The pace of technological change sometimes feels breathtaking and relentless. Contact centers are undergoing a massive transformation thanks to the introduction of Conversational Artificial Intelligence (CAI) and Large Language Model (LLM) generative AI software like ChatGPT and OpenAI. Organizations are now trying to figure out how to keep up with the competition and provide callers with a better experience by adopting this technology.
These new AI capabilities provide exciting enhancements to the portfolio of tools for delivering excellent customer experience and satisfying self-service. But let’s consider this new technology in a historical context. The introduction of AI to contact centers is not unlike the advent of IVR so many decades ago—a technology capable of providing huge cost savings, but one that relies on an effective design that callers are willing and able to use.
When early speech recognition technology emerged, it was a refreshing alternative to button-pushing. However, the technology was also rather fragile. Applications had to be carefully crafted to direct callers how to use them successfully. They required patient and artful development, monitoring, analysis, and tuning. As a result, speech applications were significantly more expensive in both technology investments and development services.
Now, LLMs built on vast amounts of domain-specific transcripts offer more “conversational” interactions and promise to make creating a voice or chatbot easy for anyone. While these tools may facilitate a better caller experience, building a good CAI application is still an art coupled with skill. And, like the traditional IVR technology it’s meant to replace, it too is more expensive to implement and maintain.
The takeaway? Any good customer experience application requires art, effort, investment, and a substantial understanding of human factors. What the average caller is trying to do, and how they express their intentions, must be anticipated by the designer.
Choosing the best solution starts with an exploration of your organization’s goals. How many languages will be supported? Are self-service tasks complex or linear? What types of data need to be collected from the caller? Is the cost of implementing CAI justifiable? And, what is your organization’s tolerance for innovating and adopting change?
The key is not to dive headfirst into Conversational AI, but rather to plan carefully and choose an appropriate solution that meets the need. For some use cases, a CAI application may seem intriguing, but a well-designed directed dialog flow may serve callers equally well. Organizations should take a good look at the technology and integration capabilities they already possess, determine how existing resources can be leveraged, and ensure all of the right team members are on board with the solution.
Need help defining your self-service roadmap? INI has decades of experience adopting and working with new technologies. Contact us for an effective and appropriate solution to your customer experience needs.