The ask is always the same: “I wish you’d know me better”, “I am counting on you to understand my situation”, “Answer me promptly, please!”, “I wish you’d resolve my problem right now!” – these have remained top consumer priorities, no matter the channel – stores, websites, contact centres, or email. Consumers already have access to standard solutions for common challenges on social media. And many often scour online or talk to friends and user communities to try and resolve issues before they contact support. That’s why, brands need to go that much further, try that much harder to create branded customer experiences that beat every other available option to turn these experiences of the brand into differentiators for the business. At the same time, businesses are always under pressure to keep cost-to-serve, across channels, at a low, while raising the bar for customer experience.
Brands have turned to AI, but answers elude
Most leading companies are in the process of or are already taking advantage of AI for their customer service operations. From virtual agents and automated answers to Frequently Asked Questions followed by AI-based agent assist tools to guide through query resolutions, the early attempts are promising. There are advances that have followed in the form of Generative AI and Retrieval Augmented Generation (RAG) techniques amplifying these tools. However, with the data sets used to train the AI usually being existing repositories of product documentation, internal Wikis and resolution databases, the results are often underwhelming. The lack of a rigorous approach to continually enhance this data set with newer resolutions and better solutions leave much to desire. The agent driven resolutions are also not always captured well, during the call closure process, and this means the data sets continue to lose out on reflecting any new potential learning from these daily interactions.
AI can be used to help AI work better for customer experience
We already know that when valuable insights, from previous customer interactions, do not feed into ongoing customer support programs, or the data sets that power customer experience tools, the results are far from optimal. AI can be used to create automatic resolution closing summaries. These AI-generated summaries accurately and efficiently capture resolution details: from the probing questions used, the system status at that time, consumer responses, the deductive process used by the agent and any steps taken, ensuring that nothing is lost in translation or by human oversight. The agents can approve these closing summaries after a quick review. This not only saves time for support agents but also provides customers with a clear and concise summary of how their issue was resolved, enhancing their overall experience.
The true value emerges when these captured resolutions are used to enrich existing data sets. By training the AI on this data, patterns and trends can be identified, potential problems predicted, and proactive solutions offered. This dataset becomes a valuable repository of information that can assist with customer issues. This not only improves the efficiency of customer support but also helps in creating more personalized and satisfying experiences for customers.
Additionally, the implementation of AI in customer support can also facilitate the continuous learning and development of support agents. With access to a rich dataset of past resolutions, agents can learn from previous experiences and improve their own problem-solving skills through simulated training. This continuous improvement cycle ensures that customer support teams are always equipped with the latest knowledge and best practices, leading to more effective and efficient resolutions.
Using recent advances in AI Agentic techniques, digital agents can, in the future, be provided access to system interfaces (APIs) to resolve common troubles, even before the consumer has realized that there are issues. We believe that a major subset of system related errors which cause customers to reach out to customer service will be resolved by digital agents in the future. Resolution datasets and digital agents are exciting developments that will drive significant value for customer service.
In conclusion
Businesses that embrace AI for capturing and closing resolutions are better positioned to enhance their resolution data sets and to use this for enhancing their customer delight, improving operational efficiency, and driving economic value. They can create a virtuous cycle of continuous improvements and greater customer delight. To close more effectively in the future, we must start by closing well today.
