AI-driven Business Models in the Data-information-intelligence Economy: The NRT Methodology

The exponential rise of Artificial Intelligence (AI) coupled with the continuous growth of big data led to the need for transforming traditional business models into AI-driven business models. These AI-driven business models are crucial in providing much needed Strategic Intelligence (SI) to strategic decision-makers. This research paper seeks to introduce the nominal ranking technique (NRT) methodology as a strategic methodology for streamlining the management of big data towards innovation. The NRT methodology is a structured and systematic approach, innovatively developed for the management of big data. The NRT methodology is further reinforced with the Pareto principle, also known as the 80/20 rule. The Pareto principle in the management of big data considers that 80 percent of insights derived often stems from 20 percent of the big data. When the NRT methodology is applied in the management of big data, four primary elements are considered, namely - 1) standard of inclusion of data, information and intelligence, 2) data cleansing, 3) relationship of data, information and intelligence, and 4) VARCS principles. VARCS refer to the validity, accuracy, reliability, credibility, and sufficiency of data, information and intelligence. The NRT methodology considers the volatile, uncertain, complex and ambiguous (VUCA) environments in which the world operates. The NRT methodology is an iterative process that can be applied in large-scale data-information-intelligence management initiatives, as it encourages adaptability based on ongoing findings and challenges. Overall, the strategic insights derived from the application of the NRT methodology highlight its potential as a valuable tool in the data-information-intelligence economy. By enhancing decision-making frameworks, promoting collaboration, identifying key variables, providing visual representations of data, and fostering continuous improvement, the NRT methodology significantly contributes to the effectiveness and success of complex business environments. Research data collected and analysed from content analysis determined that businesses are concerned about the strategic nature and use of data, information, and intelligence for strategic decision-making and innovation.