In the last two decades, the business communication landscape has entirely revolutionized by the advances in machine learning, especially with the rise of the Internet and Voice over Internet protocol (VoIP). Realizing its significance, most enterprises now have acknowledged or performed the integration of disruptive technologies, intending to enhance their communication systems.

Business communication is generally considered as a vital internal operation acted in organizations for obtaining commercial benefits. It also refers to the exchange of information between people and employees both inside and outside of companies, helping them to develop and bolster the employer-staff relationship. As a result, it leads to surged business productivity, enhanced efficiency, improved business partnerships, advanced business operations, and considerably boosted the customer base.

In the latest digital transformation age, where data is an indispensable asset of organizations, they have also augmented their dependency on machine learning, AI, and big data. And this helps enterprises to improve their business communication practices.

Meanwhile, big data has effectively provided businesses with tons of information on how their staff chooses to engage, when people log in and log out, and when and where they become most productive. These are the human aspects of business communication where the average data platform cannot yet seize and turn it into actionable insights.

As modern business communications generate a huge amount of data, advancements in technologies like machine learning enable businesses to process vast volumes of data faster. And in this way, recording, storing, and processing call data provides access to companies to spot patterns for enhanced productivity, recognizing trends in team and customer communications that need to change and where they are working.

Machine learning can also be helpful for businesses to cope with the always-on expectation of customers. With the help of technology, businesses can easily address customers’ queries from wherever they are working as customers expect immediate answers to a phone call. Also, business staffs are relieved from their email influx by handling emails with intelligent automated responses.

It is expected that machine learning might soon be able to tell enterprises whether customers prefer a call or meeting, and when to speak and listen to a customer on the call. The data collected will also assist customer service teams to work what time of day they need to be on calls and how many staffs need at one time.

In years to come, machine learning will become increasingly combined within workplace communications systems that can help analyze intricate patterns in user data, improving productivity across each interaction.