Finance needs to transform before AI enters the picture. Here’s why.

Finance organizations seeking to benefit from AI should begin strengthening their infrastructure now rather than waiting for models to become fully reliable, as adoption remains limited despite widespread interest among CFOs. The gap between early adopters and slower-moving firms is expected to grow because legacy vendor contracts, fragmented data, complex manual processes, and organizational inertia make modernization increasingly difficult over time. Effective AI implementation depends on clean and standardized data, connected systems that provide a unified view of the business, and repeatable processes designed for automation; otherwise, AI may amplify existing errors and inefficiencies. Human oversight and feedback are presented as essential to improving AI outputs, while finance leaders are encouraged to shift from conducting analysis themselves toward designing, training, and governing AI-enabled workflows. The central measure of success is increasingly time to insight, as connected and automated systems can reduce the delay between detecting business changes and responding to them.

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