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The Rise of the Contact Centre Chatbot: How Conversational AI Is Redefining Customer Support
Contact centre chatbots—also known as customer service chatbots or conversational bots—have rapidly become a cornerstone of modern customer engagement. At this point, their presence is almost universal: if you have an internet connection, you’ve probably interacted with one. Embedded within call centre software and customer service software, these virtual assistants guide customers through text or voice and now stand among the most influential automation tools shaping the customer experience.
Their impact is immense, both in revenue generation and cost reduction. Research by McKinsey in the second half of 2023, 61% of customer service organisations reported direct revenue growth linked to generative AI, up from 45% at the start of the year. Cost savings followed a similar trajectory. In early 2024, 45% of organisations said generative AI and Conversational AI had reduced their costs; by the second half of the year, that number had risen to 58%, a surge of nearly 30%.
Customers are feeling the shift as well. Salesforce studies show that since October 2024, the longest time U.S. consumers spend resolving a single customer service issue has dropped by three hours, largely thanks to the widespread adoption of Conversational Customer Service AI models. That reduction translates into the equivalent of hundreds of thousands of employee work-hours reclaimed and redirected to higher-value tasks.
Unsurprisingly, the business world has taken note. According to Microsoft, 79% of leaders now view AI adoption as essential for staying competitive. Customer experience leaders share this urgency: Zendesk reports that 90% of CX trendsetters expect that within just a few years, about 80% of customer issues will be resolved without any involvement from a human agent.
This acceleration is driven by the extraordinary pace of AI development. Conversational AI models aren’t simply improving; they are making month-to-month leaps that redefine what chatbots are capable of. Salesforce found that in 2025, AI was already resolving 30% of all customer cases. By 2027, that share is projected to reach 50%, meaning that half of all support interactions could soon be handled autonomously. And the nature of those interactions is evolving, too: in the first half of 2025, employee interactions with AI agents grew by 65% month over month, while the depth of those conversations rose by 35%. AI is not only shouldering more volume: it is enabling richer and more substantial dialogues.
As a result, the strategic significance of conversational AI is becoming unmistakable. Zendesk notes that 70% of CX leaders now see AI agents not just as efficiency boosters but as core architects of deeply personalised customer journeys. In other words, chatbots are evolving from simple problem-solvers into key shapers of how customers experience and perceive a brand.
Yet, as every customer-experience professional knows, not all chatbots are equal. Some rely on advanced AI, others on basic rule-based logic. Some excel with routine queries, others can handle complex or highly contextual issues. Ultimately, a chatbot's effectiveness depends on how well its capabilities align with a business’s needs and the realities of its customer-service operations.
In this article, we break down everything you need to know about contact centre chatbots: why they matter, the types available, how AI is transforming their capabilities, and what the future holds for conversational AI in customer service.
But first, a quick refresher: what exactly is a contact centre chatbot?
What Is a Contact Centre Chatbot and How It Boosts Efficiency and Satisfaction
Essentially, a chatbot is a computer program that emulates human conversation, accommodating both written and spoken interactions. Serving as an interface between individuals and digital devices, chatbots come in diverse forms—from basic programs providing quick responses to complex digital assistants. Chatbots leveraging Artificial Intelligence can learn and evolve as they have more conversations, delivering personalised interactions based on accumulated information and creating dynamic and tailored conversational experiences for users, contributing to customer experience automation and optimisation.
Some of the ways in which contact centre chatbots boost efficiency and satisfaction are:
Instant responses and 24/7 availability
One of the biggest sources of customer frustration is waiting for help, especially outside regular business hours. 90% of customers rate an “immediate” response as important or very important when they have a customer service question and 60% of customers define “immediate” as 10 minutes or less, according to research by HubSpot. Chatbots eliminate this problem by being available around the clock. They can instantly answer routine questions like order status, billing issues, or service availability. This immediacy not only reduces frustration but also conveys reliability, showing customers that the company is always ready to assist them.
Ability to handle high volumes of inquiries simultaneously
During peak periods, human agents can become overwhelmed, resulting in long wait times and unresolved queries. Chatbots, in contrast, can handle hundreds, or even thousands, of interactions simultaneously without any drop in performance. This scalability ensures that customer needs are consistently met, preventing service bottlenecks and maintaining operational efficiency even under heavy demand. According to Salesforce, in the first half of 2025, businesses experienced an average monthly growth rate of 70% in daily customer service conversations handled by human agents, largely driven by the adoption of Conversational AI tools.
Consistent and accurate information delivery
Human agents, no matter how skilled, can occasionally make mistakes or give inconsistent information. Chatbots pull from a centralized knowledge base, ensuring that every customer receives precise, uniform answers. Companies using Generative AI are also 35% less likely to have agents feel overwhelmed by the information in front of them during calls, according to research by Deloitte. This consistency strengthens trust, as customers are less likely to encounter conflicting information or errors, and it reduces the risk of misunderstandings that could escalate into complaints.
Automation of repetitive tasks like password resets or appointment scheduling
Many customer inquiries are repetitive and low in complexity, yet they take up a lot of agents’ time and are a major source of frustration and stress. Conversational AI bots can handle these tasks, resetting passwords, updating contact details, or scheduling appointments automatically and efficiently. By relieving agents of this time-consuming work, AI frees them to focus on complex or high-priority issues that require empathy, judgment, or negotiation skills.
The impact is clear: 81% of agents say AI makes them more productive, 80% report it reduces job stress, and 79% say it increases job satisfaction, studies by Salesforce reveal. By eliminating these time-draining, stress-inducing tasks, AI not only improves employee well-being but also speeds up responses for customers, creating a smoother, more efficient experience for everyone.
Personalized interactions based on customer history and preferences
Modern chatbots can analyze previous interactions, purchases, and preferences to tailor their responses. For example, a returning customer might be offered product recommendations relevant to their past orders, or troubleshooting steps can be customized based on known issues. This level of personalization makes customers feel understood and valued, which increases satisfaction and loyalty.
Smooth escalation to human agents when needed
Not every problem can be solved by a chatbot. When complex issues arise, a well-designed chatbot gathers all necessary context and transfers the case to a human agent. This prevents customers from repeating themselves and ensures the handoff is seamless. By bridging automated support with human expertise, chatbots maintain efficiency while still providing the nuanced assistance that some situations demand.
Naturally, customer service departments and contact centres have found a massive edge in this technology: a Contact Centre Chatbot is able to not only reply to common questions automatically, freeing up agents’ time to focus on high-value interactions, but it’s also available 24/7. This make chatbots one of the most important tools used for Call Centre Automation. However, there are many more applications for Contact Centre Chatbots beyond customer service, from appointment scheduling to lead generation or customer feedback collection.
The Future of Contact Centre Chatbots: LLMs and AI Agents
Recent advancements in Conversational AI are taking efficiencies to the next level, enabling businesses to adopt more advanced AI agents like ConnexAI's. Unlike traditional chatbots, AI Agents leverage deep learning and machine learning to not only manage routine inquiries but also learn from past interactions and anticipate future needs. This adaptability allows them to handle increasingly complex and unique queries, bridging the gap between basic automation and human-level understanding.
Some advanced AI customer service chatbots leverage Large Language Models (LLMs), which represent a significant leap beyond traditional AI chatbots. While basic chatbots are typically built for specific use cases, relying on predefined tasks and scripted interactions, LLMs excel in understanding and processing natural language. Their advanced architecture enables them to address a broader range of inquiries with greater depth, precision, and flexibility. By integrating LLMs, businesses can deliver more personalized, context-aware customer interactions, meeting modern customer expectations with unmatched efficiency and intelligence.
A chatbot’s proficiency in certain tasks will always depend on its level of technological sophistication, on whether it uses AI or not, and on the algorithms backing its performance. As we have said earlier, not all chatbots are alike, and it’s crucial to know the differences between them before implementing one in your business.
In the next section, we’ll go through the different types of chatbots you can find in different contact centre software platforms and what differentiates them.
Summary
Contact centre chatbots, or conversational bots, have become key in customer service, handling routine queries, saving billions of work hours, and responding to most customer interactions. While early chatbots used simple scripts, advances in AI—especially large language models (LLMs)—allow them to learn, personalize responses, and handle complex inquiries, bridging the gap between automation and human-level understanding. Available 24/7, they free agents for higher-value tasks and support uses beyond customer service, like scheduling or lead generation. Effectiveness varies by sophistication, and many customers still prefer live agents for nuanced issues, making it essential to understand chatbot types before implementation.
What Are the Types of Contact Centre Chatbots and Which One Suits Your Business?
Stateless vs Stateful Contact Centre Chatbots
Chatbots can be categorised as either stateless or stateful based on how they handle information and context during user interactions.
Stateless contact centre chatbots consider each user prompt as a new and independent event. They don’t store any details about the user or the context of past conversations. Consequently, each response is generated from scratch, depending solely on the immediate input. Stateless chatbots are apt for handling uncomplicated queries where it is not essential to maintain context across interactions.
On the other hand, stateful chatbots maintain a record of user details and the ongoing conversation context throughout their interactions, creating a conversational state that allows them to reference previous user inputs. This enables stateful chatbots to provide a more personalised and context-aware conversational experience. They excel in handling more complex and continuous conversations, offering a seamless and engaging user experience.
Scripted or Quick Reply Chatbots
Scripted contact centre chatbots are the most basic type, functioning as a hierarchical decision tree. They engage users through predetermined questions, guiding the interaction until arriving at an answer to the user’s query. Menu-based chatbots, where users are prompted to choose from a predefined list or menu, are also similar.
Keyword Recognition-based Chatbots
Contact centre chatbots using keyword recognition seek to grasp user input and formulate responses by identifying keywords within customer replies. These bots combine customizable keywords with Artificial Intelligence to generate suitable responses. Yet, if the AI component lacks sophistication, these chatbots might encounter difficulties when faced with repetitive keyword usage or redundant questions.
Contextual Contact Centre Chatbots
Contextual chatbots, surpassing their less advanced counterparts when it comes to the quality and efficiency of interactions with users, rely heavily on the processing and interpretation of large amounts of conversational data.
Utilising artificial intelligence (AI) and machine learning (ML), these bots store and remember user interactions, evolving and enhancing their performance gradually. Instead of depending on specific keywords, they analyse the subtleties in customer inquiries and phrasing, autonomously delivering responses and thereby improving their overall capabilities.
Our Athena AI Agent is an example of a contextual contact centre chatbot and stands as a prime illustration of AI’s capabilities. It is specially designed to provide detailed responses and personable responses to customer service queries, with the capacity to fine-tune its training according to domain-specific datasets and learn from past interactions; this allows Athena to improve its proficiency at dealing with specialised industry queries.
Deployable on multiple channels, such as websites, social media, and WhatsApp, it facilitates conversations with customers and propels customer journeys forward.
Voice-Enabled Chatbots
Voice-enabled contact centre chatbots are sophisticated conversational interfaces designed to understand and respond to spoken language. Unlike their traditional text-based counterparts, these systems utilise automatic speech recognition (ASR) technology to transcribe spoken words into text, facilitating seamless communication through voice interactions.
These chatbots make use of natural language processing (NLP) and machine learning algorithms to grasp user intent, allowing them to interpret complex spoken queries and provide responses that are contextually relevant. This technological advancement significantly enhances the user experience by providing a hands-free and intuitive means of communication. It proves particularly valuable in applications like virtual assistants, customer service hotlines, and voice-activated systems, where users can easily engage with the bot by speaking naturally.
LLM Chatbots
LLM chatbots are a more advanced type of AI-powered conversational tool, built on Large Language Models. Unlike basic scripted or keyword-based bots, LLM chatbots understand and generate natural language with remarkable depth and nuance. They can handle diverse queries, provide context-aware responses, and adapt to the conversational style of individual users. While they still operate within the bounds of their training data, LLM chatbots bring greater flexibility and intelligence to customer interactions than traditional chatbots.
AI Agents
AI agents represent the next step beyond LLM chatbots. These systems are autonomous, capable of multi-step reasoning, and can orchestrate tools or workflows to achieve specific goals. Unlike chatbots that mainly respond to inquiries, AI agents proactively guide users, anticipate needs, and continuously learn from interactions. For example, Athena AI Agent is a contextual AI agent designed for contact centres, able to provide detailed, personalised responses, improve over time from past interactions, and handle specialised domain-specific queries. AI agents can be deployed across multiple channels—websites, social media, WhatsApp, and more—facilitating seamless, intelligent conversations that actively advance the customer journey.
