Introduction to Responsible AI: The CallMiner Research Lab Responsible AI Framework
The CallMiner Research Lab Responsible AI Framework outlines definitions and concerns, as well as some of the driving questions, leading to deeper con...
The Team at CallMiner
January 10, 2018
Artificial intelligence (AI) gives machines the ability to learn from experience as they take in more data and perform tasks like humans. Computers with these advanced technologies are trained to complete human-like tasks by processing data and recognizing the patterns within it.
In 1955, the term “artificial intelligence” was coined. Early AI research involved problem-solving and symbolic methods, which evolved into training computers to mimic human reasoning in the 1960s, street mapping projects in the 1970s, personal assistants in the early 2000s, and self-driving cars, game-playing computers, and self-serve customer service today. As data volumes have increased, algorithms have advanced, and computing power and storage have improved, industries across the board apply AI and utilize deep learning and natural language processing to perform more advanced tasks every day.
There are various fields of AI in existence today. For example, machine learning automates analytical model building and uses neural networks, statistics, operations research, and physics to glean insight from data. Similarly, deep learning utilizes neural networks with layers of processing units to learn complex patterns in vast amounts of data. Additionally, natural language processing occurs when computers can analyze, understand, and generate human language; specifically, natural language interaction is a technology that enables humans to communicate with computers using spoken language to perform tasks.
One roadblock to AI adoption is fear. After all, movies have been touting the dangers of artificial intelligence taking over the world for decades. However, the risks are not just drama drummed up by Hollywood. Noted theoretical physicist Stephen Hawking himself has stated there is a reason to beware of AI: he fears the dangers of creating AI that is on par with or surpasses human abilities. For now, Hawking admits that existing AI is primitive and useful, and he makes use of it himself to communicate.
Another common challenge with AI is the assumption that companies do not require human input when adopting AI. The fact of the matter is that humans play a critical role in developing and implementing AI. In fact, AI only works well when the data it has to work with is sound. Humans guide the systems and data input while asking the right questions to set up automation.
Businesses taking advantage of AI technology reap a multitude of benefits. From reducing operational costs, to boosting efficiency, to increasing revenue, to improving customer experience, AI makes business better. Specifically, AI empowers companies to save time and money by automating redundant processes and tasks. AI also helps companies reduce human error.
Businesses utilizing AI see a significant benefit when they use it to mine data, make data-driven decisions, and predict customer preferences to personalize their experiences. AI adds intelligence and improves business capabilities while delivering accuracy via deep neural networks that was impossible to achieve before for companies. Namely, people do not have the capabilities of mining the vast quantities of data that AI can. The more companies utilize AI, the more accurate it becomes. This accuracy makes AI especially useful for improving patient satisfaction in healthcare as well as for marketing because it teaches itself as it continues to work without fatigue. Companies that achieve self-learning algorithms via AI gain a competitive advantage because they get the most from their seas of data.
Many industries are leveraging the power of AI today, but it’s proving particularly useful in improving call center performance. Thanks to artificial intelligence, virtual customer assistants can predict customer needs and provide them with personalized solutions. Call centers deploying AI are more efficient because the virtual assistants handle routine questions and queries while human agents handle more complex customer needs.
Specifically, Big Data and AI give chatbots the ability to predict and analyze customer questions, especially using previous customer activities and behaviors. AI also frees up human agents by following up with customers and automating operations. The result is customers are more satisfied with their interactions with companies when AI and customer service representatives work together and utilize data insights to personalize service, solve problems, and deliver exceptional customer experience.
How is your company using AI?
Learn 3 key ways that AI benefits customer service organizations from the experts at Gartner.