The API Developer Experience Enabling Rapid Integration

whitePaper | September 23, 2022

It’s been several years since organizations began adopting artificial intelligence (AI) to improve their business; few have come close to mastering its existing capabilities. A small number of organizations in our research aim to become AI-driven—a status we define as AI and machine learning underpinning almost everything the enterprise does—by 2025. However, this elite group—who we term “AI leaders”1—as well as the many others looking simply to embed AI more firmly in the enterprise foundations face formidable challenges to achieving their objectives.

whitePaper | September 12, 2022

Artificial intelligence is currently attracting considerable interest and attention from industry, researchers, governments as well as investors, who are pouring record amounts of money into the development of new machine learning technologies and applications.

whitePaper | September 13, 2022

Companies are going digital, whether their IT infrastructure is ready or not. Digital transformation initiatives like migrating to cloud promise speed, adaptability and reduced costs to effectively respond to today’s real-time, customer-focused world.

whitePaper | July 28, 2022

Before the pandemic, the path to AI adoption used to be slow and steady. According to the survey of industryleading firms, only 48% of companies deemed AI and Big Data a source of innovation. In 2021, this number has climbed to almost 65 percent.

whitePaper | July 23, 2020

LogicMonitor asked 500 global IT leaders from across the globe how their departments are evolving to maintain SLAs and business continuity amidst the 2020 global pandemic.

whitePaper | July 20, 2022

The growing diversity of actors and circumstances involved in AI/ML deployment makes establishing a set of ethical participatory practices especially difficult for this field. Even among practitioners working at the same organization, there can be substantial differences in both their knowledge about how AI/ML systems were created or will be used and their ability to incorporate inclusive practices. Additionally, the greater availability of AI development platforms, including “no-code” platforms, means that algorithmic models can be deployed without having deep expertise, expanding the number of circumstances automated systems are deployed in. This means many more instances where automated systems are being deployed without consideration of how the algorithms were developed, the provenance of the datasets and nature of the bias on which they were trained and tested, and the ethical implications of their development and deployment.