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. The “consumerization of IT” as it’s often referred to, is a logical endpoint for ITSM considering the technology-driven consumer market that an organization’s employees are all a part of in their personal lives.
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 an enterprise search provider in the age of AI, it’s not easy to withhold the bigger idea that, “Heh, you can get a lot more with AI-POWERED SEARCH”.
Last week, my colleague Joe and I, along with 170,000 other customers, prospects, partners, and fans, descended upon San Francisco for Dreamforce.
Salesforce’s annual user conference, Dreamforce, gives participants insight into upcoming features, practical application of existing capabilities, and best practices across five days and more than 3,000 sessions. You can see some of the activity and performances (including Metallica and Janet Jackson) that were part of this massive event.
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. But we'd like to help you better understand what an AI search engine is.
Key Terms Related to an AI Search Engine
To make the explanations more accessible to the layperson, we’ve created a list of definitions for a number of key AI terms related to an AI search engine.
What is artificial intelligence (AI)? 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. AI for life sciences is becoming particularly relevant due to several trends that are converging and 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? The initial answer was federated search, which, on behalf of the user, submits the query to multiple repositories, and returns results back in a list, sometimes consolidated, often not.
It’s great to be in tech in Boston. With great schools developing talent and an ecosystem dedicated to supporting tech growth and success, the scene thrives. And nowhere is that more evident than at the annual Boston TechJam, the city’s biggest tech party festival and a time when we all come together to celebrate and accelerate our leading position in tech.
Recently, we spoke with a head of customer service at a US-based manufacturing company. He relayed how he periodically reviews recordings of support calls, to understand where coaching is needed or improvements can be made in the process. He shared how incredibly frustrated he was that in most cases, 80% of the call time is actually silence. Silence, while the agent searches for the right answer to solve the problem, and the customer waits.
What shocked us was the estimated cost of that silence – thousands of dollars per day; millions of dollars per year.
Imagine if that answer could be found 1 minute faster – or even 10? The savings for a team of 100 agents in 1 year could amount to over a million dollars.
With customer support & service at the forefront of the brand battle, it's no wonder that companies are turning to artificial intelligence (AI), such as a chatbot, for help. The customer churn caused by poor customer service is $62 billion problem, so finding ways to speed response time is no small matter.
On CMS Wire, David Roe took a look at "10 Ways AI Helps Improve Customer Experiences" based on a report from PointSource. The report found that of more than 1000 people surveyed, 83% said they'd be OK shopping with a brand that uses chatbots or other AI capabilities.