We've spoken to many leaders in healthcare and pharma over the last half a decade, and when it comes to AI, the most pressing challenge that healthcare and pharma leaders report is that they're unsure of how to streamline and structure their data in a way that lets them build machine learning models. Healthcare companies are stuck in the data consolidation phase of their potential AI initiatives while vendor after vendor is trying to sell them on a new application that the company might not even be close to ready for. AI and machine learning projects can take months to get off the ground. Many pharmaceutical companies don't start seeing an ROI for half a year or more after launching an AI product if they see one at all. As such, it's important for pharmaceutical companies to clean and store their data so that it's "machine-readable," ready for feeding into a machine learning algorithm when the time comes. This is likely to save them time and money (thousands even) on an AI product's initial integration, whether the company makes it in-house or purchases it from an AI vendor.

The ability of companies to collect external data is likely to change the insurance industry as it is today. Traditionally, insurance companies would collect internal data from customers: data such as their weight, gender, and any family history of health issues. However, with the advent of technologies making possible the collection of data specific more to individuals than large groups of people, insurance companies could be able to offer unique, tailor-made policies to their customers.

Should I attend an artificial intelligence event or not? What event should I go to? Should I go to this event? Should I go to that event? We hear these questions a lot.

Which AI applications are playing a role in automation or augmentation of the retail process? How are retail companies using these technologies to stay ahead of their competitors today, and what innovations are being pioneered as potential retail game-changers over the next decade? Innovation is a double-edged sword, and as with any innovation results are a mixed bag. While many AI applications have yielded increased ROI--this case study of AI in retail marketing segmentation is one example--others have been tried and failed to meet expectations, shining a light on barriers that still need to be overcome before such innovations become industry drivers. Below are 10 brief use cases across five retail domains or phases.

Episode Summary: One of the promises of artificial intelligence is aiding humans in making smarter decisions. Whether it's in pharma, retail, or eCommerce companies, the idea of being able to pool together streams of data and coax out the insights that would help make the best call for the organization to reach its goals is the promise of artificial intelligence. As it turns out that same dynamic is sort of happening in the public sector where AI is now being used to inform policy. Previously, she was Program Director at the National Science Foundation. PhD in computer science and she runs the Data Science Initiatives at URI.

Natural language processing, or NLP, is one AI-based technology that's finding its way into a variety of verticals. We covered the business applications of NLP and where it comes into play in finance broadly in our previous reports. We intend to cover the technology's applications in banking specifically in this report. NLP might allow a company to garner insights that can be used to assess a creditor's risk or gauge brand-related sentiment across the web.

A 2017 Nielsen report titled "Young and Ready to Travel (and Shop)" revealed that the millennial generation travels more than any other generation, including Baby Boomers. The report suggests that, unlike previous generations, the travel industry will need to shift in order to cater to the millennial's unique preferences and "lack of predictability."

There is a certain level of stigma that exists around using machine learning and location data in business applications, understandably due to risks inherent in exploitation of individual privacy. But if we look under the hood of society's daily web of interactions, we see that the location information economy--from GPS to radio signal based-triangulation to geo-tagged images and beyond--is now almost ubiquitous, from the moment we track our morning commute to the end-of-day search for healthy and convenient take-out for dinner.

Stockbrokerage might be viewed by investors as a traditionally human-based service allowing them to buy and sell equities. When looking at the shift in how stock brokerage is different today compared to the early 2000s, the largest change seems to be in software-based automation. Put simply, a lot of what was being done by humans (such as executing trades, giving advice to investors, discretionary trading) can now be done through software.

Implementing artificial intelligence into an existing business is about more than algorithms. In fact, many AI researchers believe that algorithms are the easiest part of an artificial intelligence implementation. Algorithms need data, and for a business to assess, organize, clean, and use it's data requires ways of thinking that are entirely foreign to most existing enterprises. Partnering with Corinium Global Intelligence, we asked six experienced AI and analytics professionals (all speakers at Corinium's Chief Analytics Officer Spring event in on May 14th-16th in San Francisco) the following three important questions: In the sub-sections of the article that follows, we'll explore each of these questions in depth, highlighting the best insights from the professionals we corresponded with. IT procurement, software development, and software aren't new concepts to many experienced executives.