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R0042 — Private AI Motivations

Mode: Query · Status: Active · Tags: enterprise-ai, private-ai, sycophancy, behavioral-customization

Input

  1. What are the documented reasons why enterprises build private or on-premises AI systems rather than using third-party AI vendors? Look for industry surveys (McKinsey, Gartner, Deloitte, KPMG, Forrester) that rank enterprise motivations for private AI deployment. What is the full list of reasons and how are they prioritized?
  2. Among enterprises deploying private AI, is behavioral customization — including the ability to control or eliminate sycophancy, adjust response style, or enforce domain-specific interaction norms — a documented motivation? Or is the conversation limited to data sovereignty, security, and compliance?
  3. Has any enterprise or research institution documented building a private AI system where sycophancy reduction or elimination was an explicit design goal? Look for case studies, white papers, or conference presentations describing custom-trained models with anti-sycophancy objectives.

Runs

2026-03-28 — Initial run

Mode: Query · Queries: 3 · Prompt: Unified Research Methodology v1 · Model: Claude

First investigation of enterprise private AI motivations and sycophancy reduction as a deployment driver.

2026-04-01 — Rerun

Mode: Query · Queries: 3 · Prompt: Unified Research Methodology v1 · Model: Claude Opus 4.6 (1M context)

Independent rerun investigating the two-conversation gap between enterprise deployment motivations and AI safety/sycophancy research.