{
  "schema": "vedokrok.public-item.v1",
  "release_id": "MHC-RPUB-20260920-75ad787a",
  "url": "/knowledge/map-the-task-to-the-current-ai-frontier",
  "id": "MHC-D-RESEARCH-0370",
  "version": "0.1.0",
  "title": "Map the task to the current AI frontier",
  "summary": "AI capability is jagged: two tasks that feel equally hard to you may be very different for the model.",
  "kind": "heuristic",
  "body": "Break the workflow into concrete tasks and test AI on representative examples before assigning broad trust. Label where it consistently improves quality or speed, where it is mixed, and where it degrades the result. Keep high-supervision or human-first handling for the uncertain edge instead of declaring the whole job 'AI-ready.'",
  "limits": [
    "The frontier is model-, tool-, data- and time-dependent; today's map is not a permanent capability certificate."
  ],
  "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": "The jagged-frontier study argues that tasks of similar apparent difficulty to humans can fall on different sides of current AI capability and that the boundary is difficult for workers to know in advance.",
      "source_id": "RS-22FF68CA0BFD77A0",
      "role": "supports",
      "note": "The concept describes uneven task fit rather than offering a universal classifier for every model.",
      "locator": "Introduction and jagged-frontier discussion"
    }
  ],
  "use_when": [
    "You are deciding whether AI should draft, analyze, advise or execute a meaningful part of a knowledge-work task."
  ],
  "avoid_when": [
    "The frontier is model-, tool-, data- and time-dependent; today's map is not a permanent capability certificate."
  ],
  "example": "In SAP work, AI may draft a status update reliably while performing poorly on a system-specific root-cause judgment that depends on hidden configuration.",
  "check": "The workflow has task-level evidence of AI fit rather than one opinion about AI for the whole role.",
  "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": [
    {
      "from": "MHC-D-RESEARCH-0370",
      "to": "MHC-D-RESEARCH-0372",
      "type": "use_before",
      "url": "/knowledge/keep-a-human-baseline-for-important-ai-tasks"
    }
  ],
  "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"
    }
  ]
}