Talk about change! As we approach the two-year anniversary of the announcement of OpenAI’s launch of GPT-3.5, conversational AI has been reinvented to incorporate generative AI (genAI) to take advantage of the many ways that this technology can make self-service applications smarter.

Previously, the conversational AI tools used to create chatbots and intelligent virtual agents (IVAs) required specific training for every interaction, including identifying the many ways someone might ask any question that the system was set up to handle. Conversations had to flow in a very specific order, as the systems were very limited in their ability to switch between topics without a great deal of very specific training and guidelines. It took a lot of work to build applications that were often disappointing to users.

GenAI significantly shortens the development time for applications while creating much better user experiences, replacing stilted and awkward conversations with comfortable, almost human interactions. This has a revolutionary impact on the chatbots and IVAs built with conversational AI systems that utilize generative AI. New chatbots can provide much more information to customers and deliver it in a comfortable, conversational manner. These are early days for genAI-driven conversational AI solutions, but early results are impressive and the potential is off the charts. My latest report, The State Of Conversational AI, looks at where conversational AI is today in this crazy, fast-moving market moment.

While the report looks at conversational AI across several areas, most readers of my blogs are focused on customer service, so I’ll spin this blog in that direction. Here are some of the key findings in the report that customer service leaders should pay attention to as they consider adding conversational AI to their contact center.

  1. Prioritize Customer Experience Over Cost Savings

While the cost benefits of automation are undeniable, focusing solely on cost reduction can undermine customer loyalty. The report emphasizes the importance of balancing efficiency with customer satisfaction. When customers can use self-service to get quick answers to simple questions and agents are available to help tackle the hard stuff, everyone wins.

  1. Implement Robust Guardrails For Safe AI Interactions

Safety and reliability are paramount when deploying conversational AI. The report highlights the need for guardrails such as retrieval-augmented generation and finely tuned large language models to ensure that AI interactions are secure and trustworthy. This enables applications such as “infrequently asked questions,” where knowledge bases, or even a set of PDFs, can provide answers to many customer questions without needing to predefine them. This creates solutions that are fast to build, useful for customers, and reasonably safe from hallucinations, since all answers must come from a specific data source.

  1. Drive Positive Customer Experiences With Transaction Workflows

Self-service applications that answer customer questions are helpful, but without the ability to connect to back-end systems, a chatbot or IVA is of limited value. If you can’t check on the status of an order, schedule an appointment, or make a purchase, automation will fall short in customers’ eyes. Effective management of transaction workflows is essential to deliver positive customer experiences.

The state of conversational AI is at a pivotal juncture, offering unprecedented opportunities for customer service and customer experience leaders. By embracing generative AI, prioritizing customer experience, implementing robust safety measures, future-proofing self-service offerings, and managing transaction workflows effectively, organizations can unlock the full potential of conversational AI.