The Dynamic Duo | Agents and AI
Artificial intelligence (AI) is at the forefront of technology in recent months and there is uncertainty about how it will shape our lives, the workforce, and the future. Generative AI at Work, a recent study from the National Bureau of Economic Research (NBER), analyzes how this new technology is changing the norms of work, education, and core institutions of the world. However, with this dynamic shifting, there is fear and lost opportunity in the customer service industry. ChatGPT and other large language model (LLM) programs have the potential to revolutionize customer service, contact centers, and other customer success and experience conventions. Generative AI has developed in the succession of a series of machine learning advancements and offers a new tool for the customer service industry to utilize.
LLM models can be “trained” on records of previous customer service interactions and correspondence. Mainly seen through the chat functions of customer service operations, the LLMs analyze two critical aspects of these conversations. They break down sentences into segments they can process and then assign a weight value to each segment. This value determines what problem a client or customer is experiencing and prompts the AI to search company data and records. It combs through recorded chats and notes what patterns arise in these cases, what responses elicit the best emotional responses from customers and different ways of aiding customers. The AI then suggests courses of actions to the agent, providing sample responses to use and pulling documentation and resources for the agent to use to solve the customer’s issue.
The NBER study analyzes the effects of AI on the performance of customer service agents managing chat-based requests. When this model is woven into the work of customer service agents, there is a noticeable increase in performance. Overall, average handling time decreases, resolutions increase, and customer satisfaction improves. Key metrics of customer service improve for all agents, but especially for new agents. This becomes noticeably valuable, given the high turnover rates of the customer service industry and in contact centers. AI eliminates lost productivity that comes in training new agents and optimizes the functionality of customer service operations. The teamwork of an agent and AI allows for new agents to learn quickly and reduce the consequences, costs, and headaches of attrition.
Despite the great advantages of this AI learning system, the returns of AI assistance dwindle with agents that have more experience. The study shows that because the system is sourced from professionals in customer service, once agents reach industry proficiency, the AI does not have as much impact on their performance. Experienced agents still had improved AHT and resolution rates, but they did not experience the significant jump that newer agents did.
Fears of whether AI can replace agents are misguided, as AI has mainly been tested within customer service chat functions. Phone calls, in-person services, and even chat support cannot be fully automated by AI. There is a need for human connection in many of these points of contact, and at least human management of AI assisting features. Additionally, AI does not possess the creative or adaptive faculty that agents do; rather it reflects the industry’s best professional practices. Therefore, the AI is based on machine learning of these agents, so AI cannot replace the source of its information.
It is more accurate to view AI not as replacement, but as enablement. A tool that enables customer service operations to reduce average call handle times, decrease attrition losses, and promote best practices to ensure better call resolution. A term that is commonly used is the augmentation of skills through AI. It will make agents faster, smarter and better. Moving forward, customer service will be affected by the power of AI, but human agents won’t be phased out by it. As the advent of any new technology for the workforce, there will be changes and adaptations to be made while firms capitalize on the AI tools emerging today.
In a competitive landscape where companies are increasingly committed to delivering high quality customer experiences, AI can help. Ensuring that clients are met with consistently satisfying customer service is crucial to branding and retention. A negative customer experience can have costly consequences, and having an AI-backed workforce helps mitigate the ebbs and flows of customer service.
In contact centers, generative AI may be utilized in similar ways to prompt, train, and improve agent performance. Along with Intradiem, whose intelligent automation technology unlocks efficiencies in training time, schedule adherence, and workforce productivity, call centers can drastically improve their operations, training efficiency, and attrition rates. Most importantly, Intradiem puts people first with its agent-focused call center solutions, and agents will utilize the unique technology of Intradiem to improve customer experiences and make clients—the people who matter most—top priority.
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