A Bain & Company article published in Business Insider points out that while companies are investing heavily in AI deployments, current results are often limited to faster execution of individual tasks such as writing, programming, and report generation. While these minor productivity gains are helpful, they fall far short of a transformation. What companies need is to build a sustainable productivity engine, enabling AI to drive overall organizational productivity growth.
The so-called "continuous productivity engine" involves AI performing tasks and extracting data insights, while humans make judgments, corrections, and experiments. Enterprises then incorporate the feedback into model optimization, business processes, and management systems to achieve continuous improvement.
In this process, the issue of "workflow debt" is particularly important. Redundant approvals, redundant handovers, and ambiguous permissions accumulated by enterprises over a long period can be further amplified by AI. Enterprises should first simplify processes, then clearly define the division of labor between humans and machines, and manage business workflows as a continuously iterative product: treating enterprise workflows as continuously updated products, recording anomalies, human interventions, efficiency bottlenecks, and execution results, focusing on the performance of the entire process, rather than simply counting the number of times AI was used or how many minutes were saved.
The core asset for future enterprise AI competition may shift from model capabilities to the continuously accumulated business execution data and feedback mechanisms. The value measurement of enterprise-level AI may gradually shift from the number of uses and time saved on individual tasks to the cost, quality, and operational results of the entire business process.
