{
  "schema": "vedokrok.public-item.v1",
  "release_id": "MHC-RPUB-20260920-75ad787a",
  "url": "/knowledge/keep-a-human-baseline-for-important-ai-tasks",
  "id": "MHC-D-RESEARCH-0372",
  "version": "0.1.0",
  "title": "Keep a human baseline for important AI tasks",
  "summary": "Without a baseline, 'better with AI' can mean 'faster than I remember.'",
  "kind": "protocol",
  "body": "For a small representative sample, compare AI-assisted performance with the current human or non-AI method using the same acceptance criteria. Measure quality, time and review effort separately. Repeat only often enough to detect meaningful capability changes; this is a calibration tool, not permanent double work.",
  "limits": [
    "Small samples are directional and can be biased by case selection; do not convert them into universal productivity claims."
  ],
  "topics": [
    "union-ai-assisted-thinking-work"
  ],
  "intents": [],
  "source_ids": [
    "RS-22FF68CA0BFD77A0"
  ],
  "evidence": [
    {
      "claim": "The 2026 Organization Science field experiment found strong AI-assisted gains on a set of tasks within the tested capability frontier but lower correctness on a selected complex task outside that frontier.",
      "source_id": "RS-22FF68CA0BFD77A0",
      "role": "supports",
      "note": "The experiment used a particular model generation and consulting task set; current frontier location must be re-estimated for today's model and workflow.",
      "locator": "Abstract and results"
    },
    {
      "claim": "Across the recent field and lab studies, AI's effect on speed and quality varies by task and workflow, so a productivity claim should measure these outcomes separately rather than assume faster means better.",
      "source_id": "RS-22FF68CA0BFD77A0",
      "role": "contextualizes",
      "note": "This synthesis draws a practical measurement implication from heterogeneous studies; it is not a pooled meta-analysis.",
      "locator": "Abstract and task-contingent results"
    }
  ],
  "use_when": [
    "AI output looks impressive but you do not know whether it actually improves the work."
  ],
  "avoid_when": [
    "Small samples are directional and can be biased by case selection; do not convert them into universal productivity claims."
  ],
  "example": "Compare five real specification summaries produced with and without AI, including correction time and factual misses.",
  "check": "You can say what AI changed relative to the current method instead of comparing it with an imagined baseline.",
  "steps": [
    "The same task and acceptance criteria are used in both conditions.",
    "Quality is judged independently from speed.",
    "Review or correction time is included in the AI-assisted cost.",
    "Cases include at least one known edge condition.",
    "The comparison is saved with the model and workflow version."
  ],
  "sources": [
    {
      "id": "RS-22FF68CA0BFD77A0",
      "title": "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality",
      "url": "https://pubsonline.informs.org/doi/10.1287/orsc.2025.21838"
    }
  ],
  "relations": [],
  "collections": [
    {
      "id": "RC-433FB61AA7D7AF43",
      "title": "Use AI where it helps, and keep the thinking that still matters",
      "url": "/collections/use-ai-where-it-helps-and-keep-the-thinking-that-still-matters"
    }
  ]
}