Chatbots vs AI Agents: What Is the Difference?

Intro

Customer service teams are struggling to keep up with rising call volumes and increasingly complex customer demands. Modern customers expect fast, personalized responses, whether they’re calling, texting, or messaging via social media. 

In the wake of these demands, traditional approaches to customer service are insufficient. Enterprise brands need to explore intelligent automation as a way to manage demand, reduce costs, and protect positive customer experiences. 

Chatbots were the original solution for contact center automation. These pre-AI systems relied on lots of upfront training to recognize certain keywords and then execute a predefined script to help answer basic questions or solve specific challenges. However, they typically lack any kind of contextual understanding and often lead to errors or frustration, making them ineffective in meeting rising customer expectations. 

The development of AI Agents has transformed the scope of contact center automation. Unlike chatbots, AI Agents can understand customer intent, adapt to changing context, personalize responses, and execute complex, multi-stage tasks.

The shift from static, inflexible chatbots to adaptive AI Agents shapes a new future for the customer service sector. In this guide, we’ll look at the difference between early chatbot technology and modern AI solutions to demonstrate why enterprise organizations need to start deploying AI Agents as soon as possible if they want to remain competitive in the next few years…

What Is a Chatbot?

The term ‘chatbot’ is a broad term used to describe any digital system that aims to mimic human conversation. It was first coined with the invention of ELIZA in 1966, a system that could communicate with users by identifying patterns in their speech and following predetermined rules to create a response.

This early system set the tone for chatbot development, with every new iteration offering improvements but still relying on human supervision and continual additions to a chatbot’s conversational workflow. 

Chatbot technology has continued to evolve to better serve customer needs. What began as a tool that aimed to mimic conversation became more task-oriented, welcoming in a new wave of chatbot-driven self-service. Apple’s Siri is an early example of this type of more advanced chatbot that sought to assist users in carrying out key tasks on their smartphones. 

Until the development of AI systems, chatbots explicitly relied on human supervision, pattern recognition, and scripted responses or dialogue flows. They could help a user carry out basic tasks, but they would be unable to accommodate any tasks or queries outside of their pre-designed scope and could quickly return errors or mistakes if a user strayed too far from expected parameters. 

With the advent of AI, what people refer to as a ‘chatbot’ has grown more advanced. Conversational AI and NLU allow a computer to understand natural human language, allowing chatbots to accommodate for the variability of real conversations. Generative AI, such as Large Language Models (LLMs) enables chatbots to create accurate, human-like responses based on a user’s input. 

With AI, chatbots are better able to navigate customer input and create personalized responses, but they remain entirely dependent on user prompts to take action. 

(Please note: the popularity of AI as a topic means discussions often use AI Agent, AI Assistant and AI Chatbot interchangeably. For this guide, AI Chatbot refers to more reactive, NLU-driven Conversational AI Agents.) 

Use Cases of AI Chatbots

When it comes to deploying a chatbot within your organization, the best use cases are those with narrow scope and well-defined parameters…

ID&V Agent

Identification or verification happens at the start of almost every contact center call. This process has long been a testing ground for different types of automation, with early attempts involving customers choosing pre-defined options via numeric input on their keypads. This helped reduce reliance on human teams, but often led to user frustration due to errors and navigation issues. 

Chatbots, specifically those designed with Conversational AI, are superior solutions for automating the ID&V process. They can carry out the ID&V process quickly and efficiently, using natural conversational language to guide users through the required inputs. Once verified, the chatbot can create a contextual handover and pass the case to a human agent so they can immediately start solving the problem, rather than having to ask repeat questions. 

FAQ Bot

For many enterprise organizations, most inbound customer queries often boil down to a few common questions. Though you can create FAQ pages on your website to try and answer these questions, you’ll find that customers often miss them and call your team anyway. 

An FAQ chatbot solves this issue by allowing you to field common questions that satisfy customers and save your human team’s time. An FAQ agent can listen to a customer’s question, then generate a personalized response based on the content within your existing knowledge base. 

Document/Information Gathering

Another straightforward use case that clearly demonstrates the time-saving capabilities of a chatbot-style AI virtual agent is document or information gathering. This is particularly useful for enterprise organizations that need customers to follow a specific process, such as those in the insurance sector. 

When submitting an insurance claim, for example, a customer may need to provide supporting documentation or photography. An AI Chatbot can communicate with the customer and guide them through each process step, ensuring that your company receives the information you need to process the claim in precisely the format you need. This also benefits the customer by eliminating confusion, enhancing their overall experience. 

Real-World Examples of AI Chatbots

Broad use cases help demonstrate potential applications, but here are real-world examples of AI Chatbots in action. Below you’ll find some great demonstrations of global brands using Conversational AI Agents/AI Chatbots to support customers and improve processes…

Henkel’s Stain Support Agent

Henkel is a global FMCG brand that sought to build brand loyalty with customers and increase awareness of its ‘cleaner living’ mission. To do so, the team identified a trend in customers looking for support with stains and spills. These customers were turning to online search engines in moments of panic, which often left them overwhelmed by conflicting advice.

To combat this, Henkel developed a Conversational AI Agent that functioned like an advanced chatbot that could identify stains and direct customers to the correct treatment. Available 24/7 and on any device, the chatbot allowed customers to get fast, focused support exactly when needed. 

Find out more by reading the full case study here. 

Translating WeChat Queries For BICS

BICS is a global communications enabler, with customers located around the world. Chinese users prefer to use WeChat for service queries, which means BICS needed a way to provide seamless support across that channel. However, WeChat’s complex API integration and strict privacy rules made it a tough challenge. 

With Cognigy’s support, BICS developed a WeChat AI Agent that allows Chinese customers to connect with BICS in their preferred language and platform. The AI Agent can translate in real-time, allowing English-speaking BICS agents to communicate effectively with Chinese-speaking customers. 

Having solved the technical and privacy-related challenges associated with WeChat, BICS can now focus on expanding its Chinese customer base by providing efficient service exactly where customers need it. Click here to learn more. 

Solving Complex Customer Queries With Lippert

Lippert is a component manufacturer that achieves over $5.2 billion in annual sales. As you may expect, such a high amount of sales also means a significant amount of customer service communications. 

These communications usually involved complex product queries, which caused problems for the Lippert team. Roughly 80% of customer interactions lasted as long as 5-7 minutes due to the need for detailed information gathering. 

To automate these queries, Lippert needed a solution that could not only help simplify the service process, but also one that could be trained with industry-specific terminology and product knowledge. 

Working with Cognigy, Lippert created a self-service AI Chatbot that was trained on a vast library of industry-specific resources. Once deployed, the AI Agent quickly achieved a 37% containment rate for customer queries related to part pricing, product availability, and order status tracking. This translates to almost 180,000 automated conversations that represent an 80% cost reduction for handled queries. 

What Is an AI Agent?

An AI Agent is an evolution of previous ‘chatbot’ technologies that brings a new level of autonomy to an organization. With new developments in Agentic AI, there are now two distinct AI Agent types that you can deploy to different processes:  

1. NLU-Driven Conversational AI Agents: Process-driven agents that use NLU to understand customer intent and trigger predefined dialogue flows. They are more predictable and controllable, but they require more human oversight. 
2. Agentic AI Agents: Goal-oriented agents that use LLMs to understand context, then independently plan and carry out tasks without needing to script every process. Their contextual awareness allows them to engage in natural conversations and adapt to changes in user queries. 
3. Composite AI: A ‘best of both worlds’ approach from Cognigy pairs process-driven AI Agents with goal-driven Agentic AI Agents, seamlessly switching to whichever solution is more appropriate based on context. 

With the advent of Agentic AI, AI Agents have become far more intuitive and effective in handling complex customer service processes. They can communicate instantly via voice or text with customers, identify the problem, and then plan their workflow to achieve the best solution. 

Where chatbots rely on human supervision and training via dialogue flow design, AI Agents are given initial training via LLMs and internal knowledge, then continue to learn and evolve based on customer interactions and both short and long-term memory. As such, they act as autonomous AI Agents that become more efficient over time, driving continual improvements in customer satisfaction. 

Use Cases of AI Agents

AI Agents are transformative for customer service teams, allowing you to provide fast, effective support to customers that drives cost reductions across your organization. Here are some fantastic example use cases to consider…

Customer Service Agent

Automating customer service processes can be risky. If your automated system can’t deal with the dynamic nature of real human conversation, customers will quickly grow frustrated and either abandon the call or insist on speaking to a human agent. 

AI Agents help avoid this issue, allowing you to deploy an efficient automated customer service agent that is available 24/7 and across multiple channels. Customers can get support without waiting on hold and can communicate in their native language. The AI Agent will create personalized, contextual responses and will pursue any tasks required to resolve the customer’s problem or need. 

Agent Copilot

AI Agents aren’t solely designed to help customers – they can also improve internal processes and augment your existing teams. AI Agents can be deployed to support human service agents, helping them complete cases more efficiently and taking care of some of the more labor-intensive, low-complexity tasks such as call summarization. 

Outbound Agent

Outbound calling has always posed challenges for automation due to the unplanned, unpredictable nature of customer reactions. With no reliance on dialogue flows, Agentic AI agents are the ideal solution for outbound calling. Not only can they reach out to customers and use natural language to guide them through whatever process is required, but they can also spot negative sentiment and adjust their actions on the fly.

Real-World Examples of AI Agents

These AI Agent examples show how leading brands have worked with Cognigy to revolutionize their customer service processes…

Bosch Augments With AI Agents

Bosch is a multinational engineering and technology company with over 400,000 employees based in more than 60 countries. The team recognized the value of AI-driven automation. It worked with Cognigy to deploy an AI Agent platform that fields more than 90 AI Agents that provide both internal and external support. 

One of the best examples of Bosch’s AI Agents comes in the form of Cognigy Agent Copilot, which supports human workers during tasks. The AI Agent listens to the call, retrieves relevant information and provides suggestions for the next best action for employees. It also transcribes the conversation, then creates and stores a detailed wrap-up. 

Even this single application has helped slash costs and improve agent productivity – but Bosch hasn’t stopped there, with AI Agents now operating successfully across more than 90 use cases. Click here to learn more. 

Ditching Phone-based Support With Frontier Airlines

For Frontier Airlines, fast annual growth rates of 15% to 30% meant customer call volumes were increasing rapidly. Dealing with this increase via traditional phone-based support proved difficult and costly, with peak times putting a severe strain on customer service operations. 

While working with Cognigy, Frontier deployed an AI Agent platform that could simultaneously handle hundreds or thousands of conversations. This immediately reduced the burden on service teams and allowed customers to get the support they needed without waiting. Not only did the AI Agent help reduce costs but it provided direct, personalized support that helped drive an increase in Frontier Airlines’ Net Promoter Score (NPS). 

Outbound AI with Toyota

Toyota is a renowned automobile manufacturer that produces over 10 million vehicles every year. Toyota is known for technical innovation and chose to work with Cognigy to explore AI Agents that could provide phone and chat support at scale. 

As part of this task, Toyota also developed an AI Agent named “E-Care” that is connected to a vehicle’s onboard electronics. If an engine warning light appears, the AI Agent reaches out to customers to arrange a service appointment and books it in, notifying the customer and the dealership to avoid any scheduling issues. 

This is a great example of the utility of a proactive AI Agent. For Toyota, E-Care helps make customers feel valued, reduces the hassle of manually booking an appointment and ensures the vehicle itself is kept in optimal condition. 

What Is the Difference Between Chatbots and AI Agents?

Understanding the difference between chatbots and AI Agents is quite simple: chatbots are a rule-based, input-dependent tool that can respond to queries, whereas AI Agents are flexible, intelligent systems that can communicate dynamically with users and carry out tasks. 

1. Chatbots, even those powered by AI, are entirely reactive and have little to no ability to carry out tasks. 
2. AI Agents are proactive and can break down complex customer needs, plan actions, and carry them out. 

Where a chatbot talks to your users to answer questions or provide content, an AI Agent works for them to solve problems and carry out tasks. 

How to Choose Between a Chatbot or an AI Agent

AI Agents don’t just offer an evolution on chatbot technology – they also reshape how customers respond to contact center automation… 

Chatbots are viewed as too impersonal and inflexible to offer support outside a highly limited scope. AI Agents, on the other hand, provide humanlike service experiences by engaging in fluid conversations, understanding context, identifying problems, and then acting on tasks. 

Though AI Agents are the superior option for direct customer support, there’s still value in deploying more limited AI Agents that function in a similar manner to chatbots to tackle basic tasks such as FAQs. With Cognigy’s AI Agent platform, you can have the ‘best of both worlds’, with AI Agents designed to carry out specific roles within your team. 

Get Started with Cognigy AI Agents

For enterprise contact centers, AI Agents are already shaping the future of customer service. By providing fast, efficient customer support available 24/7 on any device, your contact center can slash waiting times, improve agent productivity, boost customer satisfaction, and cut costs. 

Try a demo today to see it for yourself.