Call center AI is already helping call and contact centers in a wide range of areas, from agent performance and support to automation and customer experience.
5 Technologies Made Possible Today by Artificial Intelligence in Call Centers
I’ve rounded up the most useful AI call center capabilities available on the market right now, plus three exciting advancements you’ll see in the coming years.
Conversational IVR
Interactive voice response is one of the first applications of advanced call center technology, automating important aspects of customer interaction by eliciting spoken responses.
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In its early days, interactive voice response (IVR) worked exactly like the machine it was. While it had a somewhat human feel, it sounded more computer-like and only recognized pre-recorded responses. If a caller said anything other than a specific phrase (something basic like “speak to an agent,” “check account status,” or “main menu”), they were typically routed to a live agent with an explanation that basically amounted to “the response cannot be processed.”
Today's versions are in stark contrast to the robotic versions of the not-so-distant past. The automated tool, much more conversational in its approach, can recognize and respond to a wide range of statements or requests.
Natural language capabilities mean customers can speak as they do in real life. And machine learning enables an ever-expanding voice catalog, though it still requires human assistance to guide its efforts.
Companies like Apple and Amazon have bet heavily on this technology and have popularized it with features like Siri and Alexa. Amazon Lex even offers this advanced interactive voice response (IVR) system for developers creating the next generation of responsive apps.
While the feature has come a long way, it's still not perfect. The silver lining is that it can automate call flows to reduce labor costs and improve containment rates. But its financial benefits come with a huge upfront time investment, as mountains of data must be entered to build an on-brand dialogue.
Real-time voice analysis
Data analysis is one of the areas where AI technology really shines. In a matter of seconds, your system can digest and interpret incredible amounts of data that would otherwise take your team days, or even weeks, to analyze.
You can also extract and flag relevant information, such as interactions between agents and customers, as they happen, giving you the opportunity to right the ship and resolve any issues before they escalate.
Real-time speech analytics makes this possible, working in conjunction with automatic speech recognition features to highlight keywords or phrases that alert you to a potential agent misstep. This way, you’re more likely to spot any compliance or quality control issues that arise when a team member goes off-script or shares incorrect information.
You can also analyze speech patterns that address customer sentiment, both positive and negative, focusing on specific words or phrases that indicate frustration so you can implement the necessary triage measures.
As VoIP providers like Dialpad and RingCentral develop this technology further, we’re starting to see advanced capabilities that include behavioral pattern recognition. These make it possible to fine-tune every aspect of an agent’s performance, from speaking too quickly to handling an angry customer.
Real-time speech analytics allows you to monitor, identify and adapt to data-related trends quickly and efficiently, reducing the amount of time and human effort required to optimize your operation.
Generative Call Scripts
Good call scripts can improve conversion rates by helping agents overcome customer objections or resolve complaints. They ensure consistent quality across the team, giving everyone the same framework for conversation. The only problem is that they take quite a bit of time to create and perfect (or at least, they used to).
Today, instead of analyzing hundreds and thousands of transcribed customer interactions to extract the essential snippets, you can feed this information into a machine that will do it for you. Analytics software can sort through all your data at lightning speed and then generate call scripts based on the parameters you’ve set. This generative technology is best exemplified by AI software like ChatGPT.
However, like ChatGPT, generated call scripts are still in their early stages. The content you generate is only as good as the information you provide, so it's especially important to phrase your requests as specifically and detailed as possible.
Even so, you won't get a perfectly polished product.
You'll need to spend time refining the final script before it's usable. But the generative approach saves you many hours of work and gives you, at the very least, a working structure to start with.
Smart lead generation
You may spend hours and days analyzing customer data and market trends, looking for patterns to develop a list of potential clients. After all that, your results may not be what you expected, as agents struggle to convert leads too early in the sales funnel.
Modern artificial intelligence takes the guesswork out of the process, analyzing vast amounts of data, web traffic, and customer profiles to deliver the most compelling leads possible.
You can then automate outreach efforts via text, email, or chat to get things up and running.
Brands like Customers.ai and Seamless.ai even offer automatically generated email copy designed to improve opens, clicks, and engagement. AI technology is still being refined, so it’s always a good idea to proofread any automated copy before sending it.
All of these features leave your agents with more time for direct customer interaction and ensure that those interactions are as successful as possible.
Some platforms (Customers.ai included) offer a free version so you can get a feel for what's available. Robust versions for commercial purposes can cost upwards of $500 per month.
Post-call automation
Closing tickets and adding final notes to a customer profile can take up to a third of an agent's available time.
Still, these aspects are crucial to building strong customer relationships and identifying opportunities for future growth. Companies like Dialpad and Balto aim to eliminate human note-taking altogether by using generative AI as a means to streamline the process.
Dialpad's generative AI assistants can use a call summary feature to outline the core topics and important ideas discussed between an agent and a customer.
These notes can serve as an alternative to agent post-call tasks, as they avoid the need to rely on memory and only require a brief review to check for accuracy. You can even program the system to meet specific compliance measures that are essential to your industry.
Current examples of this AI technology include ChatGPT and Google Gemini (formerly Bard), both online consultation platforms that can automatically and creatively generate answers and content, much like a human would. While it is far from perfect, the algorithms running the technology maintain a continuous cycle of self-learning and improvement.
So the responses and results are getting better and better, providing a solid content framework that, with a little human testing, can hit the mark on a variety of objectives, from cold emailing to scripting calls.
3 Future AI Technologies for Call Centers
Real-time voice translation
The generative and machine learning capabilities of AI are leading us into new territory where language barriers may no longer exist.
Current versions convert speech to text, translate that text, and then convert the content to audio. Modern versions are approaching the speed of real-time conversational translation, although there are still some issues to be resolved.
Microsoft Azure holds the top spot in this emerging field, although last year Google introduced a promising pair of augmented reality glasses with real-time translation.
The biggest hurdle to perfecting this technology is the varied sentence structure and cultural and emotional complexity behind the 7,000-plus languages that currently exist. But as the specific algorithms that govern machine learning continue to improve, we'll likely see real-time translation technology up and running in the contact center field within a decade.
IVR authentication using biometrics
It is already common practice to rely on knowledge-based authentication methods, asking the customer to enter their account, PIN or Social Security number to verify their identity.
New biometric methods use “voiceprint” technology to verify a customer simply by the sound of their voice. This identifying information can be obtained and stored after the customer repeats a series of specific phrases or in the course of a casual conversation.
The beauty of biometrics is the convenience it brings to the customer.
Don't waste any more time entering the same numbers you provided the last 10 times you called your bank or auto loan servicer.
It's also fairly accurate, as each caller's “voiceprint” is different. However, as with facial recognition technology, your voice data can be stolen and misused. We're not likely to see widespread adoption of biometric authentication features (at least, not without the customer's express consent) until certain data security and privacy concerns are addressed.
Virtual reality for agent training and customer tutorials
Virtual reality has come a long way in the past decade, offering more engaging and realistic experiences for games and videos. Some companies are already testing the technology for training purposes, allowing employees to simulate a variety of complex situations in an effort to perform at their highest level.
While the quality and responsiveness of the technology is certainly adequate for these purposes, the cost remains extremely prohibitive.
A VR learning management system requires an investment of at least $10,000 to $15,000. Industries such as healthcare and entertainment, where many jobs are highly technical in nature, are at the forefront of this approach. For contact centers, it is expected to become more accessible, even commonplace, within a decade.
