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A support chatbot should handle a defined customer task better than the available alternatives—not become a mandatory gate between a person and help. Design it around real support needs, tell users what it can do, make recovery and human contact obvious, and measure whether it resolves problems with less effort.
Decide whether a chatbot is the right service
Start with a customer need, not a technology choice. Review telephone enquiries, emails, chat logs, repeated concerns, website analytics, and feedback from both customers and support staff. Look for frequent, bounded tasks where a conversational exchange could genuinely help.
Then compare a bot with simpler interventions. Improved help content, navigation, or website search may solve the same problem with less time and cost. GOV.UK recommends considering that trade-off, along with how a chatbot fits the service, what information it needs, and what answer or decision a user expects.
For each candidate task, write down the user’s goal, the information required, what the service can do, and when another channel is more appropriate. A chatbot is a poor fit when it cannot act on the request, depends on information it cannot reliably access, or would add steps to a task that another channel handles more directly.
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Choose a narrow first release
Begin with a small set of common tasks and expand in response to evidence. A gradual rollout can keep the work focused and produce more useful feedback for later improvements. Compare the bot with alternatives against practical criteria:
- Task fit: Can the interaction lead to a useful answer or action?
- Effort: How many steps and how much information does the customer need to provide?
- Operational fit: Does it connect to the processes staff already use?
- Access and inclusion: Can people use it in their context, and are other routes available?
- Knowledge ownership: Who keeps answers accurate when policies or products change?
- Recovery: What happens when the bot misunderstands or cannot complete the task?
- Support and upkeep: Is a person or alternative contact route available, and can the organization test and maintain the experience?
Prioritize likely paths without ignoring failure
Google’s conversation-design guidance uses the “80/20 rule” as a heuristic: invest most in key paths, cover likely detours, and handle rare edge cases proportionately. It is not a guarantee that a particular share of requests will follow one path. Avoid spending disproportionate effort on unlikely scenarios, but do plan safe responses for uncertainty and failure.
For background on service selection and rollout, see GOV.UK’s guidance on using chatbots and webchat tools.
Set expectations before the first request
Tell customers plainly that they are using an automated service. Explain what it can help with and what is outside its scope. For a natural-language bot, provide examples of useful questions. Do not use a fictional human identity or a person-like avatar that could mislead someone about who is responding.
Keep each turn short and relevant. Ask focused questions, then give the customer room to answer. A brief listening cue can reassure them that the system understood and indicate what happens next—for example, “I’ll look up the return options for your order.” Use that kind of statement only when the system is actually doing the stated work.
Tone should help the service, not distract from it. Microsoft’s conversational-experience guidance emphasizes consistent, appropriate language and respect for users’ emotional and cultural context. If someone is frustrated, acknowledge it plainly and move toward a useful next step. A warm message does not make an unresolved problem resolved.
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See Microsoft’s principles of conversational experience design for guidance on efficiency, accessibility, intuition, empathy, and trust.
Build conversations around real customer tasks
Use the language customers actually use, not internal team names or process labels. Organize the knowledge behind the bot so it can recognize different ways of expressing the same goal. For each common need, define the starting request, the minimum information required, the answer or action available, and the point where another channel should take over.
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Previous enquiries and customer feedback can reveal recurring questions and unfamiliar wording. Use them to create representative user utterances for each intent—the goal behind a request—and keep the underlying answers current. GOV.UK advises testing response accuracy with users before release; after launch, requests outside scope and changes in accuracy can reveal missing coverage or outdated service information.
Do not make customers learn the organization’s vocabulary before the bot can help. Microsoft’s example of a user saying “I can’t print” illustrates how a short, ordinary description can lead into troubleshooting without requiring technical terminology first. That is a design principle, not proof that every technical problem belongs in a chat interface.
Choose free text, choices, and information carefully
Use free-text input when customers need to describe a problem in their own words. Use suggested buttons or widgets when a small set of choices makes the next step clearer. In either case, provide only the information relevant to the current decision; do not answer a focused question with a large block of unrelated content.
Microsoft’s Bot Framework conversational UX guidance suggests evaluating whether a bot solves a problem with minimal back-and-forth, is better or easier than the alternatives for that problem, works on platforms customers care about, and can help when a person gets stuck.
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Design misunderstanding and handoff before launch
Map common intents and likely detours, then decide how the bot will respond when it is uncertain. A useful recovery does not pretend to understand, repeat the same prompt indefinitely, or leave the customer to guess what to do next.
What should a support chatbot say when it doesn’t understand?
Acknowledge the request, be specific about what remains unclear, and ask one necessary clarification or offer a small number of relevant choices. If the bot still cannot help, state the limit in plain language and show the next useful action, such as contacting a person or using another support channel.
For example: “I understand you’re asking about a delivery, but I can’t tell whether it has not arrived or arrived damaged. Which applies? If neither, you can talk to a support agent.” Adapt the options and contact route to the actual service; do not promise a transfer unless one is available.
Can I speak to a person?
Make the human route visible and usable, including when the customer is unsure how to phrase a request. Preserve other contact choices, such as a phone call or webchat, where the service offers them. Plan how a handoff works operationally: what context carries over, what hours or queues apply, and what a customer should do if an agent is not immediately available.
GOV.UK warns that users can get stuck in conversation loops and recommends a real-person transfer or another contact route. Gartner’s August 4, 2026 release reports that 87% of 3,566 surveyed B2B and B2C customers said access to a human agent was essential when a company uses GenAI for customer service. Gartner also advises against making GenAI a mandatory first step for every issue. This survey finding is not a universal rate for all customers or proof that any particular bot will perform well.
Gartner Senior Director Analyst Eric Keller said: “Service leaders should not use GenAI as a mandatory first step for every issue.” Read the Gartner survey release for its survey context.
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Why can’t I get past the chatbot?
This usually indicates a service-design failure: the bot may be outside its scope, may not recognize the words used, or may offer no effective way to correct an assumption. During planning, identify prompts that could create dead ends and specify how each can be unblocked. Do not make customers repeat themselves without changing the response, or require a bot interaction before they can reach help for an issue it cannot handle.
Make accessibility, alternatives, and follow-up part of the service
Accessibility is a design and evaluation requirement, not a claim to make based on the presence of a chat window. Plan for it from the beginning and test the actual interface with users. MITRE’s Chatbot Accessibility Playbook, informed by a review of industry and academic literature and a small user study, provides five development “plays” and checklists for chatbot accessibility assessment and user research. It does not establish that a particular implementation complies with a specific jurisdiction’s law.
GOV.UK recommends keeping alternative ways to get help and giving customers a way to refer back to an exchange, such as a downloadable or emailed transcript. Tell users about the transcript option before the session and make its controls visible. Consider whether chat suits the customer’s context; it should not become the only route to support.
If the service stores personal data, determine the operating geography and data practices before making legal claims. GOV.UK points to GDPR obligations and ICO guidance, but whether a particular organization or deployment complies depends on its circumstances.
Consult MITRE’s Chatbot Accessibility Playbook for its accessibility-focused development guidance.
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Before launch, test whether people can complete representative tasks and whether the bot’s responses are accurate. After launch, review unsupported requests, feedback, outdated knowledge, repeated questions, abandonment points, and whether escalation resolves the issue. Keep the bot discoverable where customers need support without letting it obscure essential service information; test placement with users.
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Use outcome measures tied to the task
Choose measures that reflect the service goal rather than activity alone. Useful questions include:
- Did the customer complete the task?
- How much unnecessary back-and-forth did completion require?
- Was the bot easier, faster, or more useful than the relevant alternative?
- Could customers reach help when the bot was stuck?
- Did a handoff preserve enough context for the next person to resolve the issue?
Interpret usage figures carefully. Gartner’s August 2026 release says 58% of surveyed customers who use GenAI had used it to complete a task on their behalf; the figure was 74% among B2B users. It also reports that surveyed customers were approximately three times more likely to have used a third-party GenAI tool than a company chatbot in their most recent service interaction. These are survey findings, not universal usage rates or evidence that a given support chatbot improves service.
Should you build a bot or use a support platform?
The right implementation depends on the task, required integrations, knowledge ownership, accessibility, privacy, escalation, and ongoing evaluation. A platform can provide a conversational interface, but it does not decide what should be automated or guarantee accurate answers. Compare a build or purchase with simpler changes to content, search, or existing support workflows.
For broader design principles, a 2022 journal study by Geovana Ramos Sousa Silva and Edna Dias Canedo, with an arXiv record submitted in 2023, reviewed and coded 40 selected studies into conversational-design guidelines. That is a synthesis of published work, not a general performance guarantee for a particular design.
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Read the study record on arXiv.
Frequently Asked Questions
Should every support website have a chatbot?
No. First identify a customer task and compare a bot with improvements to content, navigation, search, or existing contact channels. Use a chatbot only when the conversation can help customers complete that task.
What should a support chatbot say when it doesn’t understand?
It should acknowledge the request, identify the specific uncertainty, ask one useful clarification or offer relevant choices, and provide a clear contact route if it still cannot help.
Can a chatbot be the only way to contact support?
The guidance here recommends keeping alternative ways to get help and making a human route or another contact channel available when the bot cannot resolve the issue.
How do you know whether a support chatbot is working?
Test task completion and response accuracy before launch. After launch, examine completion, unnecessary back-and-forth, unsupported requests, abandonment, repeated questions, feedback, and whether escalation leads to resolution.
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