If you're familiar with machine learning, you know that some use cases are churn prevention, fraud detection, and predictive maintenance. All of those are trying to predict some kind of event happening, but it's not about actually intervening on that event. It's not about asking questions such as, "What would happen if I do this action." So for instance, in a dynamic pricing scenario, if you use machine learning, you're not really asking yourself the question, "What will have happened if I raise my prices? Will I still have the same number of bookings, can I have higher prices?"

Gradually recovering from the effects of COVID-19 pandemic, will be a top priority for practically every firm and industry in 2021. A few organizations may get stale or never recuperate. Others will see the purge as a remarkable opportunity to comprehend and improve their data and analytical assets, operationalize and update their model production process, and promise clients that their machine learning adoption can be trusted. Everybody is hoping to improve over their present AI and ML insights, for example, a bank improving fraud detection, a medical care provider moving to telehealth, a retailer or manufacturer attempting to make your supply chain more proficient. All through the recent years, there have been a couple of revelations in machine learning and artificial intelligence.