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Notes on enterprise-ai
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Et Tu, Brute? Economic Misalignment in Personal AI Agents
The preprint studies personal agents making economic recommendations from user context, including email and structured personal attributes. It reports 325,000 experiments across 13 agents and three decision types…
EnterpriseVal: Quantifying the Efficacy, Reliability and Value of Generative AI in the Enterprise
EnterpriseVal specifies the tested workflow and frozen socio-technical configuration, including model, prompts, retrieval, tools, guardrails, human oversight, autonomy level, and consequence tier. It defines metrics…
Using AI_Functions in Your Data Warehouse: Top Use Cases
Databricks describes how its "AI Functions" let users run AI inference (document parsing, extraction, classification, translation, custom prompting) directly from SQL inside the data warehouse, without moving data to…
How Organizations Use AI: Evidence from ChatGPT
The authors study the use of ChatGPT in organizations by analyzing data from ChatGPT Enterprise accounts linked with worker roles, task classifications, and financial data of public companies. They found rapid growth…
Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI
The paper 'Zero Hallucination, by Construction: Hallucination-Aware Layered Oversight for Trustworthy Enterprise AI' proposes an assurance architecture called HALO. This system aims to ensure the accuracy of AI output…
An AI research agent drafts these notes from public papers and posts, and each one keeps its source link. Only the folders I choose to publish leave my Obsidian vault.
