Forget The ROI: With Artificial Intelligence, Decision-Making Will Never Be The Same – Forbes
People are the ultimate power behind AI.
There are a lot of compelling things about artificial intelligence, but people still need to get comfortable with it. As shown in a recent survey of 1,500 decision makers released by Cognilytica, about 40 percent indicate that they are currently implementing at least one AI project or plan to do so. Issues getting in the way include limited availability of AI skills and talent, as well as justifying ROI.
Having the right mindset is half the battle with successfully building AI into the organization. This means looking beyond traditional, cold ROI measures, and looking at the ways AI will enrich and amplify decision-making. Ravi Bapna, professor at the University of Minnesota’s Carlson School of Management, says attitude wins the day for moving forward with AI. In a recent Knowledge@Wharton article, he offers four ways AI means better decisions:
AI helps leverage the power and the limitations of tacit knowledge: Many organizations have data that may sit unused because it’s beyond the comprehension of the human mind. But with AI and predictive modeling applied, new vistas open up. “What many executives do not realize is that they are almost certainly sitting on tons of administrative data from the past that can be harnessed in a predictive sense to help make better decisions,” Bapna says.
AI spots outliers: AI quickly catches outlying factors. “These algorithms fall in the descriptive analytics pillar, a branch of machine learning that generates business value by exploring and identifying interesting patterns in your hyper-dimensional data, something at which we humans are not great.”
AI promotes counter-factual thinking: Data by itself can be manipulated to justify pre-existing notions, or miss variables affecting results. “Counter-factual thinking is a leadership muscle that is not exercised often enough,” says Bapna relates. “This leads to sub-optimal decision-making and poor resource allocation.” Casual analytics encourages counter-factual thinking. “Not answering questions in a causal manner or using the highest paid person’s opinion to make such inferences is a sure shot way of destroying value for your company.”
AI enables “combinatorial” thinking: Even the most ambitious decisions are tempered by constraints — to the point where new projects may not be able to deliver. “Most decision-making operates in the context of optimizing some goal — maximizing revenue or minimizing costs — in the presence of a variety of constraints — budgets, or service quality levels that have to be maintained,” says Bapna. Needless to say, this inhibits growth. Combinatorial thinking, based on prescriptive analytics, “can provide answers,” he says. Combinatorial optimizations algorithms are capable of predicting favorable outcomes that deliver more value for investments.