The COVID-19 pandemic exposed significant gaps in the alliance between theoretical models and their practical applications across various sectors, presenting what this paper terms as the “Pandemic Paradox”. Frequently, process of implementation affects model performance. Some scholars commented in the post-pandemic intellectual discourse that biases can creep into the process when the method tends to popularise a specific type of statistical/optimisation /programming/networking technique at the inopportune time. There were post-pandemic scholars who had resonated more profoundly with the idea and commented “Politicalised Implementation”. Unfortunately, opaque and heuristic-based practices occasionally resulted in negative outcomes, such as damaging reputations, lowering credit ratings and impacting future funding opportunities. While these discrepancies exacerbated inefficiencies during the pandemic, they also offered critical lessons on the limitations of intelligent automation (e.g., artificial intelligence (AI), robotics) in dealing with complex, unpredictable nature of real-world crises. Occasionally, rigorous criticism of methodologies adopted and unpremeditated remarks multiply anxiety and have long term negative impacts. This study investigates whether criticism by business and political elites in the post-pandemic discourse has been foreboding or offers valuable lessons for future resilience and adaptation.