Automation in a Multi-Cloud World: The Key to Agility

whitePaper | November 15, 2019

The demands of running global supply chains can be unrelenting – perfection of operations, innovation in support of lastmile delivery, and assurances of sustainability in the pursuit of delivering the “perfect order” as promised. Now, transportation companies face the additional challenge of sharpening their core competencies while increasing the pace of innovation, adapting emerging technologies, and sharing data with their customers to improve transparency and efficiency. Long characterized by capital-intensive assets, forward-looking cargo companies are turning their attention to the data that comes from their own assets and infrastructure as well as from their customers. It is a complex transition, one that requires knowledge-based labor, automation, and predictive planning to turn transportation companies into digital platforms.

whitePaper | July 20, 2022

In recent years, the use of AI in organisations has drastically increased. As a result, lawmakers around the world have been paying close attention to the developments in the field of AI too. While there is no comprehensive legal body in Switzerland and the EU that regulates the use of AI today, there are clear signs that Swiss and European organisations should prepare for stricter regulation.

whitePaper | April 16, 2020

To your credit, you’ve done a little research and picked up a drawing tool here, an inspections tool, a bidding tool, and a financials tool. You’ve embraced the future or The Present, as it’s now called and are keeping abreast of ever-changing best practices.

whitePaper | September 28, 2022

As the speed of enterprise operations surge and expectations for quick responses rise, decision making increasingly migrates from data centers to the network’s edge. Whether it’s on the shop floor, where busy production lines must not stand idle, a doctor in a surgical suite waiting for answers, a fire crew needing orders to fight a frantically advancing wildfire, scientists looking for insights that will help a recovering coral reef, or a retail environment where impatient customers wait for help in making a purchase, enterprises must configure their systems to collect information, to develop actionable insights, and to provide decisions or answers in real time.

whitePaper | January 18, 2020

As organizations adopt digital transformation strategies to prepare for the future, a core idea for how to accelerate that transformation is the adoption of AI. While most organizations fundamentally believe in the promise of AI, they are still only just beginning to use it. Only 1 in 5.5 organizations are deeply committed to AI, while more than half are experimenting or beginning to see it around their organization.

whitePaper | January 5, 2020

Machine learning (ML) is an application of artificial intelligence (AI) that allows systems to automatically learn and improve from exposure to more data without being explicitly programmed. ML focuses on the development of computer programs that can access data and use it to learn for themselves.1 While AI represents the broader concept of machines being able to carry out tasks in an intelligent way, machine learning is a current application of AI based on the idea that we can give machines access to data, and they can use that data to learn for themselves.