We’ve been hearing about Artificial Intelligence for years now – enough so that even the most casual AI watcher has a high-level understanding of what AI is: the art and science of getting computers to act and respond just like humans do. Casual AI watchers are also likely to recognize that AI is getting smarter - less artificial and more intelligent - by the day. In part, that’s thanks to machine learning, an AI technique that helps AI become more intelligent by learning over time, on its own, without having to be programmed.
Last week we looked at three reasons why call deflection is vital for your customer support efforts. While it may not always be correct to think of your employees as customers, when it comes to internal support and ITSM, it’s reasonable to draw a parallel between your customer support organization and your internal support system.
KMWorld 2018 in Washington, DC concluded last week. Since the event combines attendees registered for the Enterprise Search & Discovery, Taxonomy Boot Camp, Text Analytics Forum, and Office 365 Symposium sub-conferences, co-located exhibitors hear an interesting mix of challenges and views related to knowledge management. Not surprising, some attendees are tasked with finding solutions to very narrow problems such as creating a labeling taxonomy to a set of documents or how to configure Sharepoint to improve search results.
As products become commoditized, and the time and cost of innovation lengthens, businesses are relying on their brand and support experiences to become a business differentiator.
But providing an exceptional support experience for customers, and internally for employees, is incredibly tough. To be successful, support organizations must find and apply the right information in order to add efficiency to their business operations, increase employee retention and effectiveness, and improve customer satisfaction. Yet most organizations fall well short of these objectives.
There’s a tremendous amount of buzz these days around Artificial Intelligence, and the concepts and techniques associated with it. These concepts and techniques involve sophisticated technology, and their explanations are often confusing to a non-technical audience.
The concept of AI has been around for so long that most of us have a good high-level understanding of just what artificial intelligence is: it’s the technology that makes it possible for computers to act and react like humans. And most of us also understand that AI is becoming more and more intelligent, and seemingly less and less artificial. Yesterday, it was Amazon suggesting books we might like. Today it’s Alexa answering our trivia questions and turning the thermostat down. Tomorrow it will be driverless Ubers finding the quickest way to get us to wherever we need to go.
Although artificial intelligence (AI) draws a lot of attention in the consumer engagement space, it’s also poised to make a dramatic impact in life sciences.
Several trends are converging in life sciences that bring new challenges and opportunities for which AI technologies are ideally suited. These trends include precision medicine, improved treatment safety and efficacy evaluations, the increasing complexity of scientific questions, and the explosion of data from wearable and implantable devices.
Some time ago, people looking for answers to solve business problems realized that the information they sought resided in different places. It could have been in a file system, on an intranet, on the web, or in a proprietary database associated with a specific line-of-business application. What could be done to make sure employees and customers had a way to search once and get answers back from any source?