Artificial intelligence has become an essential part of business operations and consumer experiences. There are basically two of the most common use cases: AI agents and chatbots. On the outside, they may appear similar for the most part – both use natural language processing to understand human requests and provide a proper response.
Yet, there are a couple of differences under the hood that affect how they are usable in practice. Business leaders looking towards AI to add value to their customer engagement and operations strategies would need to understand these distinctions.
In this article, we will discuss what makes chatbots an AI agent, their weaknesses and strengths, and the best applications in real-world scenarios. We can begin by defining what each one actually is.
Definitions: AI Agent vs Chatbot
An artificial intelligence agent is an artificial intelligence software that has the ability to sense its environment using sensors, act without human input, and learn to optimize its behavior towards a goal. For business purposes or the public sector (read more), AI agents are in touch with human users through natural language interfaces.
Specifically, a chatbot is a type of AI agent capable of only simple conversational interactions. Chatbots give predefined responses based on recognized keywords, queries or commands. They pretend to understand natural language but are quite limited in their abilities.
Some key capabilities that set AI agents apart:
- True natural language processing to understand context and intent rather than just keywords
- Ability to complete tasks and provide services, not just information retrieval
- Capacity to learn from interactions and improve over time
- Integration with other systems, data sources and IoT devices rather than working in silos
- Dynamic responses tailored to specific users and situations
Now, let’s explore some of the key differences and use cases in more depth.
Key Difference #1: Level of Intelligence
AI agents demonstrate higher-level intelligence that is more akin to human cognition. This intelligence manifests in several ways:
Natural Language Capabilities
AI agents can understand text and voice data in wider contexts, while chatbots rely more on recognizing set keywords, preset questions and limited variations. As one example, IBM’s AI agent, Watson, can ingest the equivalent of 1 million books worth of information to understand language nuances and topic concepts.
Reasoning Abilities
AI agents can dynamically gather additional information to reason through complex or unusual requests. Chatbots follow more rigid logic flows and have trouble responding adequately to novel situations.
For example, the IPsoft Amelia agent uses semantic reasoning to maintain thousands of knowledge domains. If she doesn’t know the answer to a question, she can query integrated data sources or even ask a human expert within the company.
Learning Capabilities
The most advanced AI agents continue to learn from new situations without additional programming. For example, conversational AI assistants like Siri, Alexa and Google Assistant analyze billions of speech samples to expand their skills. Chatbots only evolve when developers update their code.
According to McKinsey, as of 2024, over 72% of medium to large enterprises will be using AI platforms with automated AI model creation, monitoring and maintenance. This self-learning capability will be crucial for advanced AI agents to keep improving.
Key Difference #2: Task Scope
AI agents can be designed for a wide range of functions – anything from customer service to technical support to financial analysis. Their flexible architecture allows for expanding capabilities over time.
In contrast, chatbots have a narrow focus. They are usually used for information retrieval and basic transactions within a single domain. Therefore, you need to create separate chatbots for different purposes. This fragmented approach won’t scale as needs grow.
Let’s compare some real-world examples:
- The IPsoft Amelia agent provides IT support, customer service and back-office automation across different business areas.
- The Allstate Insurance chatbot Answers Your Questions can only respond to basic questions about insurance policies – it can’t actually sell a policy or file a claim.
Key Difference #3: Integration
As intelligent software programs, AI agents can integrate with multiple systems within and outside an organization. This allows them to draw data from diverse sources to fuel dynamic responses tailored to the situation and user.
According to Deloitte, the ease of integrating AI applications with existing data ecosystems is one of the main drivers of investment. Over 68% of IT decision-makers say their AI programs interact with external data sources provided by customers and business partners.
In contrast, most chatbots operate as standalone tools and cannot leverage other resources. This limits their functionality when users’ needs fall outside their parameters.
Some examples that highlight this distinction:
- The IPsoft Amelia agent connects with multiple enterprise systems such as Salesforce CRM, SAP ERP and ServiceNow. This allows her to handle complex customer service cases spanning data from different departments.
- The Marriott hotel chain’s chatbot can tell you about room amenities and nearby attractions but cannot access your reservation details from the booking system. To address any booking issues, a guest would need to be transferred to a human agent.
Use Cases and Examples
Now that we’ve compared the core capabilities of AI agents and chatbots, let’s examine some real-world examples of ideal use cases based on their strengths.
AI Agent Use Cases
Here are some of the most valuable business applications for AI agents:
Customer Service Agents
AI-powered virtual agents can handle a wide range of customer inquiries without human involvement:
- KLM Royal Dutch Airlines uses an AI customer service agent that can rebook flights, manage mileage programs, and handle over 100 other types of requests. It can understand complex customer questions and respond with detailed, personalized answers.
- The IPsoft Amelia agent steps in when human agents need help providing mortgage support at SEB bank. By quickly accessing data from multiple systems, she resolves complex issues 6 times faster than humans alone.
Intelligent Process Automation
It is expected that robotic process automation (RPA) will make it possible to digitize business operations, resulting in a 25-60 percent reduction in corporate costs.
But with AI setting the next level to that by using its power further, we can actually have our automation task done with AI, which will do more to make the process much more efficient as it becomes able to respond not just to simple situations but to more complex ones.
Use cases include:
- Extracting and structuring data from contracts and other documents
- Filling out forms and updating database records
- Following up on overdue invoices and other collections processes
AI assistants even understand speech and written customer communications to automate requests, saving substantial labor costs.
Conversational Platforms
AI chatbot capabilities keep advancing, allowing users to interact conversationally with technology in new environments:
- Google Duplex scheduling agent makes appointments via phone calls without the recipient realizing they’re interacting with AI.
- FPT.AI’s human-like AI news anchor has conducted live TV interviews with business executives in multiple languages.
- Woebot chatbot provides therapeutic mental health counseling based on cognitive behavioral therapy principles. In clinical trials, users felt an equivalent sense of connection and empathy compared to human therapists.
While still narrow in scope, these assistants showcase the potential for AI to take on specialized roles combining information delivery with emotional intelligence.
Chatbot Use Cases
While AI agents are taking on complex issue resolution and workflow automation, there is still a place for chatbots in basic informational and transactional support:
FAQ Services
Chatbots are good at answering a high volume of repetitive questions, freeing human agents to attend to more value-adding tasks.
- On its website, Sephora’s chatbot answers most customer service queries regarding orders, shipping status, store hours and more.
- KLM Royal Dutch Airlines built a Facebook Messenger chatbot that asks only for flight status requests and allows human agents to take a break.
Lead Generation
Chatbots are often used by many companies to talk to visitors as soon as they land on a website. Additionally, they can screen for key attributes to identify the sales prospects who are likely to be the most promising to prioritize for follow-up contact.
For instance, McKinsey uses a chatbot to qualify inbound leads based on seniority level, company size, and so on. The human sales rep gets routed to qualified leads faster.
Transaction Processing
For purchases that only require simple payment and order information, chatbots allow basic commerce capabilities without human intervention.
Domino’s Pizza customers can place delivery orders via text-based chatbots or even through voice commands with Amazon Alexa.
While AI agent capabilities are maturing quickly, chatbots still have relevance for targeted, straightforward tasks that rely less on context and judgment – especially at high volumes.
The Future of Intelligent Agents
As artificial intelligence and natural language processing continue to advance, more companies will shift from limited chatbots to multifunctional AI agents. The most sophisticated offerings already surpass human capabilities for many focused applications.
The global market for AI as a virtual customer service agent is forecasted to grow at an average rate of 45.8% per year, reaching nearly $15 billion by 2027.
Still, for the next several years, large enterprises can expect to use the combination of solution – multipurpose AI Agents for complex interactions and specialized chatbots for simple repetitive tasks. Because barriers to entry are lower for smaller businesses, initially, chatbots may be the order of the day for them.
As time goes on, the capabilities of an AI agent will grow, while chatbot usage will wane for most but the most trivial use cases. Now is the time for companies to begin experimenting and building up their in-house skills for any customer-facing operations.
Early adopters who can harness AI to enhance customer experiences, drive automation and unlock data insights will gain sustainable competitive advantage. AI will lead the future of business.
Conclusion
The use of AI to power conversational agents is set to change how many aspects of companies’ operations and customer communication. The reason chatbots are useful is because they require repeated interaction.
However, in this test, a greater spectrum of complex words is required, which involves different tasks and can be customized to new applications over time.
Knowing this basic difference allows business leaders to be smart about the AI adoption strategies to be employed for customer engagement and support, as well as the automation of business processes now and in the future.
