Fast forward to the mid-1990s. The deployment of advanced technologies like robotics, programmable logic controllers (PLCs), and manufacturing execution systems (MES) is reshaping how work gets done. There are fewer line workers on the floor handling a broader set of tasks, like clearing jams and loading robotic cells, while producing twice the output. Supervisors are now empowered with production data to coordinate across automated processes and cross-functional teams.
The value of these technologies didn’t trickle down to line workers. From the 1980s to the 2000s, new knowledge layers amplified supervisors, increasing the wage disparity between management and production staff. This is a common scenario across industries as a result of historical technological disruptions.
AI could flip the script because it is the first technology that is accessible through natural language rather than specialized technical skills. Past technological disruptions required workers to learn software, programming, or analytical tools to benefit from new systems. This created a push factor that required businesses to lead adoption and integration. AI lowers the entry barrier with tools that extend and amplify human capability. The interface is a conversation rather than code.
Over 80% of AI projects fail to deliver business value, with 84% of those failures tied to leadership gaps, like unclear metrics, underinvestment, and unfocused sponsorship, according to research from Pertama Partners. Deploying generic machine intelligence inside legacy structures, without accounting for the real, often messy ways teams actually work, is a recipe for failure.
This is why we predict that intelligence will no longer concentrate in a narrow band of supervisory or white-collar roles. It will diffuse across every layer and role of an organization, reshaping organizational hierarchies and previously exaggerated socio-economic disparities.
By amplifying their skills and experience with AI, workers can create hybrid intelligence to produce higher-value output. An HVAC technician can detect early compressor failures for proactive maintenance through AI-driven diagnostics. A bank teller can leverage AI-augmented compliance checks and personalized recommendations for customers. A customer service representative can use AI to gauge customer sentiment in real time, enabling more empathetic responses and faster conflict resolution.
Current reports warn that AI will replace workers, eliminate entry-level roles, and accelerate jobless growth across industries. But if AI increases worker productivity, there is potential for the technology to rebalance the labor market, give workers more leverage, and improve the circumstances for workers.
AI has the potential to create new models and systems that expand equitable socioeconomic opportunity. Historically, rising output often concentrated value in the hands of a few. But that outcome is not inevitable.
AI is advancing at a pace rarely seen in previous technological revolutions. But the most important innovation of the coming decade may not come from artificial intelligence. It will come from empowering every worker to use it to generate economic and societal value.
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