What Operational Issues Does AI Address?
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What Operational Issues Does AI Address?

By CIOReview | Wednesday, May 4, 2022

It addresses various operational issues, allowing contact centers to reach their KPIs and ROI objectives.

Fremont, CA: Contact centers, like all organizations, are under growing pressure to accomplish more with less. There's a need to cut expenditures and potentially human resources, yet contact volumes are growing, customers' problems are becoming more complicated, and their service expectations are greater than ever.

Artificial intelligence (AI) has long gets regarded as offering a solution to problems. So much so that it's projected that Conversational AI bots will manage 20 Percent of customer support conversations by 2022. As a result, this technology's market will expand from 4 billion dollars to 15 billion dollars by 2024.

What operational issues does AI address?

Assisting customers

The first is consumer self-service via online, mobile, and phone channels. In general, the AI system reads client requests and attempts to react the same way a human agent might. It is usually possible to fail over to a live agent if it cannot. It addresses various operational issues, allowing contact centers to reach their KPIs and ROI objectives.

For starters, it provides 24/7 coverage at a much lower cost than rotating shifts of human agents could. It also allows for the ongoing management of huge quantities of interactions, even peaks, with each client responding virtually instantaneously.

Assisting agents

 The second primary use of Conversational AI in contact centers is to assist live agents by offering a rather more natural user interface to the tools, systems, and information that agents require to execute customer engagements.

That super-powered AI companion also becomes the agent's most helpful tool, substantially improving the employee experience and resulting in reduced attrition rates while decreasing the time required to educate agents to a given degree of skill.

Unlocking knowledge

Most businesses now save a massive quantity of consumer data, including information on purchases, previous encounters, and even phone and chat transcripts. Much of the information comes in unstructured data, such as verbatim remarks, which may be quite useful if mined for insight.

Only an AI can query such a data bank to cross-reference and discover linkages between bits of information that provide fresh insights into client behavior. A Conversational AI can achieve this on the fly by monitoring conversations – whether on its own or listening in on agent interactions – and utilizing sentiment and keyword analysis to determine how a customer reacts or forecasts what a consumer wants.