AI makes work faster but not necessarily companies more productive

Artificial intelligence (AI) has the ability to streamline tasks and reduce the time required for various activities, but its impact on overall organizational productivity is not always clear. While AI can make individual tasks faster, it does not necessarily translate to increased overall efficiency for the company. A survey by PwC in January 2026 found that 56% of executives did not report any financial benefits from AI implementation.

The paradox lies in the fact that AI enhances task productivity while organizations generate value through interconnected systems of tasks. Making specific tasks faster does not inherently make the organization more effective. This situation is not unique to AI but echoes previous technological advancements, such as the case of computing in the 1980s, where the benefits were not immediately evident in productivity statistics.

There are three key areas where AI implementation may create a mismatch between technological advancements and organizational productivity:

1. Rethinking goals and business cases: Organizations need to clarify what they aim to achieve with AI. While efficiency gains are a common objective, they do not always equate to value creation. AI's widespread accessibility may lead to a reduction in differentiation as standardised activities become cheaper and less competitive.

The true value of AI lies in extending scarce expertise, evaluating more opportunities, and providing services that were previously unprofitable. In the UAE, for instance, AI job postings have tripled since 2021, and corresponding wages have increased by 92%.

2. Redesigning workflows: AI implementation often begins by breaking down tasks and identifying which ones can be handled by machines. However, this decomposition can lead to a fragile workflow, as some human elements are crucial for verifying AI outputs and maintaining accountability. It is essential to determine not only if AI can perform a task but also who will be responsible for verifying its results and ensuring the overall workflow remains robust.

3. Reevaluating the role of human workers: Traditionally, organizations have relied on comparative advantage, assigning tasks to AI where it outperforms humans and leaving the remaining work to human employees. This approach risks relegating human roles to a shrinking residual. A more effective model is to view humans as responsible for larger units of output, with AI agents handling specific components.

Two critical human capabilities become prominent in this context: problem formulation (deciding which problems to solve) and solution validation (assessing the quality of AI-generated answers). Human workers must also contribute to the judgment and accountability aspects of the work.

The AI productivity paradox is fundamentally an organizational design issue. To maximize the benefits of AI, organizations must focus on redesigning their systems at three interconnected levels: defining their objectives, structuring workflows that combine human and AI contributions, and determining who remains accountable for the outcomes.

As AI becomes more capable, organizations need to pay greater attention to their design, ensuring that the integration of AI leads to increased productivity and value creation rather than a simple division of labor between humans and machines.