It’s not surprising that the primary focus of most of the CIO roundtables I am part of these days is getting AI apps planned, built, and deployed. But more recently, executive management has asked IT to justify these projects by documenting the benefits and value to the business. Gone are the days when you could simply do something cool that incorporated some obvious AI functionality.
Nowadays, management wants return on investment (ROI) calculations as part of any AI proposal. But how do you calculate ROI on something completely new and different—or on something as complex as AI, which brings with it lots of issues such as data privacy concerns, regulatory compliance complications, and all-new security risks? These considerations have forced many dev teams to reduce the scope of their AI plans—which is something of a relief for those charged with delivering on the promise of AI.
This is putting a new emphasis on building AI apps that can meet ROI goals by improving metrics that are already being measured while reducing the scope of AI projects to focus on the most compelling improvements (including delivering the largest ROI) for that digital system. This is a smart move. Dev teams can use existing metrics as guideposts for application design, evaluating the current apps to identify the most beneficial ways to use AI. And when a project is aimed only at improving current processes, not inventing something completely new, the scope is greatly reduced.
