Call center artificial intelligence is already helping contact and call centers in a wide range of areas, from agent performance and assistance to automation and the customer experience.
I’ve compiled the most helpful AI call center capabilities available on the market right now, plus three exciting advancements you’ll see in the next few years.
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 earliest days, IVR performed exactly like the machine that it was. While somewhat human in nature, it sounded closer to a computer and recognized only 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 usually shuttled to a live agent with an explanation that essentially amounted to “answer does not compute.”
Today’s iterations stand in stark contrast to the robotic versions of the not-so-distant past. Much more conversational in its approach, the automated tool can recognize and respond to a wide range of statements or requests.
Natural language capabilities mean customers can speak the way they do in real life. And machine learning empowers a continuously expanding speech catalog, although it still requires human support to guide its efforts.
Companies like Apple and Amazon have gone all-in on this technology, popularizing it with features like Siri and Alexa. Amazon Lex even packages this advanced IVR for developers creating the next generation of responsive apps.
While the feature has certainly come a long way, it is not yet perfect. On the plus side, it can automate your call flows to cut down on labor costs and improve containment rates. But its financial benefits come with a heavy up-front time investment as you input the mountains of data it needs to construct on-brand dialogue.
Data analysis is one of the areas where AI technology truly shines. Within seconds, your system can digest and interpret incredible amounts of data that would otherwise take your team days, if not weeks, to sort through.
It can also mine and flag pertinent information, such as agent-customer interactions, as they occur, giving you a chance to right the ship and resolve any problems before they escalate.
Real-time speech analytics make this possible, working hand-in-hand with automatic speech recognition features to highlight keywords or phrases that alert you to a possible misstep by an agent. This way, you’re more likely to catch any compliance or quality assurance issues that result from a team member going off-script or sharing incorrect information.
You can also analyze speech patterns that tap into customer sentiments, both positive and negative, zeroing in on specific words or phrases that signal frustration so you can implement necessary triage measures.
As VoIP vendors, like Dialpad and RingCentral, further develop this technology, we’re beginning to see advanced capabilities that include behavioral pattern recognition. These allow you to fine-tune every aspect of an agent’s performance, from speaking too quickly to managing an irate customer.
Real-time speech analytics make it possible to monitor, identify, and adjust to data-related trends quickly and efficiently, reducing the amount of human time and effort required to streamline your operation.
Great call scripts can improve conversion rates by supporting agents in overcoming customer objections or working through complaints. They ensure consistent quality across your team, giving everyone the same conversational framework. The only problem is that they take quite a bit of time to create and perfect — or at least, they used to.
These days, instead of poring over hundreds and thousands of transcribed customer interactions to extract the vital pieces, you can feed this information into a machine that will do it for you. Analytics software can sort through all of your data at lightning speed and then generate call scripts based on your set parameters. This generative technology is best exemplified in AI software like ChatGPT.
Just like ChatGPT, however, generated call scripts are still in their infancy. The content it spits out is only as good as the insights you provide, making it especially important to phrase your requests as specifically and as detailed as possible.
Even then, you won’t get a perfectly polished product.
You’ll need to spend time refining the final script before it becomes usable. But the generative approach shaves ample hours off of your task, providing, at the very least, a workable structure to get you started.
You can spend hours and days poring over customer data and market trends, searching for patterns to develop a list of leads. After all that, your results can still miss the mark as agents struggle to convert prospects too early in the sales funnel.
Modern AI takes the guesswork out of the process, sifting through immense amounts of data, web traffic, and customer profiles to serve up the warmest possible leads.
It can then automate outreach efforts via text, email, or chat to get the ball rolling.
Brands like Customers.ai and Seamless.ai even offer auto-generated email copy engineered to improve opens, clicks, and engagement. AI tech is still being perfected, so it’s always a good idea to proof any automated copy before sending.
All of these features leave your agents more time for direct customer interaction and ensure those interactions are as successful as possible.
Some platforms — Customers.ai included — provide a free version to give you a taste of what’s out there. Robust versions for business purposes can cost you upwards of $500 or more per month.
Closing out tickets and adding final notes to a customer profile can take up as much as one-third of an agent’s available time.
Still, these aspects are crucial to building solid customer relationships and identifying opportunities for future growth. Companies like Dialpad and Balto aim to do away with human note-taking completely by utilizing generative AI as a means of streamlining the process.
Dialpad’s generative AI assistants can use a call summary feature to outline any central themes and important ideas discussed between an agent and a customer.
These notes can serve as an alternative to post-call agent efforts, relieving the need to rely on memory and requiring only a brief review for accuracy. You can even program the system to adhere to specific compliance measures essential to your industry.
Current examples of this AI tech include ChatGPT and Google Gemini (formerly Bard), both online query platforms that can auto-generate responses and content creatively — much the way a human might. While it’s nowhere near perfect, the algorithms that run the tech maintain a continuous loop of self-learning and improvement.
So, the answers and output are only getting better, providing a solid content framework that, with a bit of human proofing, can hit the bullseye on a range of goals, from cold emailing to call scripting.
AI’s generative and ML capabilities are leading to new territory in which language barriers may no longer exist.
Current iterations convert speech to text, translate that text, and then convert the content to audio. Modern versions are approaching real-time conversational translation speeds, with a few kinks still to work out.
Microsoft Azure holds the leading spot in this budding field, although Google unveiled a promising pair of real-time translation augmented reality glasses last year.
The biggest hurdle to perfecting this technology is the varying sentence structure and cultural-emotional complexity behind the more than 7,000 languages currently in existence. But as the specific algorithms that govern machine learning continue to improve, we are likely to see real-time translation tech at work in the contact center field within the decade.
It’s already common practice to rely on knowledge based authentication methods, asking a customer to input 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 gleaned and stored after the customer repeats a series of specific phrases or in the course of casual conversation.
The beauty of biometrics is the convenience they afford the customer.
No more wasting time inputting the same numbers you’ve provided the last 10 times you called your bank or auto loan servicer.
It’s also quite accurate, as every caller’s “voiceprint” is distinct. Still, just as with facial recognition technology, your voice data can be stolen and improperly used. We aren’t likely to see the widespread adoption of biometric authentication features — at least, not without express customer consent — until certain data security and privacy concerns are addressed.
Virtual reality has come a long way in the last decade, giving more engaging and lifelike experiences for gaming and video. Some companies are already testing out the technology for training purposes, empowering employees to simulate a variety of complex scenarios in an effort to perform at their highest level.
While the quality and responsiveness of the tech are certainly amenable to these purposes, the cost remains extremely prohibitive.
A VR learning management system requires an investment of $10,000 to $15,000 on the low end. Sectors like healthcare and entertainment, where many roles are highly technical in nature, sit at the forefront of this approach. For contact centers, it’s expected to become more accessible — even commonplace — within the decade.
