The field of AI has been evolving since the 1950s, but only with the recent addition of the cloud has the technology been able to realize its commercial potential. In time, AI will find its way into everyday life — both at home and at work. For workplace productivity, we’re now starting to see some practical applications.
While cloud is the great enabler for all things AI, the pandemic has created many AI-friendly use cases. Automation remains a key driver for AI, but there are many applications in use now that focus on making employees more productive and collaborative, especially when confined to remote workplace settings.
While automation can improve productivity, AI can provide richer results when it’s applied to make communicating easier and collaboration smarter. Let’s examine four applications of AI in unified communications that show what’s possible when the technology is used for workplace collaboration.
1. Noise suppression
Noise suppression is a new AI application and addresses some distinct challenges that have arisen with so many of us working from home, often in less-than-ideal conditions for collaboration. The more people on a group call, the more likely there will be extraneous noise.
Noise suppression technology addresses two problem sets. First, noise suppression can block out extraneous sound coming from your workspace environment. So, others aren’t distracted, and you come across as more professional. These would include sounds you have control over, such as keyboard typing or crinkly food wrappers, as well those you don’t, such as kids yelling, dogs barking or sirens screaming past your window.
Second is the need to block noise out so speech recognition applications can accurately capture and analyze the conversations. AI-driven noise suppression can manage these scenarios well and are highly attuned at separating human speech from other types of sound.
2. Speech technology
Speech technology is rich with possibilities for AI in unified communications. Two popular use cases are real-time translation and real-time transcription. Both are becoming common features in collaboration platforms, and they improve productivity in different ways.
Real-time translation helps make the workplace more inclusive, where language is no longer a barrier for communication. Teams can collaborate from anywhere, which makes it easier to support enterprises with a global employee base.
Transcription is a form of speech-to-text that enables meeting productivity because attendees don’t have to be taking notes. Instead, they can focus better in the moment for more immersive teamwork. Also, once speech is converted to text, it becomes searchable and adds a layer of value that didn’t exist before. Both translation and transcription are possible without AI. But AI can process speech fast enough to be real time, and its accuracy improves the more it’s used.
3. Meeting summaries
Building on the speech-to-text transcription capability, AI can do far more than simply produce a verbatim record of a conversation. The key here is converting speech into digital form, where it complements other forms of text, like Word documents and chat threads. They all share the same qualities of being data streams, and they can be processed by AI.
AI applications can create customized summaries for each team member based on certain requirements, no matter how many participants or how long the meeting. By specifying key words or phrases, only the relevant content would be parsed out into user-driven summaries. This would enable participants to quickly generate summary reports to share with others who couldn’t make the meeting.
Workers no longer need to sift through an hour-long replay to capture the critical five minutes they need, so this saves a lot of time. Not only that but AI can produce these summaries going back in time. So, the results could also include the last few meetings to provide a more complete picture of what each team member needs to do.
4. Facial recognition and biometrics
AI can be used to make the authentication process more accurate and more efficient in the workplace. Prime examples would be authentication for starting or joining a meeting, accessing files, and granting access to enter buildings, offices or meeting spaces.
Facial recognition and voice biometrics are two methods of AI-driven authentication. These are emerging in the workplace now as bona fide authentication methods, mainly because AI applications have become accurate enough to be trusted at scale. In terms of efficiency, they save time throughout the collaboration process, as they work faster than conventional methods, such as ID cards, key fobs, passwords or two-factor authentication. During the pandemic, they also gained favor by being touchless and contributing to workplace safety as businesses try to urge employees to come back to the office.