SaaS, open source, and serverless: A winning combination to build and scale new businesses
Three tech approaches can rapidly accelerate business building for established companies that learn how to use them.
Three tech approaches can rapidly accelerate business building for established companies that learn how to use them.
Embedding AI across an enterprise to tap its full business value requires shifting from bespoke builds to an industrialized AI factory. MLOps can help, but the CEO must facilitate it.
ver the course of the pandemic, businesses have largely—and often successfully—adapted to new ways of working. They’ve also embraced digitization and reorganized their supply chains.
Companies capturing lasting value from artificial intelligence think differently, from the C-suite to the front line. Here’s how to make the shift from opportunistic efforts to a truly AI-enabled organization.
The “data” part of the terms “data lake,” “data warehouse,” and “database” is easy enough to understand. Data are everywhere, and the bits need to be kept somewhere. But should they be stored in a data warehouse, a data lake, or an old-fashioned database? It all depends on how that data is going to be used.
Over the past decade, advances in digital analytics have transformed the way businesses operate. From marketing and pricing to customer service and manufacturing, advanced analytics is now central to many corporate functions. The same, however, cannot be said for strategy—at least not yet.
Have you ever wished you could leap into your favorite cartoon and interact with characters like Bugs Bunny who entertain you onscreen? Welcome to the AT&T Experience Store in Dallas, where a life-size, high-definition Bugs Bunny greets you by name and tells you he needs your help to find several golden carrots hidden throughout the store.
Large-scale data modernization and rapidly evolving data technologies can tie up AI transformations. Five steps give organizations a way to break through the gridlock.