Technology has given us the ability to forecast enterprise trends and predict success in ways the business leaders of yesterday couldn’t fathom. In the past, successful businesses had to rely on small sample sizes, simple questionnaires, and other ways of gathering of data to predict general trends, but not anymore.
A few years back I jumped on the “machine learning will eliminate the need for radiologists” bandwagon. It wasn’t my smartest prediction. In my failure, however, I’m joined by the biggest experts in deep learning, like Geoffrey Hinton, who in 2016 proclaimed it was “just completely obvious [that] within five
Bloomberg estimates that the metaverse market may grow to $800 billion by 2024, and Facebook has changed its name to Meta to capitalize on this looming technology. The metaverse is a collection of immersive online technologies that include virtual reality, augmented reality and interactive video. At the corporate level, CIOs
What makes a company “future ready”? The author analyzed top companies by revenue across four sectors, measuring seven equally weighted factors, then analyzed what leading companies were doing differently. They discovered industry specific insights, which also informed more universal lessons. First, don’t play zero-sum games with disruptors. Looking at the
The pandemic could have been the moment when AI made good on its promising potential. There was an unprecedented convergence of the need for fast, evidence-based decisions and large-scale problem-solving with datasets spilling out of every country in the world. Instead, AI failed in myriad, specific ways that underscore where
While concerns about AI and ethical violations have become common in companies, turning these anxieties into actionable conversations can be tough. With the complexities of machine learning, ethics, and of their points of intersection, there are no quick fixes, and conversations around these issues can feel nebulous and abstract. Getting
AI influences decisions across the enterprise, but bias can do far-reaching damage to trust and stakeholder relationships. The good news is that there are ways to protect yourself. A large regional bank uses a newly developed fraud detection artificial intelligence (AI) algorithm to identify potential cases of bank fraud including
The Next in Science Series provides an opportunity for early-career scientists whose innovative, cross-disciplinary research is thematically linked to introduce their work to one another, to fellow scientists, and to nonspecialists from Harvard and the greater Boston area. This year’s program focuses on innovative applications of data science to a
The explosion of smart devices and increased automation of physical tasks are extending IT’s remit to include device and data management, edge computing, governance, and more. With the wide availability of advanced processors and sensors, industrial robots, and machine learning, any device can be smart, connected, and capable of capturing
Emotional intelligence matters more to one’s success as a manager than IQ or technical skill. The principal takeaway: emotional intelligence is just as important as any “hard skill” and investing in it helps individuals and teams succeed at work. Companies are wise to explore AI solutions that can help make
The idea of artificial intelligence – job – killing robots, self – driving cars, and self – managing organizations – captures the imagination, evoking a combination of wonder and dread for those of us who will have to deal with the consequences. But what if it’s not quite so complicated?
First, there was a data warehouse – an information storage architecture that allowed structured data to be archived for specific business intelligence purposes and reporting. The concept of the data warehouse dates back to the 1980s and has served businesses well for several decades – until the dawn of the