Artificial Intelligence (AI) and Business Communications

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How AI impacts communication tools.

AI is one of the hottest topics in the technology world today. Find out how we see it impacting communication tools and most importantly how you can take advantage of it.

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As the leading company in the business communications space, RingCentral is actively exploring how applying AI to communications systems can benefit customers today—some of these tools described below are live today for real customers, some are in active development, and on the horizon—all with the common goal of providing innovative new ways of improving the way businesses communicate.

Everyone has probably heard of AI. It follows in a long line of ‘buzzword’ technologies that industries are touting as the next-big-thing that will revolutionize the world. But not all technologies that get a spot in the limelight manage to deliver on the promises they make. For example, the IoT (Internet of Things) promised us, amongst other things, a fully connected world where all our everyday devices could talk to each other and stay up to date. But unfortunately, most of us are still waiting for the connected kettle to take off!

So what of AI? Where does it fit in this spectrum—and more immediately for us—how will it affect business communications?

Well, the first thing we should look at is why. Just because AI is a powerful technology does not mean it is universally applicable.

AI is actually an umbrella term that covers several interrelated technologies, such as speech to text, natural language processing (NLP), predictive analytics, and machine learning. Click To Tweet

One common theme throughout all of these though is that they thrive on having as much data as possible to work with—the more opportunities that AI technologies have to test out their algorithms and refine their processes, the better they become and the more suitable they are for the task. They work fantastically when performing a task they have had multiple opportunities to work with before, such as understanding human speech and turning it into text. But, for example, you wouldn’t not want to use AI for a creative and one-off task like designing a new logo for a brand.

Fortunately, communications systems, coupled with their deep integration into other tools and our existing business processes, are extremely data-rich and full of repeated actions—from call centre applications, rich with many days of call-log data, to speech analysis over many thousand minutes of calls, communications systems are, at first glance anyway, an extremely suitable place for AI technologies to be applied.

Alexa Echo as an AI Tool.

We should also be careful to set our expectations correctly on what the technology will deliver. When most people think of AI, they think of something like Jarvis, the all-seeing, supremely capable, virtual assistant that lives in Iron Man’s suits of armour.

In reality, whilst this is still very much one of the aims of AI (think of Google Assistant, Siri, and Alexa) AI tools can apply in much humbler places too, and we shouldn’t dismiss these benefits. We often also talk about IA, which stands for Intelligence Amplification, applying AI and computing techniques to make existing processes and tools that little bit smarter.

See also  Will Chatbots Replace IVR?

Here are just a few ways we will start to see AI shape our communications tools:

Better automation across all aspects of communication

Automation as a benefit for smarter communications.

Automation today plays a key part of why people implement smarter communications systems. For example, think of the clever queues that form part of most support hotlines you call. Rather than having to wait in line for an agent to become free, you can select from a menu of options to automatically handle common use cases, which saves you time on hold and also the company’s resources for additional agents.

We will see virtual agents take on more and more of this sort of activity. Instead of using an IVR (Interactive Voice Response) to choose from menu options, we will move to asking questions directly to virtual agents and getting responses directly, needing human agents only for the most difficult escalations. We will see this not just in telephony but in any area where simple responses can be provided, for example, online chat-based support. And unlike real agents, it is simple to deliver all of these services 24/7 and scale them as demand increases.

A shift in how we interact with our tools

Natural Language Processing understands human speech.

Communications interfaces have changed significantly over the last 10 years, moving to adapt to whatever the latest and best ways are to utilise features. Think of smartphone apps and how they are now all optimised for touchscreen-based input. One AI technology that will start to change our tools is Natural Language Processing,  the ability for computers to understand human speech and perform the correct actions based off it.

As users, rather than having to learn how our tools work, e.g., which buttons do what and how to perform complex actions (perhaps monitoring a call followed by barging in and then a warm transfer), we will see a shift towards asking our tools to do this via normal speech. Imagine instead of having to learn every new interface layout being able to call up complex features just by asking.

Proactive decision making

AI will be used to make more complex decisions in IT.

Especially for larger businesses, we spend a lot of time optimizing how our users consume business communications tools. From simple micro-configurations like how long a phone line will ring for, to more complex business-wide policies like routing amongst many call agents over many hours of business, and escalation paths for critical calls. For these today, we rely on human IT staff to understand the best ways to set up a system, spot patterns, and respond to them.

We will see AI and machine learning make more of these complex decisions. Not only will AI tools be able to more easily optimise systems for maximum effectiveness, performing, and analysing many iterations of optimization, it will also save on resource and the steep learning curve for the expertise needed.

Originally published Jan 25, 2018, updated Jan 16, 2023

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