How AI Is Transforming Self-Service
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Omnichannel self-service solutions are a requirement for organizations that want to deliver a consistently outstanding and cost-effective customer experience (CX). Conversational artificial intelligence (CAI)—intelligent virtual agents (IVAs), voicebots, chatbots, or just plain bots—can automate many tasks and inquiries that were previously handled by live agents, which reduces operating expenses.
But cost reduction is only one reason companies need intelligent self-service solutions; consumers are demonstrating a strong and growing preference to help themselves when self-service applications are up to the task.
Additionally, as the volume of voice and digital contact center interactions expands, businesses are finding it increasingly difficult to source and hire enough qualified people to fill their service departments. The way to address each of these items is by staffing contact centers and CX functions with an optimal mix of CAI/IVA solutions and live agents to handle their inquiries.
Intelligent self-service applications use several AI technologies, including machine learning, advanced speech technologies (e.g., natural language processing/understanding/generation [NLP, NLU, NLG]), deep neural networks, generative AI (genAI), and predictive analytics. These underlying capabilities enable CAI applications to simulate live, unstructured conversations in voice- and text-based channels, identify customer intents, recognize customer and agent sentiment and emotions, automate completion of customer requests, and escalate interactions they don’t yet know how to handle to their human counterparts. As sophisticated as CAI self-service solutions have become in recent years, the application of genAI is taking them to an entirely new level, improving their ability to understand and correctly respond to customer intents.
The first generation of AI-enabled self-service bots successfully automated many inquiries, but when confronted with undefined intents, the bot’s only option was to transfer the customer to a human agent to complete the interaction, even though the customer wanted to self-serve. GenAI greatly expands conversational understanding of unstructured input (e.g., questions) in these self-service solutions due to the large language models (LLMs) they leverage, better enabling them to provide an appropriate answer. GenAI also enhances and speeds up the development and testing process for self-service solutions, improving these applications from the outset. Although there is a tremendous amount of work yet to be done, genAI has already increased the operational capabilities of CAI solutions. This includes drafting concise and comprehensive summaries of self-service interactions and delivering them to a customer relationship management (CRM) solution or other servicing system so live agents know what has been done for a customer.
Today’s CAI solutions come with low-code/no-code development environments that allow nontechnical business users to build and implement them via graphical user interfaces (GUIs) rather than programming. These GUIs contain elements, objects, and application programming interfaces (APIs) that simplify the process of integrating them with CRM solutions, knowledge bases, and the communication components needed to create self-service workflows and automations. However, in the majority of CAI initiatives today, the vendor or a systems integrator (SI) builds the initial solution and then hands it off to the end user to maintain after training.
Omnichannel CAI/IVA solutions allow companies to build once and deploy in multiple channels, e.g., voice, email, chat, short message service (SMS), WhatsApp, and more. This reduces costs and enables customers to receive the same answer regardless of their interaction channel. When marketing and contact centers work together, CAI/IVA functionality is often deployed on the company’s website, facilitating standard responses and improving the CX.
Given the rapid speed of innovation in the self-service market, DMG recommends that all organizations assess their current capabilities, as most can be greatly enhanced with the new generation of genAI-enabled CAI solutions. However, companies considering an investment must be careful to select a vendor with proven experience in developing effective applications. Implementation success depends on the underlying technology and the platform’s performance and reliability; the vendor’s ability to build, deliver, and support the solution; and the vendor’s willingness to work closely with the organization. As good as these genAI-enabled applications are now, a major contributor to their ongoing value is the ability to identify new intents and automation opportunities. For this to happen, these solutions need to come with the tools and intelligence to capture and identify this data, as well as a design and development environment business users can easily update.