When starting out to deploy artificial intelligence (AI) and machine learning (ML), executives of legacy companies often view the challenges mainly as technical problems -- particularly finding sources of internal data to analyze and choosing the right tools. What they may not appreciate is just how data-rich their legacy companies already are. From utilities and mining, transportation and shipping, to financial services and more, legacy company operations and customer interactions generate a wealth of data. Such data can be harnessed to tackle a very wide range of issues: optimizing supply chains, predicting maintenance, reducing accidents, increasing production output, improving operational efficiency, raising revenue productivity, and growing customer value. To realize these opportunities using AI, however, legacy companies worldwide typically soon discover that their biggest problem is not technology -- it's talent.

The ability to deploy data as a competitive business asset is what has distinguished a set of well-established, data-rich companies who have reigned as market leaders over the course of the past several decades. However, business conditions evolve, and today, these companies face a new set of challenges that threaten their hard-won leadership positions. How do these well-established data leaders transform from excellence in traditional data and analytics -- of the kind that they have deployed in recent decades -- to leadership in a new era of Big Data, AI, and machine learning driven decision-making? What do companies that have excelled at disciplines like database marketing, CRM, one-to-one marketing, and advanced analytics need to do to continue to stay on top? Data and technology are driving business change.

As each year wraps up, experts pull their crystal balls from their drawers and start peering into them for a glimpse of what's to come. At BigML, we have been following such clairvoyant claims carefully this past holiday season to compare and contrast them with our own take on what 2017 will bring... and, our predictions may come across as quite unorthodox to some experts out there. For the TL;DR crowd, our crystal ball is showing us a cloudy (no pun intended) 2017 Machine Learning market forecast with some sunshine peeking through for good measure. To put it more directly, enterprises need to look beyond the AI hype for practical ways to incorporate Machine Learning into their operations. This starts with careful decision making when choosing an internal platform for your organization that will help build on smaller, low hanging fruit type projects that leverage their proprietary datasets.

As each year wraps up experts pull their crystal balls from their drawers and start peering into it for a glimpse of what's to come in the next one. At BigML, We have been following such clairvoyance carefully this past holiday season to compare and contrast with our own take on what 2017 will have in store, which can come across as quite unorthodox to some experts out there. For the TL;DR crowd, our crystal ball is showing us a cloudy (no pun intended) 2017 Machine Learning market forecast with some sunshine behind the clouds for good measure. To put it more directly, enterprises need to look beyond the AI hype for practical ways to incorporate Machine Learning into their operations. This starts with the right choice of internal platform that will help them build on smaller, low hanging fruit type projects that leverage their proprietary datasets.