Evaluation of Task Specific Productivity Improvements Using a Generative Artificial Intelligence Personal Assistant Tool
Year: 2024
Type: article
Study design: Clinical Trial
Abstract: This study evaluates the productivity improvements achieved using a generative artificial intelligence personal assistant tool (PAT) developed by Trane Technologies. The PAT, based on OpenAI’s GPT 3.5 model, was deployed on Microsoft Azure to ensure secure access and protection of intellectual property. To assess the tool’s productivity effectiveness, an experiment was conducted comparing the completion times and content quality of four common office tasks: writing an email, summarizing an article, creating instructions for a simple task, and preparing a presentation outline. Sixty-three (63) participants were randomly divided into a test group using the PAT and a control group performing the tasks manually. Results indicated significant productivity enhancements, particularly for tasks involving summarization and instruction creation, with improvements ranging from 3.3% to 69%. The study further analyzed factors such as the age of users, response word counts, and quality of responses, revealing that the PAT users generated more verbose and higher-quality content. Writing email content improved by 3.3%, summarizing text improved by 69%, creating instructions improved by 45.9%, and preparing an outline improved by 24.8%. An ’LLM-as-a-judge’ method employing GPT-4 was used to grade the quality of responses, which effectively distinguished between high and low-quality outputs. The findings underscore the potential of PATs in enhancing workplace productivity and highlight areas for further research and optimization.
Authors: Brian Freeman, Kendall Arriola, Dan Cottell, Emmett Lawlor, Matt Erdman, Trevor Sutherland, Brian Wells
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4405769520