Artificial Intelligence: An Early Enhancement In Different Verticals

whitePaper | June 30, 2022

Low code and no code (LCNC) development platforms expedite the creation of new applications with minimal or no coding requirements, as well as offer tools for non-programmers to create applications of their own. Low code development was introduced in the early 2000s to increase developer productivity. It uses a variety of approaches that automate and abstract application development activities, such as drag and drop editors, code generation, component assembly and model-driven development.

whitePaper | October 20, 2022

Artificial intelligence has already started to have an impact on urban settings at an unprecedented pace, with sophisticated solutions being deployed in the streets, at airports and in other city installations. In fact, cities are becoming experimental sites for new forms of artificial intelligence and automation technologies that are applied across a wide variety of sectors and places.

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 | December 15, 2019

The debate between experts over how automation will affect the future of work has been one of the most active cottage industries in labor economics in recent years. Numerous scholars forecast major disruptions of human work; others minimize those impacts. And yet, the field has nevertheless managed to generate a number of shared insights, with none more consistent than the finding that least welloff will suffer automation’s greatest shocks on the labor market.

whitePaper | January 12, 2020

“The services market for artificial intelligence technologies is rapidly maturing on the back of several years of learning and a realization—delivering AI is unlike delivering any other technology thus far. Delivering on the promise of AI calls for far more collaboration between service providers, tech vendors, and enterprise clients.” The biggest differentiator in enterprise AI services, as described by most of the customer leaders, is the quality of people—not just the quantity available for a specific skill. Domain understanding and data engineering capabilities are top priorities. A culture of innovation, experimentation, collaboration, and co creation with customers is the key winning formula here.”

whitePaper | May 20, 2021

AI has become a strategic imperative. Early adopters are achieving massive returns, while others risk falling behind. Yet, with AI knowledge and resources scarce, most companies cannot copy the FAANG approach. Even if their vast resources were more readily available, it’s hard to reboot an organization without disrupting the bottom line. Most executives need to work with the resources available — their existing people, processes, and technology.