From the invention of writing to quantum computing, emerging technologies have always had a profound impact on the way we work. New tools mean new products and services, new organizational structures, whole new markets, and sometimes even new methods of thought.
These days, the big news is coming out of artificial intelligence. Specifically, the release of ChatGPT has made it possible for everyone to try out an advanced AI application for the first time, and it has ignited a firestorm of speculation as to how industries ranging from medicine to copywriting might be transformed.
In this piece, we’re going to try to cut through the hype to give contact center managers some much-needed clarity. We’ll discuss what AI is useful for, how it will change how contact center agents function daily, and what tools they should investigate to get the most out of AI.
What Is AI Useful For?
Artificial intelligence is a pretty broad category, encompassing everything from the most basic linear regressions to the remarkable sophistication of deep reinforcement learning agents.
This is too much territory to cover in a single blog post, but we can nevertheless make some useful general comments.
The way we see it, there are essentially two ways that AI is useful: it can either completely replace a human for certain tasks, allowing them to shift their focus to higher-value work, or it can augment their process, allowing them to reach insights or achieve objectives that would’ve taken much longer otherwise.
Take the example of ChatGPT, a large language model trained on huge quantities of human-generated text that is able to write poetry, generate math proofs, create functioning code, and much more.
For certain tasks – like generating blog post titles or short email blasts – ChatGPT is good enough to supplant humans altogether. But if you’re trying to learn a complex subject like organic chemistry, it’s best to treat ChatGPT more like a conversational partner. You can ask it questions or use it to test your understanding of a concept, but you have to be careful with its output because it might be hallucinating or otherwise getting important facts wrong. [1]
Since ChatGPT and large language models more generally are what everyone is focused on at the moment, it’s what we’ll be discussing throughout this essay.
How is AI Changing How Contact Center Agents Work?
As soon as ChatGPT was released it spawned an unending stream of hot takes, from “this is going to completely automate the entire economy” to “this is going to be a huge flop that no one finds particularly useful.”
Recently, a study by Erik Brynjolfsson, Danielle Li, and Lindsey R. Raymond called “Generative AI at Work” examined how LLMs are being used in contact centers. They found that both these perspectives were wrong: generative AI was not completely automating contact centers but was proving enormously helpful in making contact centers more efficient.
Specifically, LLMs were able to capture some of the conversational patterns and general tacit knowledge held by more senior agents and transfer it to more junior agents. The result was more productivity among these less experienced workers, less overall turnover, and a better customer experience.
To help flesh this picture out, we’ll now turn to examining some specific ways this works.
Large Language Models are Helping Agents Work Faster
There are a few ways that LLMs are helping agents get their jobs done more quickly and efficiently.
One is by helping them cut down on typing by providing contextually appropriate responses to customer questions, which is exactly what Quiq Compose does.
Quiq Compose learns from interactions between contact center agents and customers. It can take a barebones outline of a reply (“Nope, you waited too long to return the product…”) and flesh it out into a full, coherent, grammatical response (“I’m so sorry to hear that the product isn’t working as intended…”.)
Quiq Suggest also learns from multiple agent-customer interactions, but it offers real-time suggestions. As your contact center agents begin typing their responses, the underlying model offers a robust form of autocomplete to help them craft replies more quickly. This substantially reduces the amount of time that agents have to spend up to 30% less time hunting around for information and tweaking their language to be both polite and informative.
What’s more, because Quiq Suggest leverages lightweight “edge” language models trained on a specific company’s data, it’s able to run very quickly.
Another way you can reduce agent handling time is by simply cutting down on the amount of text a given agent has to process. In the course of resolving an issue, there will usually be some extraneous text, like “Thanks!” or “Have a good day!” When Quiq’s conversational AI platform sees these unimportant messages, it automatically filters them and tacks them on to the end of the transcript.
Finally, a lot of friction and information loss can occur when a conversation is transferred between agents, or from an AI to a human agent. This is where conversation summarization comes in handy. By automatically summarizing the interaction so far, these transfers can take less time and energy, which also contributes to lower agent burnout and higher customer satisfaction.
Large Language Models can provide 24/7 Customer Support
There’s a fundamental asymmetry in running a great contact center, inasmuch as problems can occur around the clock but your agents need to sleep, rest, and play frisbee golf.
Unless, of course, some of your agents aren’t human. One of the great advantages of computers and algorithms is that they have none of the human frailties that prevent us all from working every hour of the day. They have no need for sleep, bathroom breaks, or recreation.
If you’re using a powerful conversational AI platform like Quiq, you can have AI agents deployed every hour, day or night, answering questions, completing tasks, and resolving problems.
Of course, the technology is not yet good enough to handle everything a contact center agent would handle, and some issues will have to be postponed until the humans punch the clock. Still, with the right tools, your operation can constantly be moving forward.
Large Language Models Can Help With Documentation
Writing documentation is one of those crucial, un-sexy tasks that businesses ignore at their own peril. Everyone wants to be coding up a blockchain or demo-ing a shiny new application to well-heeled investors, but someone needs to be sitting and writing up product specs, troubleshooting workflows, and all the other text that helps an organization function effectively.
This, too, is something that AI can help with. Whether it’s brainstorming an outline, identifying common sticking points, or even writing the document wholesale, more and more technical organizations are exploring LLMs to speed up their documentation efforts.
Just remember that LLMs like ChatGPT are extremely prone to hallucinations, so carefully fact-check everything they produce before you add it to your official documentation.
Large Language Models Can Help With Marketing
A final place where AI is proving incredibly useful is in marketing. Whether or not your agents have any input into your marketing depends on how you run your contact center, but this piece wouldn’t be complete without at least briefly touching upon marketing.
One obvious way that this can work is by having ChatGPT generate headlines, subject lines, Tweets, or even SEO-optimized blog posts.
But this is not the only way AI can be used in marketing. One very clever use of the technology that we’ve encountered is having ChatGPT generate customer journeys or customer diary entries. If your product is targeting men in their 40s who aren’t crushing life they way they used to, for example, it can create a month’s worth of forum posts from your target buyers discussing their lack of drive and motivation. This, in turn, will furnish targeted language you can use in your copy.
But bear in mind that marketing is one of those things that’s just incredibly subtle. It takes all of 30 seconds to come up with a few headlines for an email, but the difference between an okay headline and an extraordinary one can be a single word. Here, as elsewhere, it’s wise to have the final word remain with the humans.
Working more Quiq-ly
The world is changing, and contact centers are changing along with it. If you expect to retain a competitive edge and a top-notch contact center, you’ll need to utilize the latest technologies.
One way you could do this is by paying an expensive engineering team to build your own LLMs and AI tooling. But a much easier way is to integrate our Quiq conversational AI platform into your contact center. Whether it’s automatic summarization, filtering trivial messages, or using Quiq Suggest and Quiq Compose to cut down on average handle time, we have a product that will streamline your operation. Schedule a demo with us today to see how we can help you!
Footnotes
[1] You could argue that both of these examples boil down to the same thing. That is, even when you treat ChatGPT as a sounding board you’re really just replacing a human being that could’ve performed the same function. This is a plausible point of view, but we still think it’s useful to distinguish between “ChatGPT acting like a total replacement for a human for certain boilerplate tasks” and “ChatGPT augmenting a human’s workflow by acting like an idea generator or conversational partner.” Reasonable people could disagree on this, and your mileage may vary.
