Data, Analytics, and Artificial Intelligence Readiness

Today, we were sifting through some old stuff. By “old” we mean from a couple years ago. Among this stuff were some assorted slide decks and summaries from an October 2015 Gartner IT Symposium. This particular conference is a very big and prominent one held each year in Orlando, Florida for CIO’s, IT professionals, and various software and IT-type vendors—touted as, “The World’s Most Important Gathering of CIOs and Senior IT Executives”. Of all the summaries and predictions—mostly referencing what would happen by the years 2018-2020—far enough out of reach of the memory of most conference-goers once they actually reach 2018—there was no real mention or focus on artificial intelligence, deep learning, or machine learning. No signal to CIO’s, “Hey this thing is coming.” In the Executive Summary report, Gartner summarized, “At Gartner Symposium/ITxpo, we heard how organizations of all types and in all geographies are engaged in a fast-moving environment in which all the players are furiously experimenting. Business goals change more rapidly than traditional IT can respond. As a CIO, you must pursue agile practices to compete at the digital speeds while focusing on rock-solid IT reliability.”

Fast forward to present day, now the Board of Directors of your company who has been coached by some self-professed Digital and AI Strategist has informed the CEO to bring the CIO to the next board meeting. They have questions. What’s the company’s strategy for incorporating AI? What are we doing with Deep Learning? How will we use this technology to predict outcomes while transforming the organization to know when and how to apply those predictions and act upon them? The list of such questions goes on and on.

Quickly, such an organization might seek out expensive and premature expertise. Expertise that did not really exist a couple of years ago in the corporate domain, nor did it even matter if it did.

Here at Hybrid Intelligence, we can train our newer dedicated AI servers on all sorts of, say, image types—say cats and dogs to keep the example simple—and then an available gradient descent deep learning algorithm can discern and predict from any data set we feed of cat and dog images (e.g., let’s say we give it a picture of your dog or cat) with greater than 99% accuracy whether it’s a picture of a cat or a dog. Is this impressive? No, not at all, it’s the state of the art, which for every type of deep learning task progresses and improves each and every day. A couple of years ago, the state of the art for this type of task was slightly greater than 50%. Maybe that is why this technology scantly got even get a mention at that Gartner conference—it was only slightly better than flipping a coin to guess cat or dog!

The lesson here has nothing to do with the accuracy of Gartner predictions in any given year. The lesson here is what you are told a couple of years from now is important may not exist, be relevant or important today. We think what you can do to prepare for this type of future is to get the basics and foundational things right. These will poise you and your company to be prepared not only for artificial intelligence, but everything else to come down the road.

• If your company is not good at analytics, it may not be ready for AI or the ensuing technologies to come.
• Embracing data and analytics is not a tactic; it’s a transformation.
• Get the basics right (and mostly automated). Companies that rush into artificial intelligence investments or projects without having reached a critical mass of data and analytical automation and processes around those structures can end up paralyzed.
• Your data, analysis, and analytics and future technologies function(s) should be key contributors to the development and execution of the business strategy by supplying insights and predictions (and perhaps prescriptions for the more advanced among us) into key areas, such as customers and products, unmet market opportunities, emerging trends externally, and so forth.

These are just a few of the things we believe and espouse here at Hybrid Intelligence. And we also believe that with solid execution, a getting-things-done and outside-the-box approach, a zealotry for excellent service, and solving difficult data and analysis problems for customers with our software and outsourced services, we help to poise them for the future—whether it’s the known or unknown.