How Will Machine Learning and AI Change the Future of VoIP?

The influence of artificial intelligence and machine learning algorithms extends to almost every area of technology. Those spheres that haven’t benefited from the impact of AI yet will certainly do so in the near future, so far-ranging are the applications it can accommodate.

This includes VoIP telephony, a tech niche that has been disruptive in its own way for over two decades at this point.

So how will AI alter VoIP going forward, and why does this matter for businesses as well as average end users?

Improving customer experiences

The rise of interactive voice response (IVR) solutions is helping to enhance customer service quality in all kinds of companies, and across all industries.

AI is able to serve voice callers, answer queries, fielding complaints, and standing in for human call center workers so that more calls are answered in less time.

Augmenting conference calling

Conferencing is essential for many organizations, and AI promises to streamline this even further thanks to the likes of integrated automated translation which occurs on the fly, enabling people speaking different languages to communicate freely without the need for an interpreter.

Troubleshooting network faults

VoIP relies on consistent network performance and bottleneck-free bandwidth to work well, especially where large volumes of calls are being hosted simultaneously.

In the case of network segmentation, optimizing performance is even trickier. AI-powered tools are able to monitor extensive infrastructures, detect dilemmas and troubleshoot problems in one fell swoop.

Extracting insights

Being able to transcribe calls from customers means the data contained in voice communications can be more easily collated and fed into analytics tools by growing teams of data scientists.

Enabling remote working

Digital voice calls are key to productive remote working environments, and the latest Windows’ VoIP apps empower employees whether they’re in the office, out in the field or at home.

There are a host of AI-enhanced features which come as part of this selection of solutions, such as advanced ambient noise canceling to enhance the clarity of audio for remote workers who are having to share busy domestic environments with others.

Catalyzing call routing

Making sure that calls get to the right endpoint isn’t always straightforward, but with AI-enhanced call routing there’s less likelihood of customers having their call forwarded to the wrong person or department. This avoids customer consternation, and also saves team members time each day.

Collecting feedback

Even if human team members are primarily used as the point of contact for inbound calls from customers, businesses can still offload certain functions to an AI-powered solution.

This includes carrying out post-call surveys as well as follow-up requests for feedback, or check-ups to ensure that the needs of the customer have been satisfied.

It’s a great way to boost retention rates, and grow a loyal base of customers who might otherwise migrate elsewhere.

Identifying pain points

It’s often possible for a business to run a call center service without necessarily knowing whether or not it is proving effective at its intended tasks. Even if stats look adequate on the surface, there may be some underlying issue which isn’t obvious from a quick glance at key metrics such as average call time.

AI isn’t hindered by things like an attention span, and machine learning algorithms are equipped to unearth pain points in the service process which would otherwise have slipped past.

Final thoughts

The future of VoIP seems intrinsically tied to AI, and that’s good news for all involved. Businesses will benefit from happier customers and lower costs, while the customers themselves will have to spend less time on the phone to get what they need.

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Within the bustling realm of data science, our editorial team stands as a collective force of learning and exploration. Meet the dynamic minds behind the scenes—Sukesh, Abhishek, and other Authors. As passionate data science learners, they collectively weave a tapestry of insights, discoveries, and shared learning experiences.
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