Application rationalization: A powerful catalyst for IT consolidation

whitePaper | November 30, 2022

The first-generation low-code platforms required considerable adaptations and months of development in the late 1990s and early 2000s. Gartner expects that by 2024, low-code/no-code automation will account for 65% of all app development.

whitePaper | June 14, 2023

Generative artificial intelligence (AI) has the potential to transform industries and society by boosting innovation and empowering individuals across diverse fields, from arts to scientific research. To ensure a positive future, it is crucial to prioritize responsible design and release practices from the beginning. As generative AI continues to advance at an unprecedented pace, the need for collaboration among stakeholders to ensure that AI serves as a force for good has become increasingly urgent.

whitePaper | August 17, 2020

For years, employees drove to an office, opened their computer, logged onto a corporate network with their username and password and got on with their workday. But working standard hours in a fixed office location is becoming more and more rare. In fact, a report by Gallup found that 43 percent of American employees work remotely.1 This growing mobile workforce requires the same, secure access to resources as their onsite counterparts — without connecting to the corporate network. And both onsite and remote employees need secure ways to access the expanding number of applications and resources that are hosted in the cloud. To accommodate, enterprise technology and IT practices must adapt.

whitePaper | February 15, 2023

Artificial intelligence (AI) is an enabling technology impacting the global economy and international trade. Combined with business-process-oriented automation and more efficient data flow exchanges, AI further promises to lift barriers to international trade, stimulate growth in global electronic commerce and allow for better predictions and associations to inform policy decisions.

whitePaper | July 10, 2020

There’s no arguing that preventing failures and accidents is critical for industry. Unexpected incidents can grind operations to a halt for extended periods of time and necessitate expensive repairs. Just 12 hours of downtime for an oil production platform could cost six to eight million dollars in lost production opportunity alone. A single day of grounding for a plane costs roughly four to five million dollars. Because of these disruptions, industrial sectors are always on the lookout for newer, better maintenance methods, and the approach on everyone’s lips right now is predictive maintenance. While everyone agrees on the name, there is less consensus on what it means or how to implement it. But to truly unlock the potential of predictive maintenance, it needs to be paired with artificial intelligence (AI) and machine learning (ML).

whitePaper | October 18, 2022

If you have worked in lending for even just the past year, you have already experienced its cyclical nature. Take mortgage lending, for example. Less than a year ago, the main problem facing mortgage originators was how to process high volumes of applications while still observing regulatory compliance and conformance to underwriting policies.