Many companies are still not seeing significant impact from their AI efforts. Some experts say this may be because they’re not embracing something called “organizational learning.”
It’s not enough to use AI to optimize a business process — for example, to make better predictions or automate a manual task. Enterprises need to go one step further, to take lessons learned from their AI projects and use them to transform their organizations.
While most, if not all, organizations would say they learn from their successes and their failures, few have formal processes to embrace these learnings and promulgate them throughout the enterprise — especially when it comes to the use of AI. As a result, only 11% percent of companies saw significant benefits from their AI initiatives in 2020, according to a recent report from MIT Sloan Management Review conducted in collaboration with Boston Consulting Group.
Take, for example, scoring loan applications, much of which involves tedious data entry done manually by loan officers. Using AI or machine learning can dramatically optimize the process, reducing costs and the need for as many loan officers on staff. But enterprises can only save so much money, and employees are reluctant to get behind projects that could cost them their jobs.
Meanwhile, AI can also be used to glean new insights from that same loan application data. A bank could discover underserved customer segments, for example, that could lead to a dramatic expansion of business. Or a bank could discover that people were afraid to apply for loans because of worries about damaging their credit ratings, says Sam Ransbotham, professor of information systems at Boston College’s Carroll School of Management and coauthor of the MIT Sloan report. Offering them an opportunity for a no-risk assessment that doesn’t affect their credit rating could change that.