Generative AI finds a likely answer to the riddle of the Productivity Paradox

Generative AI finds a likely answer to the riddle of the Productivity Paradox

Study shows that GPTs can affect entire economies on a national or global level, and even drastically alter societies through strong economic and social impact

  • JP Morgan Chase utilized a generative AI system to analyse legal documents, reducing the time needed for legal reviews. The system was able to analyse 150,000 documents in a few seconds, compared to several lawyers who would have taken over 360,000 hours.
  • Autodesk used generative AI to optimize the design of a drone, leading to a 50% reduction in weight and a 20% increase in flight time. This gave Autodesk a competitive advantage in the drone market.
  • Zara employed generative AI to analyse customer data and optimize their inventory management, resulting in increased sales and reduced costs. The company managed to decrease the amount of unsold inventory by 10%, leading to savings of $1 billion.


A series of research papers revealed that General-purpose technologies (GPTs) can affect entire economies, usually on a national or global level. GPTs have strongimpact on pre-existing economic and social structures; hence, they candrastically alter societies. Examples include the steam engine, electricity, information technology, and artificial intelligence.

In April 2023, researchers studied the effect of a staggered introduction of a generative AI-based conversational assistant on 5,179 customer support agents. Access to the tool increased productivity by 14% on average, as measured by issues resolved per hour. The greatest impact was on novice and low-skilled workers, with minimal impact on experienced and highly skilled workers.

Copilot coded 55% faster

Another study found that developers who used GitHub Copilot, coded up to 55% faster than those who did not. Additionally, productivity gains extended beyond speed – with 74% of developers reporting less frustration and the ability to concentrate on more fulfilling work.Can this bean answer to the Productivity Paradoxriddle that has baffled the corporate world for decades?

The Productivity Paradox refers to the observation that despite widespread adoption of information technology (IT) in business operations, there has been no corresponding increase in productivity. Economists and researchers have been puzzled by this phenomenon for decades, as they expected that increased IT investment would lead to increased productivity. However, the paradox seems to be ending with the advent of Generative Artificial Intelligence (GAI).

GPTs to impact every task

An investigation into how large language models (LLMs) like Generative Pre-trained Transformers (GPTs) can potentially affect the US labour market revealed that nearly 80% of the workforce could have at least 10% of their tasks affected by LLMs. More alarmingly, 19% of workers may see at least 50% of their tasks impacted. The projected effects span all wage levels, with higher-income jobs potentially facing greater exposure to LLM capabilities and LLM-powered software.

Solving the Productivity Paradox

The Productivity Paradox has been a longstanding challenge for businesses, but Generative Artificial Intelligence has the potential to address some of the underlying issues. By enabling more efficient and effective decision-making and automating repetitive tasks, GAI can free up employees to focus on more valuable work. However, it is important to address the potential challenges associated with the technology, such as bias and job displacement, to ensure that the benefits of GAI are shared widely and fairly.

Professional degree holders more at risk

Researchers found that individuals with Bachelor’s, Master’s, and professional degrees are more exposed to LLMs and LLM-powered software than those without formal educational credentials. Interestingly, individuals with some college education but no degree also exhibit a high level of exposure to LLMs and LLM-powered software. Jobs with the least exposure require the most training, potentially offering a lower payoff once competency is achieved. Conversely, jobs requiring no on-the-job training or only an internship were more exposed to LLMs.

The study on the impact of generative AI on productivity in the customer service sector, an industry with one of the highest rates of AI adoption, concluded with four sets of findings.

  • First, AI assistance increased worker productivity, resulting in a 13.8% increase in thenumber of chats that an agent is able to successfully resolve per hour. This increase reflected shifts inthree components of productivity:
  1. a decline in the time it takes to an agent to handle an individualchat,
  2. an increase in the number of chats that an agent is able to handle per hour (agents may handlemultiple calls at once), and
  3. a small increase in the share of chats that are successfully resolved.
  • Second, AI assistance disproportionately increased the performance of less-skilled and less-experienced workers across all productivity measures we consider. In addition, the AI toolhelped newer agents move more quickly down the experience curve: treated agents with two monthsof tenure perform just as well as untreated agents with over six months of tenure.
  • The third set of results investigated the mechanism underlying the findings so far. It found thathigh-skill workers may not have much to gain from AI assistance. This is because AI recommendationscapture the potentially tacit knowledge already embodied in their own behaviours–so they have nothing to improve upon. In contrast, low-skill workersare more likely to improve by incorporating these behavioursas suggested by AI. Consistent with this, the research found no significant positive effects of AI access for the highest-skilled or most-experiencedworkers. Interestingly, textual analysissuggested that AI assistance can enable lower-skill agents to communicate more like high-skill agents.

AI & Humans coworking have greater impact

Finally, the research shows that the introduction of AI systems can impact the experience and organizationof work. AI assistance markedly improves how customers treat agents, as measuredby the sentiments of their chat messages. This change may be associated with other organizationalchanges: turnover decreases, particularly for newer workers, and customers are less likely to escalatea call by asking to speak to an agent’s supervisor. Overall findings demonstrate that generative AI working alongside humans can have a significant positive impact on the productivity and retention of individual workers.

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