Customer satistfaction scores go up with Voice AI deployments

September 13, 2024

Customer satistfaction scores go up with Voice AI deployments.

We're seeing what @omooretweets writes about here, in real-world Voice AI deployments.

Today's customer support operations are constrained on three axes:
1. staffing availability
2. access to the right information at the right time
3. process and technology

Voice AI provides step-function improvements in 1 & 2 , and gives companies an opportunity to solve 3 in high-leverage/high-ROI ways.

It's impossible to have enough humans on call to manage peak support volumes. (Staffing availability.) So if you can only call your health insurance support line after you get off work, for example, your wait times are going to be pretty long. Voice AI agents scale in a way that a human staff can't. It's hard to over-state how big a benefit this is for quality of customer experience.

I'm down in the trenches doing these deployments, so I see the early customer satisfaction numbers that directly compare Voice AI agent experiences to human agents. Voice AI agents are already very good, beating human agents at a wide range of tasks. But even if you think you'd always rather talk to a human, or don't think a Voice AI agent is a good fit for specific support contexts, being able to deploy auto-scalable Voice AI agents that can handle common tasks will massively improve the general customer experience by reducing wait times overall.

And why are Voice AI agents performing so well in tests and early deployments?

A big reason is the ability of LLMs to make use of large amounts of semi-structured data, quickly. You can pull all of a customer's account records into a Large Language Model's context (maybe with a little bit of contextual/RAG filtering) and get immediate, accurate answers to questions that previously required a human agent to go step-by-step through complex records. (Access to the right information at the right time.)

In fact, part of the bottle-neck that makes it hard for human agents to do their jobs is that the backend systems they use often require clicking around through multiple screens and tools. In the worst case, different agents have access to different databases. (Process and technology issues.)

If this were easy to fix, it would already have been fixed. But LLMs allow these systems to be improved much more cheaply than before, and provide much more value on the back side of investments in process and tech improvement.

LLMs are very good "adapter layers" that can sit on top of older systems. This is useful for both Voice AI agents and human agents! It's often relatively simple now to create a unified knowledge tool by pulling data from multiple systems and doing prompt-engineering work to make a SOTA LLM the interface that both Voice AI agents and human agents use to query and understand the data in legacy back-end systems.

This is a big, big change in how enterprise software is built. Forward-looking companies are investing in these tools today and seeing positive initial results.

In my experience, almost all businesses — large and small — want to provide terrific customer support. It's going to seem normal pretty soon to talk in an open-ended way to a helpful AI whenever you call a customer support line or a small business.

Olivia Moore@omooretweets

Leading models have largely solved latency. In fact, you’ll soon have to add pauses so the agent sounds more human!

In the next year, I predict people will be thrilled to make a call and get an AI (if they can even tell) - issues will get resolved faster and with less stress.

You can try talking to a Voice AI here. The default prompts are tilted towards "fun," but try writing a simple prompt that defines the role of a customer support agent.

https://t.co/yWEy4JkZWF

You can use any LLM. Response times are very fast. And voice agents now handle interruptions in natural, graceful ways.

These kinds of agents can be accessed via web, mobile, and telephone (and share the same back end).

There's also a full developer playground at https://t.co/mnGm21H1F4 that support more extensive experimentation/iteration.