## 5 Discussion and Conclusions  
Despite the rapid pace of progress in ML, there are concerns about the disconnect between innovation and adoption [9] and between investments and value (e.g., [76; 77]). A growing body of work reflecting on the state of ML notes the overemphasis of technology over transformation (e.g., [78; 79]) and worker substitution over augmentation [15]. Consequently, there is neglect in studying the unique ways by which humans and ML can jointly unlock significantly more value (e.g., [14; 22; 80]). We contribute to the conversation by adopting the view that ML is a technological asset that combines in an organizational-specific manner with other assets, chiefly personnel, to generate value. In the following paragraphs, we discuss the primary insights from the simulations performed using our proposed quantitative model that is suitably subjective (and holistic) in its conceptualization of the value-generation process. We also note the implications of these insights and how they relate to other works of a more conceptual/abstract nature in this area. Our simulations have highlighted that, although procedurally rational, local process improvements (measured via _process metrics_) do not automatically translate to commensurate overall benefits (measured via _outcome metrics_). The system dynamics approach provides an elegant way to confirm the rationale of the improvement (in this case, the ML intervention) through partial model tests before proceeding to whole-model simulations to check for unintended global consequences. Although, as noted earlier, the idea of partial-model tests is not new [56], employing the idea when the improvement comes from ML is novel. It assumes greater significance in light of recent work [17] on organizational learning (using an abstract agent-based modeling approach) in a human-ML collaborative context that shows that ML strongly influences the classic explore-exploit trade-off [81]. Specifically, since ML agents do not subscribe to preexisting organizational mental models, they tend to facilitate nimbler exploration of the performance landscape. However, this also amplifies the type of risk our experiments illustrate (entering an organization into operating regimes with an increased likelihood of untested decision routines or operating characteristics that might produce dysfunctional global outcomes). The new dynamics caused by the introduction of ML forecasting in our experiment underscores this point. Before ML, the incumbent heuristic method was slow to react to peaks and troughs in actual sales and returns (because of the inertia inherent in the anchor-and-adjust heuristic, current perceptions change only slowly). More pertinent to the earlier point regarding exploration, ML predictions (from the forecasting process) that are closer to actual values readily expose the inadequacy of the adjacent capacity management process. For instance, some high values that ML predicts are more than the standard available capacity, and the rules for using additional discretionary capacity suffer from latency, leading to poor order fulfillment performance. The example confirms the folk wisdom in manufacturing, supported by rigorous research, that saving time at a non-bottleneck resource is a mirage [82]. Translated to the experiment, the forecast improvement beyond a point collides with the capacity bottleneck, which limits the performance (unless addressed). This example reinforces the point about broadening the scope of analysis--organizational decision-making involves complex feedback loops that make it unrealistic to anticipate high-level outcomes accurately. In addition, the approach taken in the experiment to alleviate the problem demonstrates the importance of complementarity between decision pairs. Concretely, the improvement took the form of reducing the delay in using the discretionary capacity.