Rameeta Chauhan

What’s keeping leaders from adopting machine learning? Well, tools are still evolving, practitioners are scarce, and the technology is a bit inscrutable for comfort. But five vectors of progress are making it easier, faster, and cheaper to deploy machine learning and could bring it into the mainstream.

With AI applications becoming ubiquitous in and out of the workplace, can the technology be controlled to avoid unintended or adverse outcomes? Organizations are launching a range of initiatives to address ethical concerns. With great power, the saying goes, comes great responsibility. As artificial intelligence (AI) technology becomes more powerful, many groups are taking an interest in ensuring its responsible use.

​Without data science, companies can't get full value from data, and there aren't enough data scientists to go around. But automation and training are giving companies access to data science without having to wage a war for talent. As cognitive and IoT technologies generate ever larger and more varied data sets, companies face the challenge of unlocking the value of that data. And those that are failing to effectively apply data science may be putting themselves at a competitive disadvantage.