Increase  workplace efficiency and quality of work with proper use of AI

Artificial Intelligence (AI), and particularly Large Language Models (LLMs) like GPT-4, is transforming the way we work. A recent study conducted by Harvard Business School – in collaboration with Boston Consulting Group (BCG) – suggests that we can increase efficiency by 12.2% and quality of work by as much as 40% – if applied to relevant tasks. The study provides valuable insights, but do also raise relevant questions about the methodology and implications of the findings.

The study was conducted with 758 consultants from BCG, representing about 7% of the individual contributor-level consultants in BCG. The participants were assigned to one of two distinct experiments, each involving a unique type of task. The tasks were designed to be realistic and representative of day to day tasks for the consultants. The study aimed to understand how the integration of AI might reshape and change the traditional workflows.

KEY FINDINGS:

AI Boosts Productivity and Quality of Work: For tasks within the capabilities of AI, consultants using AI were significantly more productive, and produced higher quality of work. The use of AI led to an average increase of 12.2% in the number of tasks completed. It also increased the quality of the work and deliveries by more than 40% compared to a benchmark group of people.

AI's Impact Varies: The study introduces the concept of a "jagged technological frontier," where tasks that are easily done by AI gets labelled "inside the AI's frontier". Other tasks, seemingly similar in difficulty, yet outside the current capability of AI, gets labelled "outside the AI's frontier".

AI Can Decrease Performance: For tasks outside the AI's frontier, the use of AI led to a decreased performance. Consultants using AI for tasks outside the AI's frontier, were 19 percentage points less likely to produce correct solutions compared to those without AI.

Centaur and Cyborg Practices: The study identified two predominant models of successful AI use by humans – Centaur and Cyborg behaviors. Whereas Centaurs strategically delegate between human and AI tasks, Cyborgs intricately integrate their efforts with AI at the very frontier of capabilities.

Weaknesses of the Study:

Whereas we find the study very thorough and interesting, it´s also important to point out a few of the weaknesses we see with how it was conducted.

Limited sample: The study was conducted with a very specific group of the total workforce – consultants. Also, the participants all came from one spesific company. Hence, the sample is quite limited and may lack the needed variance, which in turn, may limit the generalizability of the findings.

Task Design: The tasks were designed to be representative of the consultants' daily activities. However, the complexity and diversity of real-world tasks may not have been fully captured.

AI's Frontier: The concept of a "jagged technological frontier" is interesting, but determining which tasks fall within or outside of AI's capabilities can be challenging and highly subjective.

Retainment Measure: The study used a measure of "retainment" to assess how much of AI's output was used by the consultants in their responses. However, this measure may not fully capture the quality of the interaction between the consultants and the AI.

Conclusion:

The study provides valuable insights into how AI may impact workplace efficienty and quality of work. However, it also highlights some of the complexities and challenges in integrating AI into professional workflows. As AI continues to evolve, further research is needed to fully understand its implications for productivity, quality, and the nature of work itself.

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