Technology in today's Age of the Customer is simultaneously increasing customer expectations and making service more complex. In the last few years, smarter algorithms, artificial intelligence (AI), self-service channels and analytics have exploded, and 56% of global consumers say they have higher expectations for customer service now than they had just one year ago. This wave of innovation is also bringing exciting opportunities for service managers to transform their brand's customer experience. Bluewolf, an IBM Company, predicted that AI will impact customer service in four key areas in 2018. Guiding -- Predictive and machine learning models to instruct next best action with the customer.

As Augmented Intelligence (AI) continues to rapidly progress into prominence across sales, service, and marketing for companies of all sizes, the anticipation that surrounds AI is met with skepticism among healthcare providers who question whether AI will diminish the personal touch of patient care and communication. More specifically, the concern about how AI impacts the patient experience comes with a side-eye towards automation in the call center. However, automation in the service of delivering value-driven and patient-centered care is designed to reinforce–not replace–the human connection between healthcare providers and their patients. Here are three ways AI is treating healthcare customers' paint points in the contact center. Patients expect their healthcare providers to be familiar with their medical record history as well as their record of calls, payments, and messages.

Global consulting agency Bluewolf, an IBM Company, has released its sixth-annual The State of Salesforce report that uncovers insights and emerging trends from Sales, Service, Marketing and IT professionals who use Salesforce, the world's #1 CRM platform. Based on insights from more than 1,800 Salesforce customers worldwide, the report found that companies are encouraged by the results of early artificial intelligence (AI) investments: 77 percent of companies already using AI expect to increase their investment over the next 12 months, and 38 percent of Salesforce customers expect to invest in AI within the next year. Bluewolf's report explores how some of the best companies are committed to ongoing innovation with Salesforce. Bluewolf defines the "best" as companies that are embracing the increasing necessity for smarter, faster decision-making and are embedding AI capabilities into Salesforce across their entire organization. Continued AI investment drives impactful customer experiences The C-Suite is placing big bets on AI, with 63 percent of C-level executives surveyed counting on AI to improve the customer experience.

We are in the early days of a promising new technology, and of the new era to which it is giving birth. This technology is as radically different from the programmable systems that have been produced by the IT industry for half a century as those systems were from the tabulators that preceded them. Commonly referred to as artificial intelligence, this new generation of technology and the cognitive systems it helps power will soon touch every facet of work and life – with the potential to radically transform them for the better. This is because these systems can ingest and understand all forms of data, which is being produced at an unprecedented rate. Cognitive systems like IBM's Watson can reason over this data, forming hypotheses and judgments.

Using deep learning to harness and explore large datasets has become increasingly important for businesses in every industry. There are many companies and services trying to make this a tenable problem, and yet, more people are still required to munge together home-grown solutions to meet their specific needs. Fortunately, there are many tools and resources in the market today that make building cognitive apps more doable. Here are nine interesting tools and resources I've seen and/or worked with recently to build cognitive apps: 1. Deeplearning.net: Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.