A chatbot is a program that simulates human conversation, using AI and natural language processing (NLP) to interact and automate responses. Chatbot capabilities range from simple Q&A sessions to complex systems that personalize and evolve, aiding in customer support and engagement 24/7.
There is a good chance that you’ve interacted with chatbots before. These unassuming, easily overlooked programs are most often encountered when users visit a website. As the user browses, a messaging window may pop up in the corner of the screen, making a cordial greeting, and helpfully offering to answer any questions. But while these messages are often accompanied by a headshot of a friendly-looking customer service or support agent, the truth is that there isn’t anyone on the other end. And that can be a wonderful thing for customers, employees, and businesses.
Through advanced AI programming and machine learning (ML), chatbots are capable of simulating human conversation. Chatbots can interpret, process, and follow through on user requests. The end result is an automated solution for providing reliable support, available at any time.
Evolution of chatbots
The origin of chatbots traces back to the 1960s with the creation of ELIZA—a program developed by Joseph Weizenbaum at MIT that could mimic conversation by matching user prompts to pre-scripted responses. This early experiment laid the foundation for the development of conversational agents designed to simulate human-like interactions. Over the decades, advancements in AI and NLP have dramatically transformed chatbots from simple pattern-matching scripts to sophisticated entities capable of understanding and responding to complex queries with a high degree of personalization, accuracy, and relevance.
Today, chatbots serve as an integral component of customer service and support ecosystems across a range of industries. They are employed to offer aways-available assistance, address a wide array of customer inquiries, facilitate specific user actions, and provide personalized recommendations. In many cases, chatbots take the role of virtual agents, freeing up human sales, service, and support representative to address more strategic tasks. Properly implemented, these AI-driven tools have the capacity to enhance the customer experience significantly.
Businesses and organizations across nearly every industry and interest use chatbots to streamline the customer-service process. And although these chatbots take many forms, most experts separate chatbots into two distinct categories: declarative and conversational.
The question/response chatbots that most people encounter when visiting websites usually fall into the category of declarative, or task-oriented chatbots. Incorporating rules programming—predetermined if/then conditional statements that provide automated responses to user inquiries—these chatbots are designed to direct users to solutions based on their requests. They may also incorporate some natural language processing (NLP) and machine learning (ML) to add a conversational element to their answers, but still tend to function as semi-advanced, interactive FAQ programs. In other words, declarative chatbots tend to take the role of information retrieval programs, but fall short of providing in-depth, human-equivalent conversation.
Although more rudimentary than conversational chatbots, declarative chatbots may nonetheless provide an extremely satisfying customer experience, serving up reliable solutions efficiently and instantaneously, without forcing users through multiple service channels.
More advanced than (and not nearly as common as) declarative chatbots, conversational chatbots are capable of taking the role of digital assistant. These data-driven, predictive chatbots adapt as more data becomes available, personalizing the customer experience based on past behavior, user profiles, and even cultural awareness. Amazon’s Alexa, Apple’s Siri, and Google Assistant are all examples of conversational chatbots.
Conversational chatbots leverage advanced NLP and ML, along with natural-language understanding (NLU), predictive intelligence, and data analytics to effectively learn user preferences, and tailor responses and other interactions to individuals. For example, if a user asks a conversational chatbot to update their laptop, it will know which device the user means. But more than simply answering questions and fulfilling requests, conversational chatbots can also anticipate user needs, make purchase recommendations, and initiate conversation. By interacting in a more realistic, conversational way, these chatbots help build positive relationships while providing always-available, always-reliable customer support.
Naturally, there's some overlap between these two classifications. To categorize types of chatbots as either declarative or conversational, it's important to first understand their underlying technology, purpose, and how they are designed to interact with users.
Here's a breakdown of several kinds of chatbots based on these criteria:
Menu-based chatbots
These chatbots guide users through a series of options or menus to provide responses. They are highly structured and follow a predefined path.
Keyword-based chatbots
They respond based on specific keywords identified in the user's input. Their responses are predetermined and limited to the scope of recognized keywords.
Rules-based chatbots
Operating on a set of predefined rules, these chatbots can only understand and respond to queries that fit within these rules, making them more rigid in their interactions.
No code or low code chatbots.
Often, these are designed to be built with minimal coding, relying on predefined templates or rules. While they can be either declarative or conversational, many lean towards the declarative side due to their simplicity and ease of setup.
AI-powered contextual chatbots
These use artificial intelligence to understand the context and nuances of a conversation, allowing for more natural and flexible interactions.
Voice bots
While they can be either declarative or conversational, advanced voice bots that understand natural language and context fall into the conversational category.
Hybrid chatbots
Combine features of both declarative and conversational chatbots, offering structured menu or keyword-based interactions alongside AI-driven conversational capabilities.Support chatbots
Designed to provide customer support, these can be conversational, especially when equipped with AI to handle a wide range of queries more dynamically.Transactional bots
Often conversational, especially when they need to navigate complex user requests or transactions in a more natural, intuitive way.
Social messaging chatbots
These can be found on social media platforms and can range from simple, rules-based bots to more advanced, AI-driven conversational agents, depending on their design and purpose.
Skills chatbots
Focus on performing specific tasks or "skills." Depending on how they are programmed, they can be straightforward, performing tasks based on specific commands (declarative), or more advanced, understanding context and user intent (conversational).
Two specific terms are often associated with chatbots: AI chatbots and virtual agents. Despite the many similarities of these these tools, there are also key distinctions between these terms that are worth considering. Although they are often used interchangeably, understanding the nuances between them is crucial for effectively leveraging their capabilities.
Chatbots
Chatbots represent the broadest category within this spectrum. Essentially, a chatbot is any software designed to simulate conversation with human users. This simulation can range from simple, predetermined pathways like decision tree-style interactions to more sophisticated, conversational AI-driven engagements. Chatbots are ubiquitous, appearing on various platforms including social media, dedicated apps, websites, and even traditional phone systems.
AI chatbots
AI chatbots are a subset of chatbots that incorporate artificial intelligence technologies to enhance their conversational abilities. These technologies include machine learning, which allows the chatbot to refine its responses over time based on accumulated data, and NLP and natural language understanding (NLU), which enable the chatbot to interpret and respond to user inquiries more accurately. AI chatbots are designed to facilitate more natural and intuitive interactions, progressively improving through deep learning to minimize misunderstandings and provide relevant responses.
Virtual agents
Virtual agents take the concept of AI chatbots a step further by integrating conversational AI and deep learning with robotic process automation (RPA). This combination allows virtual agents not only to understand and engage in dialogue with users but also to take direct action based on the user's intent autonomously. Virtual agents represent the most advanced form of chatbot technology, offering a seamless, efficient, and highly interactive experience.
Wherever users have issues that need to be resolved, chatbots may provide a valuable service. Because of this, chatbots serve business of all shapes, sizes, and industries. Business use cases for chatbots are as varied as the customers who interact with them. From booking tickets to events, to processing returns and exchanges, to collecting customer data and feedback, and beyond, there are nearly limitless applications for chatbot technology in business.
These use cases can broadly be divided into applications on the business side and on the consumer side, each catering to specific needs and objectives.
IT service management
Chatbots significantly improve IT service management by automating common tasks such as password resets, system status checks, outage alerts, and knowledge base queries. This enhances the efficiency of IT departments, ensuring that internal staff have round-the-clock access to essential services.
Customer contact centers
Chatbots can manage incoming communications, directing customers to the right resources while handling routine inquiries. This streamlines contact center operations and improves customer satisfaction by reducing wait times and ensuring accurate information.
Employee support
Businesses utilize chatbots for internal processes like onboarding new employees, scheduling vacations, conducting training, and ordering office supplies. These applications underscore chatbots’ role in facilitating self-service activities that do not require direct human intervention, thus freeing up resources for more complex tasks.
Marketing and e-commerce
Marketers leverage AI-powered chatbots to personalize customer experiences and streamline e-commerce operations. These chatbots provide personalized recommendations, promote products and services, and even assist with the forms.
HR and administrative Tasks
Chatbots offer employee self-service options for routine inquiries and tasks, thereby enhancing operational efficiency and employee satisfaction.
Customer service
Chatbots are at the forefront of consumer services, handling everything from event ticket purchases, hotel bookings, product comparisons, and routine banking, retail, and food service interactions. This broad application spectrum highlights chatbots’ versatility in enhancing consumer experiences.
Public sector services
Many public sector functions, such as city service requests, utility inquiries, and billing issues, are facilitated by chatbots, demonstrating their utility in improving public access to information and services.
Smart devices and apps
Consumers use AI chatbots with smart devices and mobile apps for a variety of tasks, including controlling intelligent thermostats, kitchen appliances, and scheduling healthcare appointments. These chatbots offer timely assistance, personalized recommendations, and automated reminders for time- or location-based tasks.
Social media and messaging platforms
AI chatbots are prevalent in social media messaging apps and standalone messaging platforms, where they provide instant, always-on assistance for customer service or human resource issues and facilitate seamless transitions to live support agents when necessary.
By offering a fast, easy solution capable of retrieving answers to common questions and handling simple tasks, businesses not only reduce costs and free up human agents to focus on other tasks, but also improve the customer experience in the process. In fact, according to a recent study, approximately 80% of people who have interacted with chatbots say that the experience was a positive one (Source: Uberall).
Chatbot benefits extend even further. Here, we take a closer look at some chatbot benefits for different kinds of users:
For complex requests or difficult-to-resolve issues, customers still prefer to speak with humans. However, most customers also expect reliable self-service options. As first-line issues arise, customers simply type their requests into the chatbot message window, and receive near-immediate responses. Rather than having to wait on the availability of customer-service agents, customers enjoy almost instant resolution. And, because responses are preprogrammed and fully vetted, users don’t have to worry about having their issues handled by inexperienced agents who might fail to provide accurate solutions.
Chatbots also improve customer engagement. With automated agents always standing by to help at a moment’s notice, customers become more likely to request assistance and follow up with questions. At the same time, chatbots empower businesses to more proactively interact with their customers. Where limited agent bandwidth once meant that companies were only capable of responding to customer-initiated inquiries, automated agents and virtual assistants can begin conversations with customers directly. This includes greeting customers and asking if they need any assistance, but it also allows businesses to share information about special deals, guides, tutorials, related products, and more.
Finally, as chatbot capabilities continue to expand, customers are enjoying more customized interactions that speak directly to their needs and interests. This personalized approach helps buyers feel valued and respected.
While more and more customers are preferring to interact with automated support and service options, live agents are likewise benefitting from chatbots. This is because the most effective chatbots are designed not to replace live agents, but to assist them in handling customer requests.
Live agents that spend much of their time dealing with repetitive users requests and inquiries may employ chatbot solutions to help address these important, yet front-line tasks. By passing off high-volume, low-urgency, simple-solution cases to automated agents, live agents are able to address more critical concerns. And in the event that a user requires human assistance, the chatbot can seamlessly redirect them to the appropriate support agent while also providing the agent with all relevant collected information. This ensures a seamless transition for the customer, and gives the agent the details they need without having to ask customers to repeat themselves.
Chatbots provide internal business solutions as well. Employees enjoy automated, self-service options in handling common tasks, such as resetting passwords, checking system status, and accessing vital internal tools and data.
Perhaps the most obvious chatbot business advantage is the ability to scale customer service. Instead of routing every customer request to a limited number of live agents, businesses can now address the most common inquiries automatically, resolving potentially thousands of cases without needing to divert limited live-agent resources from more-complex tasks.
The ability to scale customer service without hiring an army of service agents reduces costs while also improving operational efficiency. Businesses better utilize their human agents and get back more for their investment. And, because chatbots are essentially automated computer programs, it’s a simple matter to design chatbots capable of collecting user data while providing support. Businesses can analyze this data, building more-targeted messaging and developing more-actionable customer insights.
Data collection and analysis likewise pays off in qualifying leads. Using information drawn from conversations, and cross-referencing it against other available customer and demographic data, chatbots can help evaluate which leads are the most likely to convert, providing sales teams with qualified prospects worth following up on.
Internally, chatbots can likewise be employed to assist an organization’s employees in terms of support, solutions, and self-service. These chatbots are available whenever an employee may need them, empowering users with instantanious solutions even outside of standar business hours.
Taken all together, chatbots give employees and customers easy access to essential information and support. And when customers and employees have the resources they need to be successful, the business always benefits.
Although interactions with a chatbot may be at times very complex, at their most basic, they perform two core tasks: user-request analysis and returning the response.
User-request analysis
Before any chatbot can provide value to a user, it first needs to identify user intent and extract relevant entities. For example, a user that asks a chatbot “Where can I find office hours?” will be expecting to see a schedule of when the office is open, not the physical address of the office itself. The chatbot needs to be able to accurately assess the need behind the request by picking out not only specific keywords, but also other important cues in the language. Accurately analysing and identifying user requests is absolutely essential.
The second task in providing a useful chatbot experience is returning the response. Here, the chatbot finds or generates an accurate and relevant response based upon the user request. The response may take many forms, including the following:
- Predefined text answers
- Links to relevant support pages
- Citations from (or links to) knowledge-base articles
- Contextualized information based on user data
- Data retrieved from enterprise systems
- Actions performed through designated workflows
- Directing users to a service catalog
- ·Clarifying questions to further identify user intent
Rules-based chatbots operate on predefined playbooks created in the user interface's backend. These chatbots perform actions based on specific triggers, such as click activities, yes/no inputs, or the detection of certain keywords or phrases. This approach is straightforward and effective for handling simple, predictable interactions, making rules-based chatbots ideal for scenarios with a limited set of queries and responses.
AI chatbots leveraging NLP technologies can understand sentence structures, interpret intent, and learn from interactions. Unlike their rules-based counterparts, AI chatbots do not rely solely on predefined responses. They analyze the user's query to determine the underlying intent and generate an appropriate response based on accumulated knowledge. Over time, these chatbots refine their responses by learning from both correct and incorrect interactions. This ability to adapt and improve makes AI-driven chatbots particularly suited for environments that require handling a broad spectrum of queries, such as e-commerce platforms and other high-volume settings.
Live chat systems facilitate direct communication between customers and support teams through websites or mobile applications. Chatbots with live agent interaction capabilities use routing algorithms to manage real-time discussions, connecting customers with available representatives best suited to resolve their issues. These chatbots assess agent availability and expertise, ensuring that customer inquiries are directed to the appropriate representative. This blend of automated efficiency and human expertise allows for a seamless transition from automated to personalized service.
Implementing an effective chatbot involves more than just choosing the most advanced technology; it requires careful consideration of the platform's ability to meet current needs, future scalability, and its impact on user experience. Here are essential best practices and tips for deploying a chatbot that not only addresses immediate challenges but also sets the foundation for long-term success.
Select a chatbot solution that aligns with your immediate objectives without restricting future growth. Consider why your team needs a chatbot, the limitations of your current approach, and how a chatbot can overcome these challenges. Opt for platforms offering templates and tools that facilitate scaling and diversification of chatbot functions in the future. Ensure the platform provides a user-friendly design interface and a pricing model that supports efficient internal expansion.
The effectiveness of a chatbot significantly depends on the quality of its AI and how it interacts with users. The right AI technology should understand customer needs and articulate responses in a way that enhances the brand image. A well-implemented AI chatbot goes beyond serving as a complex FAQ system by delivering personalized, engaging interactions that reflect positively on your business.
Evaluate the complexity of developing, training, and enhancing your chatbot. Determine whether your organization requires a simple, ready-to-use solution or a more sophisticated system with API access for custom implementations. Recognize that AI requires ongoing training; assess how the chatbot can utilize existing chat logs for creating intents and how it adapts refine its responses through machine learning.
Rather than viewing chatbots as a replacement for existing communication options, consider how they can enhance and integrate with these channels. A chatbot should complement and connect with existing customer service systems, providing a modernized user experience while efficiently directing users to the appropriate resources and support personnel.
An effective chatbot implementation requires continuous monitoring and incorporation of user feedback. Regularly assess the chatbot's performance, user satisfaction, and the accuracy of its responses. Use these insights to make iterative improvements, ensuring the chatbot remains a valuable asset to your organization and its users.
Driven by advancements in artificial intelligence, machine learning, and natural language processing, chatbots are expected to become more intuitive, efficient, and personalized in their interactions. Over the next decade, these technological strides will enable chatbots to offer unprecedented levels of support and engagement—reshaping how businesses and consumers communicate.
Soon, chatbots will be able to exhibit more enhanced understanding and predictive capabilities, thanks to deeper integration of AI and ML algorithms. This will allow them to anticipate user needs and offer solutions even before a user explicitly states a problem, thereby improving the user experience and efficiency of services. Furthermore, as NLP technology advances, chatbots will become more adept at comprehending diverse languages and dialects, making digital interactions more accessible for a global audience.
The role of chatbots will continue to expand beyond traditional customer service domains into more complex fields. With the ability to process and analyze vast amounts of data, chatbots will offer extremely tailored advice, support, and learning experiences, adapting easily to individual preferences. And, as chatbots become more integrated into daily life and business operations, their development will increasingly focus on ethical considerations and security, ensuring that these digital assistants operate within a framework that respects user confidentiality.
These shifts will be accompanied by changes in the workforce. Although advanced chatbots will make certain job responsibilities obsolete, new positions will develop alongside AI-enhanced systems, providing clear opportunities for tomorrow's prospective employees to provide clear value to theoir organizations, with vital support from chatbots and other automated systems.
Whether they're customers interacting with your business or employees doing their best to grow it, today's users demand quick and easy access to support and services. ServiceNow Virtual Agent addresses this challenge head-on by offering an end-to-end, intelligent conversational experience. This powerful tool facilitates instant resolution to common requests across IT, HR, and customer service while also significantly improving satisfaction levels. With its guided setup, pre-built components, and integration capabilities, Virtual Agent is designed for rapid deployment and ease of use, ensuring that help is never more than a few clicks or taps away.
Employing purpose-built conversation topics and leveraging out-of-the-box templates and NLU models tailored for the most common service conversations, Virtual Agent provides immediate business. This tool's ability to analyze incident data through machine learning to recommend relevant conversations enhances its usability, while giving human agents more bandwidth to concentrate on other complex issues.
Additionally, Virtual Agent integrates easily with other ServiceNow tools and third-party channels, allowing for a versatile, enhanced, omnichannel solution.
And, as an added bonus, ServiceNow's Virtual Agent Designer and NLU Workbenchser empower organizations to build and refine their chatbot experiences without the need for coding, supporting advanced scenarios and third-party API connections. The inclusion of conversational analytics further enhances insights into user interactions. ServiceNow makes it all possible.
Ready to redefine support and service for your organization? Learn more about chatbot solutions with ServiceNow Virtual Agent, and give your internal and external users the improved experiences they crave.