AI can play a significant role in anticipating and, ideally, circumventing inflation trends by analyzing large volumes of data, identifying patterns, and making predictions based on historical trends and current economic indicators. AI-powered predictive analytics can enhance the accuracy and timeliness of inflation forecasts, enabling policymakers, businesses, and investors to make more informed decisions and better navigate the complexities of the global economy.
Naturally, this capability retains rich utilities for food supply chain stakeholders.
AI can do so through practicing the following means:
Economic Data Analysis.
Natural Language Processing (NLP).
Machine Learning Models: ML algorithms integrating regression analysis, decision trees, and neural networks for building predictive models.
Sentiment Analysis: Weighing global and social media, survey data, and industry newsletters.
Predictive Analytics: Through digesting a combination of historical data, economic indicators, and external factors such as geopolitical events or natural disasters.
Policy and Trade Risk Assessments: Domestically and internationally sourced macroeconomic and geopolitical data.
…and/or Scenario Analysis. Source
