Reusing prior work is an adaptive strategy that allows an organization to learn from missteps and extend its most promising insights and capabilities. Reuse grows as a challenge as the artifacts of effort accumulate. For example, stove-piped or hidden data streams make keeping abreast of internal reports and reviews difficult for engineers. The challenges are not merely in the variety and volume of discoverable artifacts but in their provenance, trustworthiness, and value. For example, the age, recency, frequency of use, and the individuals who touched an artifact are clues that often privilege the use of one artifact over another. They may also guide an interested user to a source of current knowledge about the artifact. Yet, such information is typically unavailable. To solve these challenges, we propose a digital assistant that will provide recommendations without adding cognitive load: Document Recommender With Adaptively Tailored Sensing of Needs (DR WATSON). Approved for Public Release | 22-MDA-11339 (13 Dec 22)
