VOGOSEN

Data-Driven ESG Strategy with AI-Powered Information Systems

AI-powered information systems should be the core of a data-driven ESG strategy: It is uniquely able in tackling the data challenges that ESG presents, and it helps organizations build lasting capabilities.
As ESG becomes an integral part of investing, the need to manage large quantities of often unstructured data brings both challenges and opportunities. Challenge, because the public is growing more wary of greenwashing, and because third-party rating services use inconsistent and sometimes opaque methodologies that derive dissimilar and sometimes contradictory ESG scores. Opportunity, because an evidence-based, data-driven ESG strategy backed by AI-powered information systems can not only address ESG issues in investing effectively and efficiently but also build lasting competitive advantage in an organization.
This is why we at Vogosen published a White Paper on adopting AI in investing. To help visitors to our website get the gist of this document, we are posting pieces of information that can be digested individually here. To get the full picture, however, we recommend that you download our White Paper.

Data-Driven ESG Strategy Needs AI

To collect, process, analyze, and utilize big data in an actionable manner, investors are best served by leveraging AI. AIs are versatile. They are be trained for a broad range of ESG-related tasks: from data collection and processing to analytics and visualization, specific tasks can be assigned to AIs without the need to increase the size of the analytics team. AIs are fast. They can work 24/7 at speeds that human analysts cannot match; there is also no performance degradation—in fact, AIs can perform better the longer they work on a task. AIs are also scalable. With faster hardware, they can process larger quantities of data during times of need; when computing demands are lower, they can be scaled back easily.

AI-Powered ESG Information Systems Build Organizational Capabilities

At Vogosen, we advise clients to build AI-powered ESG information systems progressively. Throughout the development and deployment of this information system, investors retain full control of their own data and their decision-making process.
With progressive deployment, investors are able to reap the benefits early-on. As bespoke modules are continually added and/or upgraded, they are able to see the payoffs in efficiency and effectiveness grow in ESG-related tasks. This allows all teams in the organization to familiarize themselves with the information system; and the boost in performance can be expected to make adoption easier.
At a functional level, an AI-powered ESG information system allows different teams in the organization to work together more smartly: Data can be shared more easily, and, as we demonstrate in the White Paper, information can be retraced and scores can be buttressed via ontologies with AI.
Together, these features mean that such an ESG information system helps investors build lasting organizational capabilities that underpin long-term competitive advantage.
Photo of Jiulin Teng

Jiulin Teng

Jiulin Teng has deep understanding of key topics in management science and new technologies. He also has broad knowledge in macroeconomics, business policy, and software engineering.
Upon completing his dissertation on the application of game theory in competitive strategy, Jiulin Teng worked as a consultant at McKinsey & Company (Belgium). More recently, he has launched and participated in a number of startups in blockchain, IT, and AI. He is a proficient database architect and is a full-stack developer with good command of modern programming languages. He has also taken an interest in Natural Language Processing.
Jiulin Teng holds a PhD in Strategic Management and Business Policy from HEC Paris (France). He also has a Master’s degree in Bioentrepreneurship from Karolinska Institute (Sweden) and a Bachelor’s degree in Biological Sciences from Nankai University (China).