A Critical Shift in Thinking About AI and Big DataBy Guest Author | Posted 2016-12-21 Print
Success with these technologies hinges on how companies marry AI and big data with business domain expertise—a C-suite imperative that cannot be ignored.
By Lee Beardmore
Machine learning and big data have steadily increased their penetration of corporations over the past few years. And if we are to believe the articles we read, the era of artificial intelligence (AI) is now firmly upon us. It’s fair to say we are just starting to dip a toe into this new world, but with new advances being made regularly, we need to prepare for a very different future.
Putting aside the dystopian views that sensationalize AI, bright prospects are ahead for corporations that embrace this transition to new ways of thinking. However, to make the leap, some radical adjustments in the ways of working are necessary.
Right now, for many companies, AI and big data are viewed in a way that limits their potential. They are often viewed as solutions for cutting operational costs, rather than as a fundamental approach for driving increases in output, productivity and improved certainty over corporate direction. True success hinges on how organizations choose to marry AI and big data with business domain expertise, which makes this a C-suite imperative that cannot be ignored.
Expanding the use of AI and big data technologies can help a company’s leadership answer some big business and organizational questions, but deep domain expertise is a critical aspect of that journey. To ensure successful outcomes from AI and big data, here are some best practices for the C-suite to follow.
Become a Trendspotter
The success of today’s AI is predicated on significant volumes of data. And the more data it receives, the smarter the AI system can become. This combination dramatically improves the ability to predict not only the traditional areas scrutinized by business leaders—such as revenue, spend and sales—but also to bring together a diversity of data sets from internal and external sources to generate new sources of insight and prediction. As a result, the C-suite will be able to more accurately spot trends that point to potential issues or opportunities.
Allowing the technology to take care of these steps—which have until recently been the preserve of number-crunching teams—should empower better decision making … or even validate decisions suggested by the technology.
Identify, Retrain, Reassign
As companies begin to make this shift, one of the first steps will be for leaders to take a broad look at their organization and determine which functions would benefit from AI and big data solutions. There will be business gaps that can be filled. Staff will require retraining, and processes will need adjusting to take advantage of the new environment.
Security is an excellent example of how this concept can be put into action, as companies struggle to deal with ongoing cyber-threats and establish clear protocols to protect their assets. An organization that has automated its threat-detection analysis functions can leverage the people who were freed up and retrain them to fill other security-strengthening activities—or any IT or business function that requires them.
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