A few years ago, it was call centers; now, it’s contact centers. Remembering how things used to be can almost make one feel nostalgic. Hunting for that toll-free number and preparing to be on hold for at least a few minutes (although it felt like much longer because of that annoying tune they played while they kept us on hold).
Customers associated call centers with long wait times, the same scripted conversations, and frustration at having to repeat the issue to several agents. However, we’ve now come a long way, which has changed how we think about customer service.
Generative AI’s Role in Automating and Redefining Customer Support
We can see how things have constantly been changing in the way companies address our questions. While there is no doubt that chatbots speed things up, they come with their own set of problems. Customers have reported being dissatisfied if chatbots fail to resolve their issues. This is obviously counterintuitive because while speed and 24/7 availability are important, it beats the purpose if they are of no real value to customers (even if it happens only occasionally).
According to the Zendesk Customer Experience Trends Report 2025, 73% of consumers will switch to a competitor after multiple bad experiences.
These insights are a strong indication that a few things need to change. We are seeing a shift from static scripts or keyword-based bots. Contact centers are now integrating generative AI (GenAI). These advanced language models can understand context, generate human-like responses, and help customers and agents address complex issues in real time.
According to Amandeep Singh, Practice Director and Principal Analyst at QKS Group,
“Generative AI is changing contact centers in a way we rarely talk about, it’s shifting the center of gravity from systems to people. It’s no longer about managing volumes, but about understanding context.”
In this article, we will explore how GenAI is transforming contact centers and its implications for the future of customer experience.
The Use of Generative AI in Contact Centers
Generative AI, or large language models (LLMs), are capable of generating and understanding human language. Some of the most popular models are GPT-4, LLaMA, and Claude.
GenAI, unlike traditional chatbots that use scripted decision trees, can respond based on conversation patterns, summarize interactions, infer intent, and learn from user behavior.
Which means that it can have a significant impact on how a contact center operates. GenAI can manage several tasks and processes, including:
- Addressing complex customer queries through conversation
- Helping agents with real-time suggestions and knowledge retrieval
- Summarizing calls, tagging issues, and reducing post-call work
- Translating, localizing, and adjusting tone as needed
In the 2025 study “Implementing Retrieval Augmented Generation Technique on Unstructured and Structured Data Sources in a Call Center of a Large Financial Institution”, Murtaza et al. demonstrate how this combination of retrieval and generation in contact centers gives GenAI the ability to pull accurate information and deliver it with human-like fluency.
Real-World Use Cases in 2025
This section explains how GenAI is already being used in live contact center environments:
1. AI Chat Assistants
Chatbots powered by GenAI now deliver comprehensive conversational support across web, app, and messaging channels. They are capable of recognizing nuances, managing ambiguity, and escalating issues only when required.
For instance, a financial services firm implemented a GenAI chatbot that handles 72% of tier-1 queries end-to-end, which led to customer satisfaction (CSAT) rising by 21%.
2. Agent Copilot Tools
GenAI can significantly help humans improve efficiency. AI can focus on facts and provide live transcription and response suggestions, making tasks easier for humans and allowing them to focus on empathy.
For instance, imagine you’re an agent who works at an online travel company. If a customer calls to register a complaint, you can access all the recent activity of the customer. In this case, you can see that the customer tried to book tickets, but the payment failed, and the money most likely got debited from the bank account.
Now the customer is concerned about two things: the money that got debited, and apprehension about the same issue recurring should they try to book the tickets again. Having all the information available in front of you will help you to identify what the issue is and ensure that the next booking is successful, while also reassuring the customer that they will get the refund soon.
You can also inform the customer that they’ve received loyalty points or credits as an acknowledgment that they have been inconvenienced. In other words, you try to compensate for their bad experience.
This is a perfect example of how humans and AI can work together to provide a good customer experience. While AI is responsible for collecting and presenting a centralized view of all the customer data, the agent can handle all the rest to improve customer experience.
3. Call Summarization & Auto-Tagging
GenAI can generate call notes, tag issues for future analytics, and feed CRM systems in a matter of seconds. This can reduce after-work calls by up to 40%, according to deployment case studies.
Business Impact
GenAI can deliver measurable improvements if implemented strategically. Its use in contact centers will help reduce average handle time (AHT) by providing better agent support. It can also help reduce agent attrition since it helps automate repetitive tasks and reduce stress.
Amandeep Singh also adds, “Agents are no longer buried in scripts or searching for answers, instead they are supported by systems that learn, adapt, and guide in real time. It’s a quiet (noise in marketing) shift, but it’s redefining what responsiveness and empathy actually mean.”
Limitations of GenAI
Despite its many benefits, it’s important to acknowledge GenAI’s limitations, which would help us determine when we need to be cautious and the extent to which we can trust it.
1. Hallucinations and Misinformation
Perhaps the biggest limitation to be aware of is how language models can still “make up” facts. To counter this, many enterprises are adopting Retrieval-Augmented Generation (RAG), where GenAI only responds based on trusted, real-time knowledge sources.
2. Bias and Compliance Risks
Contact center interactions may involve sensitive contexts, including race, gender, health, and money. GenAI must be carefully trained and tested for fairness, especially under regulations like the General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the Payment Card Industry (PCI).
3. Data Privacy and Customer Trust
Another valid concern is how LLMs can store or leak sensitive information. To stay compliant and protect customer privacy, enterprises must use sandboxed, zero-retention AI tools or fine-tuned private models.
The study by Guan et al. (2025) on health support bots highlights the importance of transparency and credibility to maintain trust.
The Need for Human & AI Collaboration
It appears that GenAI will not be replacing contact center agents. Rather, they will help them be more informed, responsive, empathetic, and productive.
GenAI will be more of a copilot, not a pilot. It manages repetitive tasks so that agents can focus on what machines continue to struggle with: judgment, empathy, and relationship-building.
Jagannathan’s 2025 research confirms this trend. Companies that combine human touch with AI augmentation see better outcomes, higher retention, and lower costs.
What’s Next: Autonomous, Omnichannel AI Agents
GenAI is showing rapid advancements; apart from answering questions, it will also proactively solve problems.
In the near future, we’re expected to see voice bots with emotional fluency, AI agents that can switch between text, voice, and video in real time, and predictive engagement, where AI will reach out even before you ask.
Final Take
In 2025, the way we look at and what we expect from customer service has changed. The focus is no longer speed; it’s empathy, relevance, and intelligence. Using GenAI for contact centers would help agents serve faster, understand better, and engage deeper. Businesses should make contact center interactions feel like a conversation, in a way that makes customers feel seen, heard, and understood.
To know more about Contact Center as a Service and related topics, click here.