The abundance paradox: How to convert individual productivity into organizational gains
In 1987, the Nobel laureate economist Robert Solow famously observed , “You can see the computer age everywhere but in the productivity statistics.” Nearly four decades later, AI has produced a version of its own. Call it the abundance paradox. At the level of the individual worker, the evidence of AI-driven abundance is easy to see. Researchers found that AI typically raises productivity on…
In 1987, Nobel-winning economist Robert Solow noted the lack of productivity evidence in computer age statistics. Nearly four decades later, AI has generated a new kind of abundance paradox. AI boosts individual productivity by 15-30% in real-world tasks and 20-60% in controlled studies. However, only 37% of companies can link AI to earnings before interest and taxes (EBIT), with 89% reporting no impact on productivity over three years.
PwC's 2026 Global CEO Survey found that 56% of CEOs saw AI delivering neither higher revenue nor lower costs.
To address this gap, companies must develop abundance systems with three key components. First, they need to focus on what AI makes possible, moving from cost-cutting mindset to possibility mindset. Second, they should build reusable components, with software engineering practices emphasizing modularity so that assets can be easily repurposed across products and purposes.
Finally, organizations must orchestrate resources for the AI workflow, ensuring that the increased production leads to fewer reviews, tests, approvals, and fittings rather than more unfinished work.
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