McKinsey's 2016 The Future Of Machine Learning

U.S. retailer supply chain operations who have adopted data and analytics have seen up to a 19% increase in operating margin over the last five years.
Design-to-value, supply chain management and after-sales support are three areas where analytics are making a financial contribution in manufacturing.
40% of all the potential value associated with the Internet of Things requires interoperability between IoT systems.

Key takeaways from the study include the following:

Location-based services and U.S. retail are showing the greatest progress capturing value from data and analytics.
Machine learning’s greatest potential across industries includes improving forecasting and predictive analyticsPredictive Analytics describes the process of analysing data with statistical algorithms and machine learning in order to make prediction about future events based on historical data. A simple application is the weather forecast, more complex cases involve the prediction of consumer's behavior. .
Machine learning’s potential to deliver real-time optimization across industries is just starting to evolve and will quickly accelerate in the next three years.
Enabling autonomous vehicles and personalizing advertising are two of the highest opportunity use cases for machine learning today.
Designing an appropriate organizational structure to support data and analytics activities (45%), Ensuring senior management involvement (42%), and designing effective data architecture and technology infrastructure (36%) are the three most significant challenges to attaining data and analytics objectives.

McKinsey’s 2016 The Future Of Machine Learning