AI ROI In Banking Requires More Than Measuring Adoption

Rohit Bhansali is Vice President of Software Engineering at Goldman Sachs.

Banks are investing heavily in artificial intelligence, but the more important question of “How much are they actually getting back?” is becoming increasingly difficult to answer.

It is not enough to know that teams are experimenting with AI, building proofs of concept or deploying new tools. Leaders need to understand whether the output returned by AI is valid, what guardrails should exist around its use, what testing endpoints are required and, ultimately, how much return the organization is receiving from its investment.

For banks, the goal should be to connect the resources AI consumes with the productive outcomes it generates, comparing its performance with the traditional alternative and ensuring its output is valid.

Start With The Cost Of AI Resources

The first question is relatively straightforward: What does the AI actually cost to operate?

Banks need visibility into resource utilization, including how many tokens are being used and how much compute is being consumed. They also need to know where those resources are being used and whether that usage is producing anything meaningful.

From there, the organization can begin comparing AI against the alternative.

For example, if I am using AI for software development, one way to think about ROI is to ask how many man-days or man-months it has saved compared with a person performing the same work. First, understand the resource cost, then measure the output against the human alternative.

That creates a much more practical starting point for evaluating AI ROI than simply tracking how many employees are using AI or how many AI applications a bank has launched.

Measure What AI Actually Changes

Ultimately, an AI investment should create an economic advantage.

In the simplest terms, if I am reducing cost or getting my work done faster, my returns are better.