Abstract
Significant administrative overhead is often encountered in operational environments due to fragmented, manual processes for tracking diverse work types and managing project lifecycles. Reliance on individual effort across disparate applications leads to inconsistent execution and limited visibility into workflows. To address these limitations, a method is disclosed for context-aware workflow automation utilizing generative artificial intelligence. Techniques are grounded in a curated organizational knowledge base containing standard operating procedures and best practices. Digital interactions are analyzed to derive project context, triggering automated cross-application actions such as drafting project artifacts, identifying stakeholders, and populating tracking tools. Administrative burdens are reduced by automating project initiation and monitoring. Operational efficiency is enhanced through standardized resource allocation and proactive risk identification, allowing a focus on strategic deliverables rather than manual tracking.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Armstrong, Katherine Brown; Woods, Kevin; and Flores, Magdalena, "Automated Project Initiation and Management Using Generative AI and Organizational Knowledge Retrieval", Technical Disclosure Commons, (February 02, 2026)
https://www.tdcommons.org/dpubs_series/9262