AI customer service uses software to answer customer questions, record requests and route problems to the right person, so that support staff spend their time on the cases that need them rather than on the same questions repeated all day. It can work on written channels such as website chat, WhatsApp and email, or on phone calls through voice agents. These are different products with different strengths, and choosing between them is the first decision to get right.
This guide is for businesses running a call centre, a small support team or an after-hours answering service. It compares chat and voice, explains how AI fits with your CRM, and sets out how to move part of your call volume to chat without making customers feel pushed away.
What can AI handle in customer service today?
AI handles customer service work well when the question has a clear answer that already exists somewhere in the business. It struggles when the answer depends on judgement, negotiation or reading emotion.
Handled well: order and booking status when connected to the right system, opening hours, policies, product details, how-to questions, appointment changes, and collecting the details of a problem before a person picks it up.
Better left to people: complaints, refunds and compensation, anything involving vulnerable customers, disputes, and any promise outside your published policies.
The best results come from designing the split deliberately: deciding in writing what the AI answers, what it collects and passes on, and what it hands over immediately.
Is AI chat different from an AI phone agent?
Yes. Many searches for an AI receptionist or AI call centre are looking for something that answers phone calls. Chat agents and voice agents solve overlapping problems in different ways.
Chat agents work on website chat, WhatsApp, Facebook Messenger, Instagram and email. They are strong at detail and records: sending links, confirming bookings in writing, handling several conversations at once, and leaving a complete transcript.
Voice agents answer phone calls and speak. They suit customers who prefer to ring and issues that are faster to talk through. They bring considerations of their own, including call quality, accents, and consent to record.
A simple way to decide: look at where your contact volume comes from today. If most customers already message you, start with chat. If most ring, and their questions are routine, a voice agent may be the better first step. If it is mixed, chat is usually the easier place to begin, because it is simpler to test, review and correct.
InCard’s customer care agents work on written channels as standard, with a voice bot available depending on the solution. In practice, many businesses use chat to absorb a share of routine contact so that their phone lines and staff have more room for calls that genuinely need a conversation.
How can chat take load off a call centre?
A meaningful share of calls in most contact centres are questions that could be answered faster in writing. Moving those to chat shortens queues for everyone, including customers who still prefer to ring.
The approach that works is to offer chat as a quicker option, not to force it. Typical touchpoints: a message on the phone line’s waiting announcement pointing to website chat for common questions, a chat button on the pages people visit before they call, and an SMS or email link sent to customers who abandon a call in the queue.
To find out which calls could move, review a sample of recent call reasons. Questions about status, hours, policies and simple changes are the usual candidates. Billing disputes, complaints and complex problems stay with people.
Measure the effect by watching call volume for those specific reasons over the following weeks, alongside customer satisfaction, rather than overall call volume, which moves for many other reasons.
Can AI replace an after-hours answering service?
For written enquiries, an AI agent can cover most of what an after-hours answering service does, often with more detail and a complete record. For phone calls and genuine emergencies, a human or voice service is still the right choice.
An after-hours answering service typically takes a message and passes it on. A chat agent can go further on written channels: answer the question from your own information, collect structured details, book a time to call back, and give the customer a confirmed next step instead of a promise that someone will be in touch.
Where urgency is possible, such as a facilities fault or a health concern, the agent’s job is to recognise it and follow an escalation path you define, never to reassure the customer that it can wait. Write those rules down before launch and test them.
How much does AI customer service cost compared with an answering service?
The two are priced differently, so a fair comparison looks at what each costs for the volume you actually have.
Human after-hours answering services are commonly priced per call, per minute or as a monthly package with a call allowance. Cost rises with volume, and the service usually takes a message rather than resolving the request.
AI chat services are typically priced as a monthly subscription, sometimes with limits on conversations, channels or seats, and occasionally with usage-based charges at higher volumes. Cost rises more slowly with volume, and the agent can answer the question and book a follow-up rather than just record it.
To compare, take a typical month: how many after-hours contacts arrive, how many are written versus calls, and how many are routine. Price both options against those numbers, then add the time your team spends maintaining the knowledge the AI answers from. Many businesses keep a human or voice service for urgent calls and use chat for the rest.
How does AI customer service work with your CRM?
Connecting the AI to your CRM is what turns conversations into usable customer records rather than a separate pile of transcripts.
With a connection in place, the agent can recognise a returning customer, see previous interactions and purchases, log each conversation against the right record, create a ticket or task for follow-up, and update fields such as the reason for contact. Your team then sees one history, whether the customer spoke to the AI, emailed or rang.
Ask any provider exactly what the agent can read, what it can write, and which actions need human approval. A well-designed connection gives the agent the minimum access it needs. For background on how AI changes the CRM itself, see AI CRM software.
What makes a good handover from AI to a person?
Handover is where many AI customer service projects lose customer trust. The customer explains everything to the AI, then has to repeat it all to a person.
A good handover has three parts. The AI tells the customer a person is joining and roughly when. The person receives a short summary: who the customer is, what they asked, what the AI already said, and what is still unresolved. And once the person joins, the AI stops replying in that conversation.
Outside staffed hours, handover becomes a commitment: the AI records everything needed, agrees a time for follow-up, and puts the case at the top of the morning queue. The customer should always know their request was received and what happens next.
How should you measure AI customer service?
The number of conversations the AI handled is easy to report but says little about whether customers are better off. Track a small set of measures that connect to outcomes.
First response time, split between staffed and out-of-hours periods.
Resolution without handover, and alongside it, the rate of customers who come back with the same question, which shows whether those resolutions were real.
Handover quality: how often staff have to ask the customer to repeat information.
Customer satisfaction on AI-handled conversations compared with human-handled ones.
Unanswered questions: the list of things the AI could not answer, reviewed weekly and used to improve the knowledge it draws on.
What should Australian businesses check before choosing a provider?
Customer service conversations contain personal information, so the choice of provider is also a privacy decision.
Under the Privacy Act 1988 and the Australian Privacy Principles, you remain responsible for how customer information is handled, including when a provider stores or processes it overseas, which Australian Privacy Principle 8 addresses. Ask where conversations are stored, which third parties process them, how long they are kept, who can access them, and how a customer’s request to access or delete their information would be handled.
Also ask which channels connect through each platform’s official business connection and which are still being assessed, since messaging platforms set their own rules on when and how businesses can contact customers. For advice on your specific obligations, consult a qualified privacy adviser.
How do you introduce AI customer service without upsetting customers?
Customers rarely mind AI support. They mind support that cannot solve their problem and will not let them reach a person.
Be clear that the customer is talking to an AI assistant. Always offer a way to reach a person, and make it easy to find. Start with the questions that have clear answers, and expand only when those run well. Keep your knowledge up to date, because an AI repeating last month’s policy creates more contacts, not fewer. And review real conversations every week, especially the ones that ended badly.
For a wider introduction to what support chatbots do well and where they fall short, see AI customer support chatbots.
To see how InCard’s customer care agent handles routine questions and after-sales follow-up, hands over with full context, and can be switched off instantly, visit the AI Customer Care page.
If you would like to test it against your own support questions, book a demo and bring a sample of recent enquiries.
