Subject. Management of the process of creating and scaling digital products based on artificial intelligence under conditions of high technological uncertainty. Objectives. Justify a management strategy that allows increasing the consumer value of an intellectual software product through the evolution of its software architecture and effective use of operational data in conditions of high uncertainty. Methods. The methodological basis of the work is the case study strategy. Results. Using the example of implementing a video analytics system in an infrastructure company, it is shown that choosing specialized solutions focused on data accumulation leads to a reduction in the share of critical errors. Conclusions. At the stage of testing an intelligent system ('reconnaissance'), it should be considered not as a defect, but as a tool that allows reducing technological uncertainty. This approach allows transforming operational risks into a manageable process of accumulating strategic assets, which is critically important for the sustainable scaling of digital products.
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