strong passwords and changing them regularly
- Keeping applications up-to-date
- Using websites that start with “HTTPS” rather than “HTTP”
- Using an ad-blocking extension to block pop-ups and spam
- Using a trusted antivirus that’s kept up-to-date
- Enabling multi-factor authentication for important accounts
- Avoiding sketchy downloads that could lead to a virus
Businesses (and People) Rely on Omnichannel Conversational AI
Traditional chatbots are text-based. They’re typically found on only one of a brand’s channels — usually a website. They aid in customer service conversations and can improve the overall customer experience.
Conversational AI solutions, however, are omnichannel. They can be accessed and used through many different platforms and mediums, including text, voice and video.
“The pairing of intelligent conversational journeys with a fine-tuned AI application allows for smarter, smoother choices for customers when they reach out to connect with companies,” Carrasquilla suggested.
Among other common conversational AI examples is the digital assistant — think Cortana, Google Home, Amazon Alexa and Siri.
According to a Statista report, there are around 200 million smart speakers in use worldwide, which — along with virtual assistants — have facilitated the acceptance of conversational AI in the household. According to WorldMetrics, 41% of people who own a voice-activated speaker say it feels like talking to a friend or another person.
Some call centers also use digital assistant technology in a professional setting, taking the place of call center agents. These digital assistants can search for information and resolve customer queries quickly, allowing human employees to focus on more complex tasks.
Chris Radanovic, a former conversational AI expert at LivePerson and now director of product marketing at Infobip, told CMSWire that in his experience, using conversational AI applications, customers can connect with brands in the channels they use the most.
“Intelligent virtual concierges and bots instantly greet them, answer their questions and carry out transactions, and if needed connect them to agents with all of the contextual data they’ve collected over the course of the conversation.”
Related Article: AI Cybersecurity: Safeguarding the AI-Driven Customer Experience
Conversational AI Facilitates Hyper-Personalization
“Hyper-personalization combines AI and real-time data to deliver content that is specifically relevant to a customer,” said Radanovic. And that hyper-personalization using customer data is something people expect today.
Radanovic emphasized that consumers and brands are embracing conversational AI because it provides personalized experiences that are much quicker and more convenient than traditional ways of interacting with businesses. Customers do not want to be waiting on hold for a phone call or clicking through tons of pages to find the right info.
According to Radanovic, conversational AI can be an effective way of eliminating pain points in the customer journey.
“A giant source of frustration for consumers is repeating information they’ve already shared, like re-confirming a phone number or having to re-explain a problem to multiple agents,” he explained.
As brands adopt tools that allow conversational AI to connect customer data, said Radanovic — like connecting conversation histories with previously stated intentions — the conversations they have with customers will feel more personalized.
Conversational AI Is Part of Our Daily Lives
Traditional chatbots remain useful for answering straightforward queries, but conversational AI has become an integral part of modern life. Powered by LLMs and natural language understanding (NLU), these systems provide dynamic, hyper-personalized interactions across channels. From customer service and virtual assistants to emotional support and professional guidance, conversational AI has evolved to address complex needs.
Whether guiding shoppers in augmented reality, automating workflows in enterprises or supporting individuals with real-time translation, conversational AI is reshaping how people interact with technology. As it continues to learn and improve, conversational AI bridges the gap between human needs and digital possibilities.
Conversational AI models are not only very effective at emulating human conversations but they have also become a trusted form of communication. Businesses rely on conversational AI to stimulate customer interactions across multiple channels. The tech learns from those interactions, becoming smarter and offering up insights on customers, leading to deeper business-customer relationships.
But these bots go beyond pure business use. People use these bots to find information, simplify their routines and automate routine tasks. Ultimately, they’ve become an extension of people’s daily lives.
