Abstract
This disclosure describes techniques to measure the net productivity impact of AI-assisted workflows on user productivity. Metadata regarding user activity on a user device is obtained with user permission, using a private, on-device architecture. The data is analyzed on-device to determine whether an AI-assisted workflow is in process and the impact of the use of AI on productivity. In an example implementation, an Activity Sensing and Filtration Layer (ASFL) and a Cognitive Workflow Engine (CWE) are implemented and used to calculate the true net time saved using AI assistance. This metric takes into account the hidden cost of the user making corrections to AI output, referred to as the AI correction cost (ACC). The net cost is determined by subtracting observed time and ACC from the predicted unassisted time for task completion without the use of AI tools. The techniques described herein enable measurement of AI tool value for organizations and individual users.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Kagalwalla, Navid; Piacentini, Tom; and Mouton, Jacques, "On-device Measurement of Impact of Artificial Intelligence Tools on Productivity", Technical Disclosure Commons, ()
https://www.tdcommons.org/dpubs_series/8991