2018 Executive Round Up – Part 2

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Welcome to insideBIGDATA’s annual Executive Round Up designed to give our readers a sense for what the upcoming year is going to look like with respective to our focus technologies: big data, data science, machine learning, AI and deep learning. Weighing in with their thought-leadership predictions are some of the industry’s biggest players. We’re honored to pass along these comments to our valued audience.

In this article, Part 2 of the series, we’ll take a look at the industries taking the lead in 2018 for adopting AI, machine learning, and deep learning. AI is being deployed more and more frequently across a breadth of industries. Far from the typical futuristic depiction of robots with human-level intelligence, AI’s role in the enterprise today is frequently to automate tasks that humans have done in the past, such as picking products at a warehouse for order fulfillment, but also to find hidden patterns in large data sets that lead to actionable insights.

Machine learning in particular, has become an essential tool for the data-driven enterprise. At its core, machine learning is the process whereby a computer is given the ability to learn without being explicitly programmed. This notion may seem fairly opaque, but there are many excellent use case examples that highlight why this technology will have a long-lasting impact on business and society. Let’s ask our experts to highlight the technology and its capabilities as we move into 2018.

Daniel D. Gutierrez, Managing Editor and Resident Data Scientist – insideBIGDATA.com


insideBIGDATA: What industries do you see taking the lead in 2018 for deploying AI, machine learning and deep learning solutions?

Roy Kim, Director of Product Marketing for FlashBlade, Pure Storage

Data-centric industries will continue to take the lead in deploying AI. Automotive industry is in a race to gather as much image and sensor data to train cars to drive autonomously. SaaS companies live and die on delivering value on top of customer data. Financial industry makes decisions, whether it’s to make a split-second trade or to provide a mortgage loan to a potential borrower, based on historical and current data. ​


John (“Jay”) Boisseau, Ph.D., HPC & AI Technology Strategist, Dell EMC

Our Senior VP for Ready Solutions & Hybrid Cloud, Armughan Ahmad, and our team have recently articulated which verticals are most likely going to see widespread adoption and significant impact from machine learning in 2018. ​In short, while we expect to see adoption across all verticals, we think financial services, retail, healthcare, and manufacturing are well positioned to realize quantifiable benefits in 2018. We’re working with customers in all of these verticals right now, and look forward to sharing their success stories in 2018.


Pankaj Goyal, VP, AI Business & Data Center Strategy, Hewlett Packard Enterprise

There are two categories. First category is industries proactively adopting AI, trying to disrupt the industry. Characteristics are availability of huge amount of data, digitally aggressive, and strong buy-in from the C-level. Financial industry, consumer tech industry are good examples.

Second category is industries being forced to adopt AI, because they are being disrupted. Auto industry and retail industry are good examples.


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