Generative AI in a contact centre today is mostly used behind the scenes to support agents rather than replace them, summarising calls automatically, surfacing the right knowledge article mid-conversation, and drafting replies an agent then reviews and sends. These lower-risk, agent-assist uses tend to deliver real value faster than customer-facing chatbots, which still need careful oversight before they can be trusted with sensitive or complex enquiries.
Why Start With Agent-Assist Rather Than Customer-Facing AI?
The appeal of a fully automated AI chatbot handling customer enquiries end to end is obvious, lower cost, instant response, no staffing to manage. The risk is equally obvious once you think it through: a generative model can produce a confident, well-written answer that is simply wrong, and a customer has no easy way to tell the difference between a correct answer and a fluent mistake.
Agent-assist tools sidestep much of this risk because a trained human stays in the loop. The AI drafts, suggests or summarises, and the agent decides whether to use it, edit it, or discard it entirely. This keeps the judgement where it belongs while still saving real time on the repetitive parts of the job.
How Are Call Summaries Actually Used?
After-call work, writing up notes on what happened, what was resolved, what needs follow-up, is one of the most time-consuming parts of an agent's day and one of the least visible to customers. Generative AI can draft a first-pass summary directly from the call transcript, which the agent then checks and adjusts rather than writing from scratch.
What This Actually Saves
The time saved per call is modest on its own, but multiplied across hundreds of calls a day across a team, it adds up to meaningful capacity that can go into handling more enquiries or spending more time on the calls that genuinely need a human touch. It also tends to produce more consistent notes, since the AI applies the same structure every time rather than depending on how rushed an individual agent feels that day.
What Does Knowledge Search Look Like in Practice?
Large contact centres, especially those supporting several clients or product lines, often have knowledge bases with hundreds or thousands of articles. Finding the right one mid-call, while a customer is waiting, is genuinely hard for a human to do quickly using a traditional keyword search that misses articles phrased differently from the search terms.
Generative AI tools built for this surface the relevant article, or a synthesised answer drawn from several articles, based on the actual conversation happening in real time. The agent still reviews what is suggested before repeating it to the customer, but the search itself becomes far faster and more forgiving of how the question was actually asked.
Keeping the Knowledge Base Accurate
This only works well if the underlying knowledge base is kept current. An AI tool pulling from outdated articles will confidently suggest outdated answers, which is arguably worse than no suggestion at all, since it looks authoritative. Centres using this well tend to pair it with a disciplined process for reviewing and retiring old articles.
How Is AI Used to Draft Replies?
For email and chat channels in particular, generative AI can produce a first draft of a reply based on the customer's message and the relevant knowledge, which the agent then edits for accuracy and tone before sending. This is different from a fully automated chatbot response because a human still reviews every message before it reaches the customer.
- Speed, drafting from scratch is slower than editing a reasonable first attempt, particularly for common enquiry types.
- Consistency, AI-drafted replies tend to follow a consistent structure and tone, which helps newer agents in particular.
- Language support, a draft can be generated in the customer's preferred language and checked by a fluent agent, which is useful for less common language pairs.
Where Does Real-Time Agent Coaching Fit In?
Some tools use generative AI to give agents live suggestions during a call itself, flagging when a required disclosure has been missed, suggesting a next best action, or nudging an agent when a customer's tone suggests rising frustration. Used well, this functions as a quiet coach sitting alongside the agent rather than a script forcing specific words.
The Line Between Helpful and Distracting
There is a real risk of this becoming more disruptive than useful if the suggestions are too frequent or poorly timed. The best implementations are restrained, flagging only what genuinely needs attention rather than commenting on every sentence of the call.
Where Should Businesses Be Cautious?
Fully automated customer-facing generative AI, a chatbot answering complex account or billing questions without any human review, is where the risk profile changes considerably. Errors here reach the customer directly, and mistakes involving personal data raise real data security questions that need to be thought through carefully before deployment, not after a problem surfaces.
Our broader look at AI in the call centre covers where the technology is genuinely ready today and where it still needs a human safety net. For most businesses, the more sensible path is agent-assist first, proving out the technology on lower-risk, internal-facing tasks before extending it further.
What Should Businesses Weigh Before Adopting These Tools?
- Data handling, understand exactly where customer data goes when it is processed by a generative AI tool, and whether this meets your obligations under Singapore's data protection rules.
- Human review points, be clear about which outputs are reviewed by a person before reaching a customer, and which are not.
- Accuracy monitoring, track how often AI-drafted content needs significant correction, since this tells you whether the tool is actually saving time or just moving the work around.
- Agent training, agents need to understand these are drafts and suggestions, not answers to repeat blindly.
Used thoughtfully, generative AI removes a meaningful amount of repetitive work from a contact centre without asking customers to trust an unsupervised machine with their problem. That balance, rather than full automation, is where most of the practical value sits today.
How Does This Change the Agent's Role Over Time?
As generative AI takes over more of the repetitive, low-judgement parts of an agent's workload, the agent's role shifts toward the parts that genuinely need human judgement, handling emotionally charged calls, resolving unusual edge cases, and making decisions that a script or an AI suggestion cannot responsibly make alone. This is a meaningful shift in what makes a good agent, moving further from someone who can recite information accurately toward someone who can genuinely read a situation and respond with judgement.
This has real implications for hiring and training. A contact centre adopting generative AI tools well tends to invest more, not less, in the human skills that AI cannot replicate, active listening, empathy, sound judgement under pressure, because these become the differentiator once the repetitive work is handled by the tools. Our piece on what makes a great call centre agent covers these human skills in more depth, and they matter more, not less, as AI tools take on a larger share of the workload.
Avoiding Deskilling
There is a genuine risk worth naming here. If agents lean too heavily on AI-drafted content without genuinely reviewing it, their own judgement can atrophy over time, and errors slip through unnoticed. Centres that manage this well build a culture where AI suggestions are treated as a starting point to be checked, not an answer to be trusted blindly, and where agents are still expected to understand the reasoning behind a suggested response, not just approve it.
How Should a Business Choose Between AI Tools and Vendors?
The market for contact centre AI tools has grown quickly, and not every tool marketed as generative AI delivers genuine value. Businesses evaluating options should look past the marketing language and ask specific questions: what data does the tool train on, how is customer data protected, how well does it integrate with existing systems, and what does an actual pilot show in terms of time saved and accuracy achieved.
A cautious, staged rollout, testing a tool on a limited set of use cases before expanding it across the whole team, tends to reveal problems early and avoids the cost of committing fully to a tool that does not perform as promised once it meets the full complexity of real customer conversations.
Frequently Asked Questions
Is generative AI in a contact centre mostly about chatbots?
Not really, at least not for the lower-risk, higher-value uses today. Most practical adoption is in agent-assist tools such as call summaries, knowledge search and reply drafting, where a human agent still reviews the output before it reaches the customer.
Can generative AI replace human agents in a call centre?
For most enquiry types, no, not without meaningful risk. Generative AI can produce confident but incorrect answers, so keeping a trained human in the loop for anything customer-facing remains the safer and more common approach today.
How accurate are AI-generated call summaries?
Generally quite good as a starting draft, but agents should still review and correct them rather than accepting them automatically. Accuracy also depends heavily on call audio quality and how clearly the conversation was structured.
Does using generative AI tools raise data privacy concerns?
It can, depending on how the tool processes and stores customer data. Businesses should understand exactly where data goes when using these tools and ensure this aligns with their obligations under Singapore's Personal Data Protection Act.
What is the easiest way for a contact centre to start using generative AI?
Most contact centres find agent-assist features, such as automatic call summaries or knowledge base search, the easiest and lowest-risk starting point, since a human agent remains in control of every customer-facing interaction.
If you would like an honest, practical view on this for your own business, get in touch via Connect Centre Group's contact page.