Capitalize on AI Investments by Focusing on Operational Decisions

Consumer preferences and competitive pressures are driving businesses in all industries to offer more services online. To help them make better, faster, unbiased, and accurate business decisions, businesses are turning to data, analytics, and artificial intelligence (AI). Yet the International Institute for Analytics reports that AI investments remain speculative, with successful deployment rates of less than 10 percent.

At Enova Decisions, we know businesses face hurdles when it comes to operationalizing AI for decision management. We recently sponsored a Harvard Business Review Analytic Services Pulse Survey to get an even deeper understanding of those hurdles and the current attitudes of executives towards AI initiatives. The results of that study— The Analytics-Driven Organization: Making Real-Time Business Decisions with AI —are now available.

Some of the more interesting findings:

  •  71 percent of companies say they’ll be more effective using AI than their competitors.
  • While 64 percent are investigating or piloting AI projects, only 15 percent are actually “using AI”.
  • Executives are almost twice as likely to consider AI for strategic and tactical initiatives, rather than for its operational benefits.

This last finding is surprising, especially for fintechs, since the operational benefits of AI include assessing customers’ credit worthiness or insurability, preventing fraud, and improving accounts receivables and collections activities. Yet an average of only 38 percent view their organizations as being strong in these three operational areas.

Of course, these figures represent only a tiny slice of what our research uncovered about today’s applications (and tomorrow’s planned ones) of AI and analytics for business decision management.

Download the full report to read the full story.

Enova Decisions

About

Enova Decisions is an analytics and decision management technology company that was formed in 2016 to enable businesses in various industries, including financial services, healthcare, and telecommunications, to automate and optimize operational decisions, in real-time and at scale, through data, machine learning, and the cloud.