Most customers don’t like calling customer support. It takes too long to get the answers they want, and they walk away feeling exasperated. Many organizations find themselves frustrated with their customer support experience as well. They know they need to provide a better, seamless experience for their customers, but they are challenged with information silos, an incomplete view of the customer, and a support team that has to hunt and peck to find the right information.
AI-powered search is delivering results for those using it, but we’re still just at the start of benefits the technology is truly capable of delivering. What’s more, companies that adopt early have a chance to run far out ahead by transforming themselves through innovation driven by artificial intelligence.
How many times have you switched your mobile phone service provider when the service or support was poor? How hard did that service provider work to keep you? It’s likely they didn’t try very hard. They have many customers, so losing one isn’t that big of a deal. But for companies that provide complex products like those in manufacturing, aerospace or oil and gas, a high-quality customer support program is critical. The question is, what does a quality customer support program look like?
Remember the Panama Papers? Those were the 11.5 million leaked documents detailing attorney–client information for more than 214,000 offshore companies associated with Mossack Fonseca, a Panamanian law firm that specializes in setting up offshore shell companies. Many of these companies were set up to “hide” money so wealthy individuals could evade taxes. Others seemed part of money laundering schemes.
The corporate intranet remains the lifeblood of any major enterprise. Over the last decade it has emerged as a key location for ideas, documents and collaboration. But for many it can also be a frustrating morass of information that remains difficult to find, forcing people to rely on email or instant messages to trade ideas and documents.
As we’ve explained in many blogs and 5-minute guides, a cognitive search platform should combine AI technologies such as natural language processing, machine learning, and knowledge graphing to deliver a contextualized search and discovery experience without compromising security. Those technologies can turn ordinary search into something much more powerful and transformative for any organization.
Today’s business users don’t search for documents that may have the information they seek buried within, instead they ask their systems for answers. This shift in attitude is a key driver behind the move to cognitive search.
When IBM Watson burst on the scene a few years ago by famously winning Jeopardy! people swooned. Here was a machine that seemed to live up to the promise of a science fiction future. IBM purported to show us all an artificial intelligence (AI) that could understand human language, sift through massive amounts of data, and provide answers to questions.
When life sciences companies develop new drug therapies or biomedical interventions, what typically attracts the most attention is news about the success or failure of clinical trials. But a lot of work goes on behind the scenes before these efforts ever reach the clinical trial stage. In fact, as life sciences companies decide what promising therapies should receive development resources, it’s often rigorous fact finding and research that determines a go or no-go decision.