A fast and resilient global supply chain is vital for businesses to meet customer demands and ensure operational efficiency.
The needs of the manufacturing industry have been constantly evolving, and the companies that are achieving the greatest success are the ones that view these ever-changing circumstances as opportunities to innovate. Embracing new technology is the key to navigating paradigm shifts. By using unified data models, building agile factories, and innovating the supply chain with AI,
Some of the most interesting scientific discoveries happen at the intersection between disciplines—and sometimes those discoveries can even save lives. That’s what two researchers at the University of North Carolina learned with organ transplantation, when they set out to find a better way to match donors with recipients.
Whether you’re using an electronic payment app to buy takeout for dinner, accessing full-service banking from a laptop at home, or trading cryptocurrencies, you’re probably aware the digital revolution in financial services is quickly moving forward. At Microsoft, we’re eager to see how the industry will continue to evolve. To gather a variety of opinions on
What AI experts see next as the technology expands AI is a transformational technology impacting organizations across various industries—from providing new insights to improving operational efficiency to enhancing customer experiences. At Microsoft, we’re working to turn meaningful innovation into actionable results. We asked thought leaders inside and outside of Microsoft about the acceleration and impact
Data-driven organizations have a strategic advantage over their competition. They see material growth in worker productivity, top-line revenue, and margin, as well as increased agility around mergers and acquisitions.
Like all businesses, commercial businesses should pursue sustainability strategies because they enable them to respond to rapidly growing market interests. It’s not just the right thing to do, it’s smart business. The future of our planet, our society, and our people depend on all of us being good stewards of our environment. There are five
S. Craig Watkins is the Ernest A. Sharpe Centennial Professor at the University of Texas at Austin, an internationally recognized expert in media, founding director of the Institute for Media Innovation, and the Lead Principal Investigator for UT’s Good Systems Designing AI for Racial Justice research project. Before coming to Microsoft, Stephen Elkin served as
The world is very different today than it was just a year ago. The realities of COVID-19 have challenged us all. The pandemic rearranged our health and wellness priorities while teaching us some valuable lessons. When faced with a crisis, we’ve learned that health systems and health leaders are capable of agile transformation. One need only look at the heroic efforts of doctors, nurses,
The rapid pace of innovation in artificial intelligence has led us to the next stage of AI development, which we call “industry verticalization”: designing AI products to meet the needs of specific industries.
In a webinar entitled “The Art of Possible: Manufacturing Priorities that Will Define 2021,” Çağlayan Arkan, Microsoft’s VP of manufacturing industry shared: “From extreme disruption comes extreme transformation.” His point is well taken. In the aftermath of the pandemic, supply chain has raced to the top of the news and manufacturing is centerstage. Arkan followed
To some extent, the term resilience has become 2020 shorthand for “surviving a global pandemic.” But as technical professionals know well, the goal of resilience long predates the cataclysms we’ve faced this year. As Mark Russinovich, CTO, Microsoft Azure, states: “Resilience is the ability of a system to fail gracefully in the face of—and eventually
There have been many articles written about how supercomputing in the cloud aids in performing compute tasks faster and more cost-effectively. However, with this new capability, both business and IT decision makers are now focusing more on how to manage the much larger volumes of produced data. More model runs and more granular results have
