Chatbot design is the convergence of user experience (UX) design, user interface (UI) design, copywriting, conversational AI and machine learning in the deployment of chatbots, interactive voice response (IVR) and virtual agents. It dictates interaction with human users, intended outcomes and performance optimization.
A sophisticated chatbot design process within the enterprise context also incorporates business process management and process mining to identify where and how chatbot implementations can improve user experience and business outcomes, mapping specific actions to be taken during or following chatbot interactions.
Improve customer service and boost revenue with AI chatbots.
Subscribe to the IBM newsletter
In chatbot design, as in any other user-oriented design discipline, UI and UX design are two distinct, albeit interconnected, concepts.
UI design refers to how things look: tangible visual elements like layouts, buttons, toggles, colors, text fields and fonts—the aspects of a product, app or website that the user most directly interacts (or “interfaces”) with. Chatbot UI design informs decisions like where a user types text input or the size and location of the chatbot window.
UX design refers to how things work: strategic and logistical concerns like which actions can be taken at each step, what information is provided to or gathered from the user and how the ideal user journey unfolds. Chatbot UX considerations include what questions a chatbot will ask, how it responds to specific inputs or when to escalate cases to a human agent.
In essence, UI design puts UX design in motion. What a chatbot says (and why) is UX design, but how that chatbot dialogue is displayed to users is UI design; the information a chatbot requests at a given step is UX design, but whether users type their answer or select it from a dropdown is UI design.
While the fine details of your own chatbot’s user interface may vary based on the unique nature of your brand, users and use cases, some UI design considerations are fairly universal.
In all contexts, your chatbot UI should be:
For some chatbot implementations, such as integrations into third party messaging apps like Slack, WhatsApp or Facebook Messenger, the conversational interface cannot be customized. Such fixed UI elements should factor into UX planning.
For many businesses, especially those without resources to develop a bespoke UI from the ground up, it’s most efficient to use a chatbot builder with templates and drag-and-drop workflows that streamline UI decisions. Leading chatbot providers offer opportunities to customize stylistic elements to suit your branding, but adhering to proven UI design patterns lets you focus on your organization’s unique UX priorities.
In further exploring chatbot UX design, we’ll use certain terms with specific meanings in this context.
A great chatbot experience requires deep understanding of what end users need and which of those needs are best addressed with a conversational experience. Employ chatbots not just because you can, but because you’re confident a chatbot will provide the best possible user experience.
Choose the right domain(s): where can a chatbot help most?
Your FAQs are an excellent knowledge bases for queries, tasks and problems that surface frequently and predictably. Your customer service teams are likewise an important source of insight. Robust business process management can further identify opportunities and inefficiencies, as well as help delineate the different knowledge centers, communication channels and levels of complexity, security and privacy germane to each domain.
Chatbots offer the most value when two-way conversation is needed or when a bot can accomplish something faster, more easily or more often than traditional means. Some domains might be better served by help articles or setup wizards. Others, like those requiring highly technical assistance or sensitive personal information, might be better left to a real person.
Balance short-term and long-term business goals
For your first chatbot, it’s wise to walk before trying to run. The less data you have, the less confidently you can make predictions: companies that spend months building an inaugural chatbot spanning many topics often learn (after launching) that key assumptions about user behavior were wrong—and have to practically start again from scratch. Effectively addressing a shorter list of topics and intents yields a better user experience than providing inconsistent results across a wider domain.
Having said that, choose a domain with growth potential. Truly successful chatbot strategy yields not standalone solutions, but conversational tools deployed across all relevant channels—websites, messaging apps, phone systems—that enrich each other by generating shared data for training and optimization.
Broadly speaking, chatbot offerings fall into two categories: rule-based chatbots and AI chatbots.
Rule-based chatbots are simple and economical. They operate on if-then-else rules: each step (or branch in a decision tree) is assigned specific inputs the chatbot can recognize, each matched to a scripted response. Lacking natural language processing (NLP), rule-based bots must restrict user utterances to simple phrases or pre-written options. This may limit success unless your users’ needs are highly predictable, repetitive and straightforward—and will stay that way as you scale up.
AI chatbots are more robust, versatile and scalable. Artificial intelligence capabilities like conversational AI empower such chatbots to interpret unique utterances from users and accurately identify user intent therein. Machine learning can supplement or replace rules-based programming, learning over time which utterances are most likely to yield preferred responses. Generative AI, trained on past and sample utterances, can author bot responses in real time. Virtual agents are AI chatbots capable of robotic process automation (RPA), further enhancing their utility.
Many situations benefit from a hybrid approach, and most AI bots are also capable of rule-based programming.
Before designing the fine details of your customer experience, plan the foundation of your chatbot.
Users unconsciously, automatically infer a character behind your bot. It should convey the positive characteristics we seek in human conversation—empathy, curiosity, patience, affability—while maintaining the transparency of being a robot. The latter is essential to both managing user expectations and avoiding the “uncanny valley” effect: the strange uneasiness provoked by humanoid things that are not-quite-right. This can be most easily achieved by thoughtful choice of name, avatar and greeting.
Your chatbot’s personality impacts most elements of conversation design. It should reflect your brand and be appropriate for its intended users and function: a fitness assistant bot should use active language; a healthcare diagnosis app should avoid jokes.
Start by considering where your chatbot falls on various spectrums:
A chatbot provides only half of a conversation. You can’t control or fully predict the user’s half. Strong conversation design ensures a positive user experience by approaching conversation flow in a way that, no matter the user’s utterance, the chatbot’s response feels natural, believable and productive.
True coverage of a topic requires not only designing ideal conversational paths, but envisioning all unique paths a conversation might follow, including potential confusions, detours and dead ends. You might program your scheduling bot to recognize “I want to change my appointment,” but a user might say, “I can’t do Tuesday anymore.” You might have an optimal path, but is there a plan B if plan A fails? If plan B fails, can your bot explain the problem to the user? If the user doesn’t understand a request, can the bot phrase the utterance differently?
Even if your flow logic is flawless, mistakes happen—but minor imperfections should not derail an exchange. Here again, AI chatbots have a major advantage: instead of manually predicting and planning for every single typo to avoid interruptions, artificial intelligence can make educated assumptions and keep things moving. For example, IBM watsonx Assistant features autocorrect for mispellings, as well as fuzzy logic to aid recognition of intents and entities. Likewise, AI bots with speech-to-text can be trained to properly interpret accents, mispronunciations and jargon in voice inputs.
As in regular human-human conversation, users want to feel understood. Chatbot design can achieve this by ensuring that all bot responses, even non-preferred responses, are informative and relevant to the user’s utterance. When copywriting chatbot dialogue, aim to acknowledge what the user has said and avoid blunt changes of subject, random leaps in conversation, or “forgetting” information the user provided earlier in the contact.
Chatbots have limitations. The capacity to fail elegantly and provide routes to repair the conversation is essential: it’s okay for a bot to be wrong, but being wrong and irrelevant may doom the exchange and deplete trust in the chatbot. Bots must be designed to gracefully handle harassment, recognize nonsense or irrelevant utterances, react to topic shifts and get the conversation back on track.
Always decrease the user’s burden.
Clear, concise copy reduces friction and demonstrates respect for the user’s time. Reconsider your conversation flow if it requires lengthy instructions.
Effective chatbot design involves a continuous cycle of testing, deployment and improvement. Individuals may behave unpredictably, but analyzing data from past contacts can reveal broken flows and opportunities to improve and expand your conversation design.
Chatbots rely on, generate, and analyze a great deal of user data. That data must be handled carefully. Failure to do so has not only ethical consequences, but potentially legal and financial consequences.
Deliver consistent and intelligent customer care across all channels and touchpoints with conversational AI.
Find critical answers and insights from your business data using AI-powered enterprise search technology.
Multiply the power of AI with our next-generation AI and data platform.
Learn more about planning, designing, building and improving your first chatbot.
Explore three techniques to streamline your AI’s dialogue, increase its effectiveness, and satisfy your users: asking for less, giving clear choices and copy editing.
Learn the skills you need to build robust conversational AI with help articles, tutorials, videos, and more.
Through expert-led courses, learn about the underlying AI, machine learning, and natural language understanding available to the nontechnical SME.
IBM watsonx Assistant helps organizations provide better customer experiences with an AI chatbot that understands the language of the business, connects to existing customer care systems, and deploys anywhere with enterprise security and scalability. watsonx Assistant automates repetitive tasks and uses machine learning to resolve customer support issues quickly and efficiently.