The adoption of artificial intelligence (AI) in industries is seen as a game-changer for global productivity. However, the latest data shows a curious reality: instead of immediate gains, many companies experience a temporary drop in productivity soon after implementing AI-based solutions. Why does this happen?
The phenomenon is known to economists as the productivity paradox. Although AI promises to automate tasks, reduce errors and optimize processes, the adoption cycle involves a learning curve. Teams need to be trained, processes are reorganized and workflows undergo major restructuring. This adaptation period generates friction that, in many cases, negatively impacts productivity in the first few months — or even years.
In addition, there is a cultural factor: resistance to change. Workers and managers, accustomed to traditional methods, may not immediately trust AI, or may be afraid of losing their jobs. This reduces engagement, affects performance and requires careful transition management.
On the other hand, success stories are revealing. Companies that invested not only in technology, but also in people training and in complete review of its operating models, reaped the rewards in the medium and long term. In these cases, the gains far outweighed the initial decline.
AI, therefore, is not a silver bullet. It requires strategic vision, patience, and leadership that understands that the digital revolution is, first and foremost, a transformation of culture and structure. Productivity, in this scenario, is an outcome—not a starting point.
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