Data, AI, & Machine Learning

In response to increasing consumer demands for faster deliveries without added cost, more companies are implementing IT solutions that enable access to real-time sales data and inventory data across the whole enterprise. Real-time sales and inventory information, coupled with advanced analytics enables networks to accommodate fluctuations and changes in the business environment quickly, a quality the authors call distribution agility.

For more than a century, economies of scale made the corporation an ideal engine of business. But now, a flurry of important new technologies, accelerated by artificial intelligence (AI), is turning economies of scale inside out. Business in the century ahead will be driven by economies of unscale, in which the traditional competitive advantages of size are turned on their head.

Researchers are exploring how to create intelligent machines that work with us better as opposed to taking our place. Robots that can express human body language can have a positive effect on their human colleagues, enabling them to be more effective at their jobs, take on higher-level tasks, and realize psychological benefits. The overall result is a more productive human-robot team.

The 2018 Data & Analytics Global Executive Study and Research Report by MIT Sloan Management Review finds that innovative, analytically mature organizations make use of data from multiple sources: customers, vendors, regulators, and even competitors. The report, based on MIT SMR’s eighth annual data and analytics global survey of over 1,900 business executives, managers, and analytics professionals, explores companies leading the way with analytics and customer engagement.

Many executives are enthusiastic about the business potential of machine learning applications. But business leaders often overlook a key issue: To fully unlock the benefits of artificial intelligence, you’ll need to upgrade your people’s skills — and build an empowered, AI-savvy workforce.

Even if your job is high paying, it is wise to ask yourself whether it is common and repetitive enough to be done by a machine. If you conclude that it is, it’s time to look for — or create — less commoditized work.

Composing valuable strategies requires seeing the world in new and unique ways. It requires asking novel questions that prompt fresh insight. Even the most sophisticated, deep learning-enhanced computers or algorithms simply cannot generate such an outlook. Innovative strategies depend more on novel, well-reasoned theories than on well-crunched numbers.

The successful use of analytics in sports, both on the field and off, comes down to integrating analytics within an organization. Three strategies — collaborative analytics, a common language, and accessible technology — are key.

Identifying the optimal prices for products was once a time-consuming process. That’s changing as businesses start to take advantage of advances in machine learning, increases in computing speed, and greater availability of data.