In the rapidly emerging artificial intelligence economy, organizations will split into two groups: those who are good at AI and those who are bad at business. Most experts agree that AI won’t replace humans, but instead augment us in a world of mixed autonomy. You’ll need new structures to harness AI’s transformative potential while managing its very real risks—the biggest of which is data privacy. So how can leaders handle AI and privacy risks?

The human brain is a prediction machine. It sees patterns, then makes predictions from previous experiences. This part of human intelligence has been critical to our survival. For example, many years ago, a forager might have eaten a particular berry, gotten sick, and thus learned the clues that indicate that a berry is poisonous. This would happen automatically—we’d get nauseous when seeing the berry again, which would make us steer clear.

Ready or not, AI workflow automation is poised to transform business operations from the shop floor to the C-suite in the AI economy. The situation is a byproduct of the generative AI boom. And yet, so many companies have hardly scratched the surface of AI automation’s full potential in their business operations. The more these companies get AI workflow automation to work for them, the more agility, efficiency, productivity, and economies of scale they’ll get back for themselves.

For business and IT leaders, digital transformation in the financial services industry continues to pose a challenge but is necessary to keep pace with changing regulatory requirements, consumer demand, and budget constraints. It's also important to prevent operational areas from growing their expenses unintentionally. In the typical boom and bust cycle, banks can feel pressure to roll out new financial products, services, and business lines.

Casework is an intricate process that involves the assembly and management of a myriad of files, tasks, and personnel. At its core, casework aims to build a comprehensive narrative or profile, drawing together disparate pieces of information to construct a cohesive whole. The complexity of case management arises from the diverse data sources and numerous stakeholders involved in the process.

In a business environment where automation is becoming increasingly essential, understanding the trajectory of robotic process automation (RPA) is more important than ever. But what exactly can we expect from the future of RPA in 2024? How will the bots themselves change? How will the market change? And what will these changes mean for buyers? In this post, we’ll look at the changes you can expect to see in RPA this year.

Artificial intelligence is changing the world. With use cases ranging from content generation to deep data analysis to detecting health issues, AI can greatly improve lives and enhance business outcomes. And with the explosion of generative AI services and large language models, we can expect AI to become even more ubiquitous than it already is. But AI isn’t perfect. In particular, AI privacy issues put organizations at risk or prevent adoption in the first place.

At most federal government agencies, acquisition teams are understaffed and overworked. They have open positions that they can’t seem to fill. And their acquisition “systems” aren’t helping matters. Rather than working with one integrated government acquisition system, many agencies rely on separate applications that each perform one function. And the applications don’t communicate with each other or integrate with the financial system.

AI automation is changing the game in business operations. For many companies, global competition is heating up fast on an increasingly crowded playing field. In the past, business leaders knew their competitors and how they operated. But now, executives across industries have to look over their shoulders for new challengers that arrive with surprising speed from virtually any corner of the globe.