Speaker
Description
This study presents a long-term forecast of the global market value of Artificial Intelligence (AI) in marketing from 2029 to 2038, addressing the pressing need for robust projections amidst rapid technological advancement and adoption uncertainty. Drawing on secondary data from Statista (2020–2028, including both actual and forecasted values in billion USD), the research employs and compares three distinct time series forecasting techniques: Polynomial Regression (degree 3), ARIMA (2,2,1), and Holt’s Exponential Smoothing. The findings reveal substantial variation across the models. Polynomial Regression projects aggressive market expansion, with the market value reaching approximately USD 829 billion by 2038. ARIMA offers a more moderate forecast of approximately USD 983 billion, while Holt’s Exponential Smoothing suggests a more conservative trajectory, estimating around USD 365 billion. These differing projections are interpreted through the theoretical lenses of the Diffusion of Innovations framework by Rogers (2003) and Organizational Learning theory by Argote & Miron-Spektor (2011), corresponding respectively to scenarios of rapid technological disruption (capturing innovators and early adopters), gradual organizational assimilation, and constraint-laden incremental adoption. Methodologically, the study underscores the utility of employing multiple forecasting models to account for volatility in emerging technology markets, in line with the recommendations of Hyndman and Athanasopoulos (2018). From a practical perspective, the multi-scenario, data-driven projections provide critical insights for strategic investment planning, policy development, and resource allocation within the domain of AI-driven marketing.
Keywords: Artificial Intelligence in Marketing, Market Value Forecasting, Time Series Analysis, Long-term Market Projection
