Call centers are seeing gains but reliability and consistency need to be a focus. Adopting a copilot approach is the best way to ensure real efficiencies and positive customer experience.
It has been said that AI agents could be a multi-trillion dollar opportunity. Intelligent software agents capable of learning to manage actions and tasks have the potential to transform almost everything. Work and life will be impacted by the drive for productivity and efficiency. But AI agents will also democratize access and help overcome barriers to empower more people and drive innovation.
The road to agentic AI is still being built and there will be many routes to explore but, today, one of the biggest pushes is coming from the telecommunications industry. Call centers have been early adopters for this kind of generative AI because it is a natural evolution from bots. Existing chat features were interesting but they do not always work well, or customers were so annoyed by the menu systems on phones that they were unhappy before they even spoke to someone.
Moving beyond bots
Generative AI brings a much better experience to the call center structure, and it enhances existing technology. For example, Google Customer Experience Suite (CES) was built on its Contact Center AI and enhanced with generative AI technology. It has better engagement with customers in both the chat channel and live. With the emerging capabilities of large language models (LLMs) and the growth of companies like OpenAI, AI agents can take on expanded tasks.
Creating a multi-modal experience allows an AI agent like Google Gemini to intake text, visuals, and audio, and add a communications layer through actual voice and text-to-voice features that are extremely realistic.
Combining Gen AI, language features, the ability to understand a vast amount of context instantly, and better and more human communication with text-to-voice capabilities creates systems with huge potential.
Enhancing the AI agent
We have recently launched the concept of thinking models that are capable of handling much more complex tasks. This is achieved through reinforcement learning based on human feedback, which means these models can actually think, process, and approach problems from multiple angles and explore different paths to find the best solution. It is very reminiscent of how a human would work to solve a problem.
Agentic AI has the capability to not only understand what a customer needs but to communicate in our own language with the right nuances and even slang. Communication is there. The thought process is there. The ability of AI agents to think through problems at length is there. And they bring the ability to use tools during interactions.
For example, a customer calls in with multiple inquiries. The AI agent can quickly understand the intent of the call so there is no longer a need to sift through menus or listen to a bunch of options. Because the intent is read in the early stages of the call, the problem resolution process operates better, as the AI agent has the information to solve the problem and the tools to execute it.
The right AI team
After detecting the true intent of a customer call, a master AI agent can act as the interactive layer with a customer, while simultaneously accessing a team of subagents to delegate tasks. The subagents can specialize in different areas, like billing issues or new installations. There is no more waiting on hold to be transferred to a different department or a manager. The master agent can access a whole host of tools and know what it needs to take action.
For example, a customer may want to process a payment. The master agent can identify the request and decide how to proceed. It can give a credit, research a billing discrepancy, or initiate other searches to complete the request. It can call different APIs to get information, update the account, and process the bill. With access to tools, there is really no end to what an AI agent can do.
These reasoning capabilities and tools mean agents can do very similar things to humans. However, it is still early days in the process and there are concerns to be addressed. Reliability and consistency are two factors. The monitoring and evaluating are improving to help ensure the responses and decisions by AI agents are correct.
Improving the call center experience
We worked with one telco client to deliver better knowledge searching, to leverage LLMs to use new methods of data acquisition summarization. The goal was to make technical documentation more accessible so when someone calls to troubleshoot a modem, for example, the answer is readily available.
Call centers are also a common sales channel. Agents can provide additional information or offer specific deals. That requires the agent to understand the needs of the customer, align them with a product, make an offer, address objections, and close the deal. Now an AI sales agent can interview the customer to understand the needs and wants and match them with potential solutions. They can even address objections and concerns to help get to the sale.
Copilots: Finding the agentic balance
According to a recent Capgemini Research Institute report, being an agent is not an overly satisfying career choice, with only 16 percent of human agents surveyed report overall satisfaction with their roles. They face a number of pressures, from rising customer expectations to inefficient systems and a high attrition rate. There are efficiency gains to be made by employing AI agents that can help humans do a better job. In addition, AI agents can help resolve issues more quickly so the customer and employee have positive experiences.
This is why the copilot effect is a popular AI agent option. Google has Agent Assist to support live agents to resolve queries and issues more quickly. It is like having an expert in the room at all times with a call center agent. For example, the human agent can use it to help digest what is being said, with information automatically appearing on dashboards to assist with the call resolution. The copilot can also provide real-time assessments of the sentiment of the caller. Now the human agent has prompts with potential resolutions, rather than having to bounce between different systems for information or consult with a manager.
The human in the middle
So the concept of the human in the middle is very important. AI agents are a powerful tool meant to enhance experience, but sometimes a model can hallucinate or produce an error – and a company is responsible for an AI agent’s output. That means companies have to own the net result. So employing copilots with the human in the middle is happening even in new call centers. Once a system is proven, the role of AI agents can expand but, since call centers have a major impact on customer experience, there needs to be a high level of comfort with the system.
Call centers that use Google Customer Experience Suite (CES) engage customers with generative AI for many tasks, like determining what a client needs and other lower-level processes, to make calls more efficient and get to resolution quicker. AI agents can, for example, engage with back-office operations so humans can focus on more high-value tasks.
It takes time for companies to be comfortable with exploring generative AI solutions. Companies need to focus on the business case and ensure innovation results in efficiency and savings.
Working with Google Cloud, Capgemini can help companies move into the agentic future. We can help companies build a competitive edge with agents to drive real customer service transformation. Google Cloud’s advanced AI capabilities enable businesses to build and deploy intelligent virtual agents easily. It is time to create, frictionless environment to scale agents where everything supports the needs of the organization and its customers.
Join us at Google Cloud Next to discover how we’re helping companies embrace the agentic era and benefit from the intersection of innovation and intelligence.
