Generative AI in Call Centres: Use Cases, Benefits, and Best Practices

CallMiner takes a look at the top use cases, benefits, and risks for implementing generative AI, as well as best practices and practical tips to get started.

Generative AI is rapidly emerging as one of the most transformational technologies in call centres, revolutionizing the way customers communicate with virtual agents and how teams work to handle day-to-day tasks.

Instead of relying on keyword matching and decision trees to interpret human language, generative AI can understand context, adapt to the flow of a conversation, and produce natural, human-like responses in real time.

For agents, it means less time spent searching for information or typing out notes and more time focused on providing value. Customers experience more seamless interactions, faster resolutions, and more personalized experiences.

The potential use cases span every facet of call centre operations, from virtual agents handling routine inquiries to real-time agent assist tools, AI-driven call summaries, and automated compliance reporting.

What is Generative AI in Call Centres?

Generative AI is a type of machine learning that can generate natural, human-like responses based on the data it’s trained on.

In call centres, generative AI can analyze context in real time, understand the intent behind a question, and adapt to the conversation flow. For customer support, this means going beyond traditional decision trees or canned scripts.

A generative AI model can automatically parse the content of an interaction, generate a relevant answer, and even summarize the conversation for agents or supervisors. In contrast, traditional AI and chatbots are largely “rule-based.”

Keywords are mapped to canned responses, or a handoff is triggered when a request is outside their set of known answers.

This works well for simple and repeatable tasks (such as checking an account balance or resetting a password). However, it’s not very natural, and customers frequently feel like they’re just talking to a rulebook.

Generative AI for customer service is trained on massive amounts of data, so it can model language patterns and produce relevant output.

It can also handle nuance, ambiguity, and unexpected phrasing that would confuse traditional bots. This technology is now emerging in several types of call centre software. For example:

• Virtual agents can use generative AI to answer more complex customer questions, with no agent involvement
• Agent assist tools use it to provide real-time suggestions, compliance text, or empathetic language during live calls
• Post-call analytics platforms can use generative AI to provide concise summaries, flag risks, and recommend next steps

These tools all reduce manual workloads and help contact centres give customers faster resolutions and more natural interactions.

Key Use Cases of Generative AI in Call Centres

Generative AI touches every part of the contact centre, from the way customers interact with virtual agents to how supervisors manage compliance.

Below, we’ve highlighted key use cases across three main categories: enhancing customer experience, empowering agents, and driving operational efficiency

1. Enhancing Customer Experience

AI-Powered Virtual Agents For First-Level Inquires

Generative AI virtual agents can answer routine questions like balance inquiries, password resets, or order tracking without connecting to a human agent.

The virtual agent understands conversational language, handles requests for clarification, and can transfer to a human agent when needed, reducing friction.

Multilingual, Personalized Responses

With generative AI, call centres can instantly support multiple languages. The technology translates queries and creates personalized replies in real time, preserving nuance and tone. This enables consistent service across regions without the need for large multilingual teams.

Reducing Wait Times With Instant Query Handling

Generative AI can reduce wait times by instantly providing answers to simple questions so that customers are only routed to human agents when necessary.

As a result, overall handling times can be reduced, freeing up call centre agents to spend more time on high-value conversations or customers with complex needs.

2. Empowering Agents

Real-Time Agent Assist

Generative AI can also assist agents in real time while they’re on a call. The technology can predict responses, automatically pull up policy information or retrieve knowledge base articles.

Agents spend less time looking for information and more time on customer service and engagement, accelerating time to resolution and helping to ensure consistency.

Call Summarization and After-Call Documentation Automation

Generative AI can summarize calls to streamline after-call work. This includes the outcome of the call, next steps, issues raised, articles, searches and links, and important bullet points from the conversation.

Agents will only need to review the summary, making a few corrections, if needed, rather than keying in notes from scratch.

Training and Onboarding Through AI-Driven Simulations

Generative AI training simulations can provide a new agent with realistic scenarios in a sandbox environment so they can practice and build their confidence and skills prior to working with live customers. This streamlines onboarding and allows new hires to hit the ground running.

3. Operational Efficiency

AI-Generated Compliance Scripts and Reminders

Generative AI can provide agents with real-time prompts to ensure they meet compliance requirements during a call.

These reminders, such as required disclosures or security verification steps, reduce human error and help protect the organization from regulatory risk.

Generative AI QA Assistants That Auto-Draft Compliance Reports From Call Transcripts

Instead of supervisors manually reviewing transcripts, generative AI can auto-draft compliance and quality assurance reports directly from call data.

These draft reports highlight potential risks, summarize adherence to required policies, and free supervisors to focus on resolving issues rather than compiling documentation.

Dynamic Knowledge Base Generation

As policies, products, or customer issues evolve, generative AI can continuously update the knowledge base with new information pulled from conversations.

This keeps resources current, reduces outdated content, and ensures agents always have the latest guidance at their fingertips.

Benefits of Generative AI in Call Centres

Deploying generative AI in a call centre can lead to tangible benefits for both customers and agents.

Enhanced customer satisfaction and agent productivity, compliance and risk management, and cost reductions are some examples of the gains that generative AI can drive across service quality, employee performance, and the company’s bottom line.

Improved Customer Satisfaction

Generative AI leads to interactions that are faster, smoother, and more personal. Customers enjoy immediate answers to routine questions, individualized responses that take context into account, and fewer transfers to other agents or departments.

The reduction in friction creates trust and results in higher satisfaction ratings.

Higher Agent Productivity and Reduced Burnout

By automating tasks such as call summarization and policy research, generative AI gives agents more time for conversations that matter.

Call assist tools reduce cognitive load in real time, and AI-driven simulations speed up training. The result is increased productivity and a lower risk of burnout from monotonous, manual tasks.

Cost Savings Through Automation and Efficiency

Automating first-level inquiries, compliance documentation, and quality assurance reduces operational overhead.

Generative AI also improves forecasting and staffing decisions, which reduces wasted labour hours. Over time, these efficiencies translate directly into measurable cost savings.

Better Compliance and Risk Management

Generative AI monitors calls in real time, prompting agents with disclosures or verification steps and flagging potential issues for supervisors.

Automated QA reports expand oversight from a small sample of calls to every interaction, helping organizations catch problems early and reduce compliance risk.

Best Practices For Implementing Generative AI in Call Centres

Implementing generative AI in a call centre works best with a phased and methodical approach.

The objective is to capture quick wins without sacrificing accuracy, compliance, or the personal connection that customers expect. The following best practices can help organizations strike that balance.

Start With High-Volume, Repeatable Tasks

The quickest way to see value from AI is to focus on high-volume, repeatable transactions such as password resets, account lookups, order status inquiries, etc. Automating these processes relieves agents from routine, low-complexity work.

At the same time, it allows you to prove the technology’s accuracy and consistency before tackling more complex tasks.

Maintain Human Oversight on Sensitive Cases

While artificial intelligence systems are capable of operating various functions autonomously, human oversight remains essential for cases involving finance, healthcare, or legal issues.

For that reason, it’s important to keep humans in the loop so they can step in if a case requires escalation, nuanced handling, or when the customer simply wants to speak to a person.

Train AI on Domain-Specific Knowledge

Out-of-the-box generative AI models will not have the necessary accuracy to deliver quality responses for specialized industries.

They should be trained on data that is unique to your organization, including products, policies, and industry-specific terminology.

Audit Outputs to Ensure Quality and Compliance

AI is only as good as the data it’s trained on. Once the system is up and running, teams must review its outputs regularly to look for errors, biases, or other compliance issues, and make adjustments as needed. This helps ensure that service levels are maintained and all regulatory guidelines are being followed.

Balance Automation With Human Touch

Automation should never come at the expense of human interaction. Empathy and reassurance can only be provided by a human, so contact centres that want to be most effective will leverage generative AI to take over the mundane, manual tasks and allow agents to focus on those interactions that are best served with a human on the other end.

Challenges and Risks (And How to Avoid Them)

Generative AI offers obvious benefits, but there are also new challenges to be overcome by call centres and customer service agents.

From data security to over-automation, having a strong sense of the associated risks will allow your organization to deploy the technology in a safe and responsible manner.

Data Privacy and Security

Generative AI processes large quantities of customer data for training and storage, creating potential for leaks or misuse if not secured properly.

To avoid this risk, ensure your generative AI solution has end-to-end encryption and data controls in place, such as access permissions based on employee roles, data retention policies, etc. Confirm the solution complies with relevant regulations, such as GDPR, HIPAA, and PCI-DSS.

Risk of Inaccurate or Biases Responses

AI models can sometimes generate incorrect or biased information, particularly if they were trained on data that was unrepresentative of their intended use case. This can have serious trust and compliance implications in a customer support context.

To avoid this risk, retrain AI models on a regular basis with a wide variety of data that includes domain-specific content.

Consider testing for bias and incorrect content regularly, and plan human review interventions for high-stakes content.

Frustration From Over-Automation

AI automation in IVR systems can increase efficiency, but it also risks creating a frustrating customer experience or dead-end loops without human agent access.

To overcome this challenge, ensure that workflows have a seamless human handoff. Pay close attention to customer feedback and adjust the automation boundaries when customers indicate that they prefer human intervention.

Integration With Legacy Systems

Call centres may be still using legacy systems not designed for AI. Integrating generative AI into existing CRM, telephony, or workforce management tools may be a time and resource-intensive process.

To overcome this challenge, prioritize using API-based or modular integrations when available. Use pilot programs to test the integrations before going to scale and plan for staged rollouts to minimize business disruption.

Tips For Successful Generative AI Implementation in Call Centres

Successful adoption of generative AI requires a carefully planned rollout addresses real business needs, fits within existing systems, and keeps people at the centre of the process. The following tips can support effective implementation.

Focus on Key Pain Points

Assess where the biggest opportunities lie for your call canter team. Do long wait times, high agent turnover, inconsistent compliance, low customer satisfaction scores or other issues top the list? Tackling these core challenges with the right AI solution early on will help demonstrate value quickly.

Choose Integrated, Scalable Platforms

Choose AI tools that can scale with your organization and integrate with existing CRM systems, telephony, and workforce management tools. Building a single, shared AI foundation will help avoid data silos and accelerate adoption by different teams.

Pilot Programs First

Validate performance and measure impact through controlled pilot deployments before committing to a full rollout. Pilots allow time to fine-tune workflows and secure buy-in from agents and supervisors who will be using the technology every day.

Invest in Agent Training

Training will be crucial for agents to effectively leverage generative AI. Help them understand how to interpret and use AI responses, balance automation and empathy, and when it’s necessary to escalate. Refreshers will help maintain confidence as generative AI capabilities evolve.

Establish Continuous Feedback Loops

AI performance improves with monitoring and feedback. Collect insights from agents, supervisors, and customers to identify gaps, then feed that data back into model tuning. A continuous improvement cycle ensures AI stays accurate, compliant, and aligned with business goals.

This blog post has been re-published by kind permission of CallMiner – View the Original Article

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Call Centre Helper is not responsible for the content of these guest blog posts. The opinions expressed in this article are those of the author, and do not necessarily reflect those of Call Centre Helper.

Author: CallMiner
Reviewed by: Rachael Trickey