AI Strategies for the Data-Light

The custom of storing silos of data for usage by AI technologies is a rather new one, and most companies today are at a stage where they have just started or are very early in the process of making this tumultuous transition one step at a time. Hence, issues such as a lack of structured data is rampant, and the belief that they can't be used currently is widespread as well. The argument to be made here is that the right AI models may, in fact, be powerful enough to use even in such data-light environments. McKinsey finds, for example, that not only do AI models have clear advantages over spreadsheet-based counterparts, but aspects such as AI supply chain management can, in fact, reduce errors by anything between 20-50%, reducing lost sales and product unavailability by almost 65% already. "Continuing the virtuous circle, warehousing costs can fall by 5 to 10 percent, and administration costs by 25 to 40 percent. Companies in the telecommunications, electric power, natural gas, and healthcare industries have found that AI forecasting engines can automate up to 50 percent of workforce-management tasks, leading to cost reductions of 10 to 15 percent while gradually improving hiring decisions--and operational resilience."