Yet even though it's been a cause of prominent business snafus this decade, the concept of AI drift still poses challenges many struggle to fully understand, especially in data-dependent industries like mortgage. Understanding where an AI tool obtains its knowledge is as important in discovering the causes and circumventing drift, tech leaders add. Instead, companies often fall into the trap of building an AI model and think their work is done, save for basic maintenance, he added. Apart from comparing results against prior outcomes, a strategy companies can also use is testing AI models against others that have already proven their ability to deliver quality output. Although intensive, AI model oversight, in the end, will serve a company's best interest, paying dividends as its data changes.