While Artificial Intelligence (AI) in customer service might have once been a futuristic concept, it is now an actual reality transforming the entire field of customer service. Whether we’re talking about large language model-powered, fully automated AI agents working on the frontlines of customer service or internal AI systems assisting customer service representatives, the technology itself is no longer a limitation.
The customer service problem
Though the technology is no longer a limitation, many customer service organizations still operate with outdated systems, that fail to meet the needs of modern customer service. I could bet that the primary source of knowledge about customer interactions isn’t documented anywhere—unless we count the handwritten notes kept in the personal notebooks of each service agent. Trust me, I’ve been there with my notebook. The value provided by customer service relies heavily on the human service representatives, particularly their experience and ability to understand customer needs, resolve issues, and add a personalized touch to every interaction.
Though this reliance on human representatives is not catastrophic, it does present a challenge especially when business managers seek more in-depth analysis of what’s happening in the customer interface. The company’s data team would likely need extra hands just eyeballing those notebooks, in their mission to extract the necessary insights.
Leveraging AI to Transform the Customer Service
The power of AI spiced with digitalization, changes this dynamic completely. The omnichannel customer service approach with Dynamics 365, as discussed by my colleague Matti in the previous blog post, addresses this challenge for you. First, the interaction data from all channels—whether we’re talking about emails, chat, phone calls, or support tickets—is seamlessly stored in the Dataverse. This centralized data repository ensures that all the valuable knowledge is now available—not just in the notebooks of service agents, but as structured data within your company’s database.
And this is where AI capabilities come into play. Customer service is now a valuable resource of data about your customers, which can be further leveraged in the decision-making process. Azure and Dynamics 365 offer a suite of tools that enable your business to automate customer service operations and gain in-depth insights from customer interaction data. Whether were talking about text analytics to understand the intents of customer interactions, extracting sentiments and key phrases, providing capabilities for conversational language understanding, analyzing documents, or leveraging LLMs, Azure provides these services to analyze customer interactions at scale. Not to mention, when connecting Copilot Studio with Azure AI capabilities, you unlock the “Terminator” of outdated customer service systems.
Artificial Intelligence in action
To bring something concrete to the table, let’s think of an imaginary example of scenario of a customer complaint interaction within the voice channel. While the conversation is happening, the built-in capabilities are converting the voice-channel data into transcriptions in real time (which by the way, is a task that requires AI). These transcripts can then be sent into Azure for enrichment and analysis.
In the figure above we can see how Named Entity Recognition (NER) identifies key information like the customer’s name, organization, order number, and relevant dates. At the same time, Intent Recognition detects the primary reason for the call (Delivery Status Inquiry) and a secondary intent (Request for Expedited Delivery). Meanwhile, Sentiment Analysis flags the conversation as negative due to phrases like “really frustrating” and “ensure that it’ll be delivered tomorrow,” indicating the urgency of the request.
The results of this data enrichment are added to the interaction metadata, enabling businesses to connect previously unstructured communication data to existing systems, like the order management system. When aggregated and analyzed across all interactions, this enriched data gives much broader understanding of the trends and common issues that customers are facing. When the same scenario is considered from an automation perspective—understanding human voice, fetching data from the order management system, checking the status of the order, and eventually replying to the customer regarding that status—it’s all there. With a custom copilots connected into the business knowledge sources such as the order management system, the process can be automated seamlessly. Figure shown below.
Closing thoughts
Enough of the AI hype—we’ve all been there. When chatting or calling customer service, and we realize we’re not talking to an actual human representative, frustration sets in. We instinctively try to get a human on the line, believing they would better understand our problem. But I challenge you—give the bot a try. It might not work perfectly due to limited information, but if you’re thorough, you might be surprised at how far AI has come. In fact, you could get your issue resolved faster than waiting in line for a human representative.