India, March 27 -- Financial forecasting has traditionally been seen as a function of accuracy and analytical depth. In practice, however, its effectiveness has just as often been shaped by timing. By the time numbers are compiled, reviewed, and presented, the assumptions underlying them have already begun to shift, and the relevance of the output starts to diminish.

This inherent lag has been one of the more persistent constraints in financial planning. Even well-built models struggle to keep pace with fast-moving business realities, which limits their usefulness in decision-making at critical moments.

Generative AI begins to address this in a meaningful way. Its value is not in replacing financial models, but in compressing the distan...