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Why "responsible" is a practical word
Adopting AI in customer-facing work is not only a technology decision; it is a decision about how your business treats people. Responsible use reduces the chance of misleading a customer, exposing personal data or damaging trust. The steps below are practical rather than philosophical.
Know what AI is good and bad at
AI models are good at drafting text, summarising long threads, classifying messages and answering questions from a defined body of information. They are less reliable at exact numbers, policy exceptions, legal or medical advice, and anything that requires accountability. They can produce confident answers that are wrong, sometimes called hallucinations.
Ground answers in approved information
An assistant that draws on a curated knowledge base of your prices, policies and frequently asked questions is more likely to give accurate answers than one relying on general knowledge. Keep that material current, and decide what the assistant should do when the answer is not in it: say it does not know and offer a human.
Be transparent
Customers should not be misled into believing they are talking with a person. Some jurisdictions and platform rules are moving toward requiring disclosure, and honesty is good practice anyway. A simple line such as "You're chatting with our virtual assistant; a team member can take over at any time" sets expectations.
Keep a human in the loop
Define when an assistant must hand over to a person: complaints, cancellations, requests involving refunds or safety, emotional distress, or a customer asking for a human. Ensure the person receives the conversation history so the customer does not have to repeat themselves. For higher-stakes content, such as public review replies, have a human approve drafts before they are posted.
Protect personal data
- Share only the data the assistant needs to do its job.
- Understand where your provider processes and stores conversations, and whether content is used to train models.
- Avoid pasting sensitive customer information into tools that are not approved for it.
- Reflect AI use in your privacy notice.
- Apply your usual retention and deletion rules.
Guard against bias and inconsistency
Outputs can reflect biases in training data, and the same question may receive different answers on different days. Sample conversations regularly, look for patterns of unhelpful or unfair replies, and adjust instructions and source material.
Test before launch and monitor after
- Write twenty real customer questions, including awkward ones, and test the assistant against them.
- Include edge cases: angry customers, out-of-scope requests, attempts to make it break its rules.
- Confirm handoff works and notifies the right person.
- Launch to a small audience or limited hours first.
- Review transcripts weekly at first.
- Keep a simple log of errors and the fixes made.
- Set a review date to decide whether the assistant is worth keeping.
Frameworks worth knowing
Organisations such as NIST publish the AI Risk Management Framework, which describes how to govern, map, measure and manage AI risks. It is written for organisations of all sizes and is a useful reference even if you only use off-the-shelf tools. See our sources page.
Where it fits
Responsible AI is a discipline that applies across the tools described in AI and business automation and conversations and customer communication. For a launch plan, use the AI assistant launch checklist.
Keep learning
- AI & Business AutomationLearn what AI chat, voice assistants and agents can and cannot do, and how to use them responsibly.
- Conversations & CommunicationLearn how email, text, phone, chat and social messages can be managed as one conversation per customer.
- Reputation & ReviewsLearn how to request, monitor and respond to reviews and feedback in an honest, consistent way.