Improving efficacy of AI models during times of business disruption

While most AI in use today can be classified as early-stage advanced analytics, some enterprises have built large data science teams to apply machine learning and deep learning algorithms to business processes. Many of these enterprises have built the necessary support infrastructure to train these algorithms on large data sets, deploying the resulting AI models to production to generate business insights. However, many consumer and business consumption patterns changed dramatically in 2020, causing these advanced AI models to fail or behave erratically. Many of these models that have been trained to make predictions based on historical data have not been able to deal with the data anomalies created by disruptive business conditions and changing preferences. Companies using AI for insights had a hard time making use of existing models in production.