3 Artificial Intelligence Applications That Will Take You by Surprise

Wireframe head looking towards the future of artificial intelligence applications

Artificial intelligence (AI), has come far in the last 30 years. As computer systems continue to become more powerful and complex, the capabilities of artificial intelligences will also increase.

In fact, artificial intelligence programs have already become a major part of how businesses operate. Companies use these programs to automate an enormous variety of data management and analytical tasks.

It is important to note that artificial intelligence is a very broad term that covers a variety of “intelligent” systems. For example, web browser search engines are an example of AI systems. However, there are applications of machine learning and machine intelligence systems may still surprise you.

1: Assessing Human Behavior and Emotions

Accurately assessing human emotion and behavior is a difficult skill for many. In fact, some people never seem to quite get the hang of judging the emotional state of others.

However, there are now artificial intelligence systems that can evaluate the emotional state of a person by analyzing how they speak. These systems break human speech into identifiable features and once combined into vectors are inserted into machine learning algorithms. Those features can be associated with particular emotional states, and by studying them, the AI of voice analytics can compile an accurate assessment of a person’s emotional state.

Call centers use this application of artificial intelligence to systematically analyze their customers and their phone agents alike. By tracking changes in behavior and tone over the course of a conversation, call centers can more easily identify which phone agents are delighting customers, and which ones need more training.

2: Predicting Future Behaviors/Actions

Predictive voice analytics programs take the emotional behavior assessment even further by predicting likely future actions of customers.

In this application, after the voice features are plugged into machine learning algorithms, the current customer behaviors are compared to past ones of other customers.

As the machine learning algorithms collect more data on the results of calls with customers, they can self-adjust their criteria for linking behaviors to outcomes. This means that, over time, the algorithms will become more accurate at predicting future behaviors.

Sales industry call centers use predictive voice analytics to assess which sales call leads are worth pursuing, and which ones are a waste of time. This vastly improves targeting of calls, increasing sales close rates and saving phone agents time spent on calls that go nowhere.

3: Proactive Learning and Interpretation of Data to Augment Business Decisions (Machine Intelligence)

Right now, most artificial intelligence systems cannot make inferences or introduce new ideas, they simply cross-reference existing data. Machine learning systems can learn from past data, but they don’t create inferences from that data.

True machine intelligences will do more than just use existing data to come up with an answer, they’ll actually be able to understand how and why specific events occur, and proactively make recommendations based on that understanding.

As noted by a TechCrunch article about artificial intelligence; “Whereas machine learning will accurately predict that your electric bill will increase next month, machine intelligence will accurately predict that your electric bill will increase next month — and tell you why.”

This capability to not only process data, but to make inferences about how individual data points affect outcomes, will make machine intelligence systems incredibly powerful tools for fine-tuning business operations.

As we move forward, artificial intelligence systems will only become more important to the way businesses in every industry operate. Learn more about how you can leverage artificial intelligence for call center operations now.


Access to RankMiner's paper on using Artificial Intelligence to monitor and measure call center agent performance