The steady rise of AI and machine learning is providing organizations with enormous value—helping to make sense of massive data sets, and find patterns that can automate programs across industries. AI can help companies create a seamless, personalized, and responsive experience for consumers— whether they are shopping for the holiday, saving for college, or considering a new car. Done right, AI can help companies identify, reach, and convert their target audiences in the right place, at the right time, with the right message. But done wrong, there can be unintended consequences.
The value of AI and automation is only as good as the underlying data sets that drive its algorithms. The complexity of AI means that there’s often little visibility into why and how data was interpreted. At best, flawed data will hamper the success of AI-powered programs, sending your message to uninterested consumers, or not generating a promised boost to sales or cost savings. At worst, the results can be more questionable, creating the potential for bias, and undermining desired objectives and results.
The COVID-19 pandemic has dramatically shifted consumer behavior, and thus the data associated with it. In fact, McKinsey recently found that 32% of executives at companies that adopted AI in sales and marketing during COVID-19, reported the failure of their machine learning models because they relied on data collected before the pandemic. So the question becomes, “how do I learn what the current reality is to build new training sets and models?”