Strategic advice to leverage new technologies
Technology is at the heart of nearly every enterprise, enabling new business models and strategies, and serving as the catalyst to industry convergence. Leveraging the right technology can improve business outcomes, providing intelligence and insights that help you make more informed and accurate decisions. From finding patterns in data through data science, to curating relevant insights with data analytics, to the predictive abilities and innumerable applications of AI, to solving challenging business problems with ML, NLP, and knowledge graphs, technology has brought decision-making to a more intelligent level. Keep pace with the technology trends, opportunities, applications, and real-world use cases that will move your organization closer to its transformation and business goals.
Recently Published
Jason Radel explores the application of a digital twin framework for the ingestion, application, and visualization of digital twins and the integration of light detection and ranging data, photographs and scans, and other engineering documents. The article includes case studies from the energy and defense sectors, demonstrating how such an approach can be used in managing digital twins in different industries.
In this Advisor, Matthew Walsh explores the results of in-depth research conducted to identify the inequities that women of color experience in the technology industry. If employers can tap into a skills-similar, tech-eligible workforce revealed by the Equation for Equality, there would be nearly 250,000 more women of color in tech jobs today, double the number currently in tech.
Breaking through psychological barriers to entry is key to succeeding with any data management initiative. This is doubly true when seeking to adopt semantic standards to implement a knowledge graph within your organization — because change is risky. Application owners don’t want to give up control. Most key stakeholders don’t really understand the principles of data; they just want a near-term solution to an isolated use case. And the data dilemma is often viewed as too low-level for C-level executives to get their arms around. In this Executive Update, we explore how to fundamentally fix data so that it becomes a resource organizations can truly leverage.
In this Advisor, we argue that disruption in banking is not being driven by technological achievements like artificial intelligence, decentralized platforms, or mobile computing. Rather, changes are the result of: (1) technology commoditization and (2) industry actors pursuing strategic interests and repositioning themselves within the sector as disruptors, innovators, and fashion-setters. Envisioning a technology agora where technological artifacts are developed and commoditized and interested parties exercise influence over innovation choices, we will see that fintechs and banks are not so much competing with each other as they are collaborating.
To address the gap between DAO awareness and adoption, along with the prevalent misconceptions and skepticism surrounding them, we seek to understand why some DAOs succeed and others fail through an industry-specific lens.
Generative AI systems have exploded on the scene, and companies are using them for business. Currently, the more common enterprise uses of the technology include for automating design and for providing semiautomated responses to customer requests. That said, companies like Audi are employing generative AI to develop cutting-edge applications that can give them a leg-up on competitors.
Human-centric functional modeling (HCFM) is a way to allow computers to solve general problems. HCFM represents problems via constructs called “functional state spaces.” These hypothetical functional state spaces are special types of knowledge graphs used to model systems. Functional state spaces are required for general collective intelligence (GCI) and are of unprecedented importance if, as predicted, GCI can exponentially increase our capacity to understand systems.
This Advisor explores the use of generative artificial intelligence (AI) systems and other large language models for customer service and CX management. These systems are expected to significantly increase the ability of customer service platforms to provide detailed responses to customer service requests. Generative AI systems are also widely applicable for supporting and enhancing customer engagement as well.