The Role of AI in Super-empowering Customer Service Agents
The Role Of AI Before, During & After Interactions Will Supercharge Agents, Not Replace Them
It is estimated that by 2026, over 80% of organizations will have used generative AI APIs, or deployed generative AI-enabled applications. Generative AI is a powerful tool and by integrating it alongside other technologies, such as other AI and automation functions, businesses will see valuable outcomes, but what does it mean for the customer experience (CX) landscape? While some may fear that AI systems are on the brink of replacing Customer Service Agents, a paradigm shift from agents to AI-enabled agents is not about replacement, but rather, augmentation.
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As with other forms of AI, such as self-driving cars which so far have amounted to little more than advancements in intelligent cruise control, AI in the contact center isn’t set to remove the human element, but instead, help them to focus on it. Generative AI specifically is supercharging agents, equipping them with powerful tools that enhance their capabilities, and plays a role before, during, and after customer interactions. Despite the growth of social and digital channels, which will also be strongly supported by generative AI, voice interactions remain popular and are an area that will be heavily influenced by the integration of generative AI to support more established AI and automation tools already in use.
To realize the benefit of how generative AI can support voice interactions, organizations need to focus on how it can play a role at every stage.
Before the Call: Pre-Call Proactivity for Customer Service Agents
AI in the contact centre starts to add value as soon as the customer initiates an interaction. Rather than spending time listening to repetitive, outdated, and poor-quality music while waiting, AI-powered interactive voice responses (IVRs) can capture customer data such as intent, demographic information, and geographic location to steer the customer to the most appropriate available agent.
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The captured information can be used on its own or can be integrated into a customer data layer such as a customer data platform (CDP) to identify previous interactions. This process within the IVR streamlines the call, ensuring that the customer is connected to the most appropriate available agent. At the same time, when the customer is connected to an agent, the agent will presented with information given by the customer and curated by generative AI to reduce average handling time (AHT) as the customer’s details are already located.
During the Call: Real-time insights
During the call, natural language processing (NLP) listens to the interaction and through its speech-to-text capabilities provides a transcription of the call to the AI. This in turn generates knowledge articles to help guide the interaction. The knowledge articles will provide information on screen with suggested answers and useful sources that can help with the customer’s query in real-time reducing the need for Customer Service Agents to take time searching for the same information.
The generative AI outputs presented to the agent can draw reference from multiple p*********** data sources from the organization, which helps to expand the agent’s own knowledge and reduces the time needed to train new agents. Automatic article sourcing and summarisation can further reduce the AHT as it eliminates the need for agents to look up information related to a customer’s query. Data transparency is vital to ensure agents are delivering the correct information. As such, it is important that agents can easily click through to find the source of the output to reduce hallucinations and increase accuracy.
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After the Call: Write off the Wrap-up
Customer Service Agents can spend up to 60% of their time in the post-interaction wrap-up, performing tasks such as updating CDP systems and creating a summary of the interaction. Generative AI can assist agents in this stage by transcribing, word-for-word, the entire call interaction using NLP. It can then use the transcription to create a summary of the call, create post-interaction reports automatically, analyze the sentiment and intent of the interaction, and even populate complaint forms and CDPs with relevant information. The agent is still involved in the process, but it saves the agent time as they only have to check the information is correct, and can compare the AI-generated data to the generated transcript as a source.
If generative AI could remove a third of the post-call processing time for each agent, this would amount to the equivalent of a 50% increase in headcount, without any added budget constraints or the need to train new staff. The extra time would also be used where it matters: serving the public and building trust in human interactions.
Efficiency Gains and Improved Experiences
Although AI, and specifically generative AI, is in its infancy, its impact will transform several industries. Within the CX space, leaders are already looking at how it can benefit their customers, agents, and organizations. Ultimately, as we’ve explored, it will be implemented across all stages of interactions and improve efficiencies. As a result, both customer and agent experiences will improve as AI takes care of typically admin-heavy tasks, that are vital to organizations that need to audit and monitor calls.
As we move into 2024, a responsible approach to generative AI will reap lucrative benefits and will be a top priority for all CX teams.
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