Explore our artificial intelligence research surveys, including qualitative and quantitative findings in healthcare, marketing, process automation, and more.
The last few years have yielded a tremendous amount of attention at the intersection of AI and healthcare, from DeepMind's partnership with the UK's National Health Service to IBM's continued pushes into areas of genomics and drug discovery. From the perspective of healthcare executives, however, many important questions are left unanswered and rarely addressed in detail: What difference are healthcare's machine learning innovations likely to make in the lives of patients? What disruptions should healthcare executives prepare themselves for now? How will the healthcare industry operate differently in 5 or 10 years into the future? We surveyed over 50 executives of healthcare companies leveraging AI. We aimed to do the hard work of separating the companies actually applying AI from those who use it as a buzzword (over 15 of our initial survey responses were turned down due to lack of evidence of real AI in use), presenting important predictions and industry insights in clear and interactive charts and graphs. The following research article is broken down into five sections:
When it comes to business applications of machine learning, marketing is always near the top of the list. Modern digital marketing offers a huge volume of quantifiable data for teams to work with, and marketing can be said to take precedent over other areas like customer service and business intelligence because of it's direct tie to driving revenue. Machine learning marketing applications are still relatively novel for most small and medium-sized business, but this may change drastically over the next five years.
Job automation predictions from an individual expert typically draw from years of academic research experience, or time "in the trenches" of industry. With growing interest and speculation on the job market of the next decade, we set out to garner a perspective as to what Silicon Valley thinks about the possibilities of automations in various business tasks. We wanted to know - what work functions have the most potential for near-term automation? In the infographics and article below, we explore the survey responses from nearly 80 Bay Area investors, founders, and tech folks - on which business functions have the greatest potential for automation today, and in the coming five years ahead.
In July of 2016, we sent out a series of survey questions to past guests who have been featured on the Emerj podcast, including academic researchers, founders, and executives who are experts in the machine learning domain. In this article, we focus on responses to the following question:
Busy and multitasking are understatements for today's executives and entrepreneurs. Machine learning has the potential to help make businesses more efficient, competitive, and profitable, but learning how it works and finding the resources to implement this technology takes time. Where to apply machine learning when first getting started is dependent on a number of factors - industry, structure, current problems - but having an idea of which solutions have proved most efficient for others and derived maximum return on investment is a helpful jumping off point.
Machine learning offers an opportunity to leverage competition and new forms of collaboration in order to yield new products, services, and entire business models... but machine learning misconceptions run rampant.
Unlike other components to an enterprises' technology mix, determining the ROI of machine learning is a less-than-obvious process, particularly when solutions are new and little by way of case studies or benchmarks exist.
AI investments have become more and more prevalent over the last 5 years, and articles in the Financial Times, TechCrunch, WIRED, and elsewhere tout an era of heightened interest in machine learning and artificial intelligence-oriented companies.
