AI Speeds Tasks, Productivity Gains Remain Uncertain

The integration of generative artificial intelligence into corporate workflows has sparked a nuanced debate about productivity gains, as highlighted by recent discussions among industry leaders. Ramine Tinati, lead of Accenture’s APAC Center for Advanced AI, observed at the Fortune Brainstorm AI Singapore conference that AI tools may enable faster task completion but do not inherently increase productivity. “If you give employees a tool to do things faster, they do it faster. But are they more productive? Probably not, because they do it faster and then go for coffee breaks,” Tinati cautioned, emphasizing the need for reimagining work processes rather than simply automating existing tasks [1]. This sentiment reflects a growing concern that AI-driven efficiency might not translate into meaningful output, particularly if workers redirect saved time toward non-work activities [1].

The discussion builds on real-world applications of AI in sectors like manufacturing and public safety. May Yap of Jabil described how AI augmented the “Golden Eye” team of human inspectors, reducing errors in phone quality checks. Similarly, Chee Wee Ang of Singapore’s Home Team Science and Tech Agency noted a 200% improvement in information extraction processes, enhancing ROI and enabling new capabilities such as addressing emerging security threats [1]. However, these examples underscore a critical distinction: AI’s ability to accelerate tasks is not automatically linked to productivity unless organizational structures evolve to absorb time savings into higher-value activities.

A parallel analysis in a recent report highlights behavioral shifts in the workplace, where AI’s efficiency might inadvertently normalize reduced effort. The study suggests that employees using AI to automate repetitive tasks often finish core duties faster but do not necessarily pursue additional responsibilities. This dynamic, described as “working to the minimum viable effort,” risks misaligning corporate expectations with actual outcomes [1]. For instance, rigid performance metrics tied to output volume could mitigate the tendency to minimize labor, but without such structures, AI’s benefits may be offset by complacency [1].

The psychological implications further complicate AI adoption. Workers who view AI as a tool to lighten their workload may reinterpret their roles, prioritizing speed over creativity or quality. This shift could erode organizational culture and innovation over time, as employees focus on meeting baseline requirements rather than contributing to long-term goals [1]. Tinati, however, remains optimistic, arguing that AI can free workers to engage in supervisory or higher-order tasks, provided companies invest in reskilling programs. He emphasized that “skills are now being uplifted to do other things,” suggesting a transition from task automation to workforce augmentation [1].

Challenges in AI implementation persist, including resource constraints and skill gaps. Ang noted difficulties in finding local expertise for generative AI, leading his team to hire candidates with adjacent skill sets. Meanwhile, infrastructure limitations—such as the lack of GPUs for Singapore’s Home Team—highlight the need for tailored solutions that balance security with performance [1]. These hurdles reinforce the idea that AI is not a standalone solution but a component of a broader productivity ecosystem requiring strategic alignment.

The broader takeaway is that AI’s success hinges on organizational design. Companies must ensure that efficiency gains are redirected toward meaningful work rather than personal convenience. This requires redefining workflows, aligning incentives, and fostering a culture that values continuous learning. As Yap from Jabil stated, “General skills and good leadership traits cannot be taken away by AI,” underscoring the enduring role of human capabilities in an automated future [1]. The path forward, therefore, lies in strategic implementation that prioritizes human-AI collaboration over replacement, ensuring that AI becomes a catalyst for growth rather than a shortcut to reduced effort.

Sources: [1] [AI might make workers faster, but not necessarily more productive: ‘They do it faster, then go for coffee breaks’] [https://fortune.com/asia/2025/07/28/ai-might-make-workers-faster-not-more-productive/]