Frontline workers are the foundation of every industry—from retail and healthcare to hospitality and field services. Yet for years, they’ve been asked to increase productivity and deliver more value, faster, often with tools that weren’t designed for the specific realities of frontline work. Today, that dynamic is shifting. When AI is applied in practical, governed
Sustainability is about survival. We want our businesses and communities—our species and planet—to carry on without running out of essential resources. Sustainability drives such important outcomes for business and society that it has become a compelling organizational priority. Microsoft has ambitious plans around sustainability.
Financial services institutions are increasingly using artificial intelligence (AI) to automate and augment their decisions. But when it comes to which AI algorithms to use, fairness and transparency must factor into the equation. Black box AI: a cautionary tale Some of the algorithms under heavy consideration today are called “black box” algorithms.
2020 is a time of challenge and opportunity for small businesses who want to prioritize their digital transformation journeys. Fortunately, tools that weren’t seen as feasible for small businesses due to costs and resources are now becoming the norm for SMBs.
Machine learning holds the promise of saving time and money while increasing accuracy and efficiency across a variety of business use cases – but there can be pitfalls on the road to implementation.
Declining oil prices are creating challenges for their economy which derives 90% of its revenue from oil and natural resource production. The State has turned to Microsoft with a need to adapt education and workforce skilling, reduce costs, and streamline operations.
“Diversity expansion can then prevent bias. Only through education can organizations understand the critical need to hire a diverse talent base. It may not be possible to have what some call an “unbiased brain”, but it is possible to bring diverse brains to the table to help prevent it.
Change is no easy task. As with most new technologies, AI adoption brings its share of cost and risk as enterprise leaders and department leads work together to build out their experience. Merely identifying an entry point has brought plenty of AI projects to a halt.
Disasters strike suddenly, and emergency management needs rapid, scalable access to data and collaborative tools that will empower their response. That’s when they turn to Juvare, one of the most experienced and established emergency response tech providers in the world. To manage the scalability and speed that emergency responders need to face the challenges of their jobs, Juvare turned to the cloud for their WebEOC and eICS platforms. With cloud infrastructure, they were able to navigate geographical data restrictions and disruptive deployment to bring comprehensive solutions to the communities they served.
Companies today are focused on keeping employees safe while trying to still meet the essential needs of their customers. But we know from our conversations with our customers and partners throughout the United States that business leaders are also beginning to think about how best to adapt and drive recovery as we emerge from this crisis. The business world is changing at an extraordinary pace.
Tom Lawry | National Director for AI, Health and Life Sciences Ours has been called the information age. There is probably no place where this name rings truer than healthcare. In the 1950s A newly-minted physician would go their entire career before seeing medical information and knowledge double.
As an on-going celebration of growth, innovation, and acceleration across the finance industry, this year’s Money Management Institute Summit focused on The Value-Add Imperative — how financial service providers can go beyond their traditional services to positively impact the lives of their customers.
Companies across the Financial Sector are using AI and Machine Learning to model customer behaviors, avoid risk, and streamline critical business processes. Consequently, building and testing Machine Learning models has become an important discipline for many financial institutions.
