A fast and resilient global supply chain is vital for businesses to meet customer demands and ensure operational efficiency.
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. The State of Alaska and Microsoft have formed a digital alliance to invest in
“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.” —Christian Thilmany, Director, AI Strategy, Microsoft Artificial
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. Since early 2019, I’ve worked with dozens of IT
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. As Microsoft CEO Satya Nadella recently observed during our earnings announcement, “we’ve seen two
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. Those graduating today can expect
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. In this time of COVID-19 Summit’s core goal of empowering attendees with
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. Traditionally, the success of Machine Learning models, and often AI algorithms, is measured by key metrics that
As Artificial Intelligence and Machine Learning continue to cement themselves as foundational resources for growth and transformation across the financial services industry, organizations must account for the added influx of data flooding into their enterprises. Every data science achievement must also account for how we secure and protect that data, making embedded data security risk management a far more pronounced need
Financial institutions are finding it difficult to insert AI into their financial risk products at scale. Financial institutions have been hiring data scientists, acquiring technology, doing POCs, in short, playing around with AI. While they understand it in theory, and may even put an AI project into production, getting it to run scale is another
As we enter the new decade, one thing is clear: the explosive growth of data science and AI has made the effective application of them a critical differentiator for any enterprise. Despite the near-universal acknowledgement of this phenomenon and the major investments being made, many enterprises struggle to deliver sustained value on their data science
For decades, manufacturing decision makers have made inventory purchases based on experience. Yet, as global trends like sustainability and digitization force markets to evolve, that ability to estimate future need based instinct and education has become less reliable.
