{
  "slug": "how-much-accepted-work-are-we-producing-per-unit-of-human-attention",
  "title": "How Much Accepted Work Are We Producing per Unit of Human Attention?",
  "date": "2026-09-22",
  "featuredClaim": "Measure AI value as accepted jobs per unit of human attention, not drafts produced.",
  "description": "This article proposes measuring AI productivity by the ratio of accepted work to total human hours spent on all attempted jobs, including review and correction time. It offers six practical controls for improving this ratio without lowering quality standards, such as agreeing on acceptance criteria upfront and matching review depth to consequences. The author provides a step-by-step approach for organizations to pilot the metric on a single workflow.",
  "keyPoints": [
    "Measure AI value as accepted jobs divided by total human hours spent on all attempted jobs, keeping time spent on rejected work in the denominator",
    "Define 'accepted work' with the recipient before generating, using three to five concrete acceptance checks",
    "Match review intensity to the consequence of the work, from private brainstorming to contractual commitments",
    "Track first-pass acceptance, reasons for returns, and post-acceptance errors, and fix recurring sources of rework"
  ],
  "topics": [
    {
      "id": "measurement",
      "slug": "measuring-ai-roi",
      "label": "ROI & Measurement",
      "description": "Measuring AI impact and return on investment"
    },
    {
      "id": "implementation",
      "slug": "ai-implementation",
      "label": "Implementation",
      "description": "Hands-on implementation techniques and frameworks"
    },
    {
      "id": "business",
      "slug": "ai-business-applications",
      "label": "Business Applications",
      "description": "Real-world business use cases and applications"
    }
  ],
  "claims": [
    "Measure AI value as accepted jobs divided by total human hours spent on all attempted jobs.",
    "A 2025 survey of 1,150 U.S. desk workers found 40% received workslop in the previous month.",
    "Workslop recipients estimated about two hours to resolve each incident, based on self-reported experiences.",
    "A randomized experiment with 758 BCG consultants found GPT-4 improved performance within its capabilities.",
    "Keep time spent on rejected work in the denominator so dashboards do not hide failures."
  ],
  "claimTitles": [
    "Measure Accepted Work per Hour",
    "40% Received Workslop",
    "Two Hours per Workslop Incident",
    "BCG Consultants GPT-4 Experiment",
    "Keep Rejected Work Counted"
  ],
  "originalUrl": "https://aiadopters.club/p/how-much-accepted-work-are-we-producing",
  "claimProvenance": [
    "author-interpretation",
    "source-summary",
    "source-summary",
    "source-summary",
    "author-interpretation"
  ],
  "primarySources": [
    {
      "title": "BetterUp Labs and Stanford Social Media Lab survey",
      "url": "https://www.betterup.com/workslop",
      "publisher": "betterup.com",
      "claimIndices": [
        2,
        3
      ]
    },
    {
      "title": "randomized experiment with 758 BCG consultants using GPT-4",
      "url": "https://www.hbs.edu/ris/Publication%20Files/dell-acqua-et-al-2026-navigating-the-jagged-technological-frontier_5c589c8c-fbb5-458f-b285-c944746cd717.pdf",
      "publisher": "hbs.edu",
      "claimIndices": [
        4
      ]
    }
  ],
  "quote": "How much accepted work are we producing per unit of human attention?",
  "keyStatistics": [
    {
      "stat": "40%",
      "context": "Share of 1,150 full-time U.S. desk workers in a September 2025 BetterUp Labs and Stanford Social Media Lab survey who reported receiving workslop in the previous month."
    },
    {
      "stat": "~2 hours",
      "context": "Time recipients estimated to resolve each workslop incident; self-reported, not measured company-wide productivity losses."
    },
    {
      "stat": "758 consultants",
      "context": "Number of BCG consultants in a randomized GPT-4 experiment first reported in 2023, which found better performance within the model's capabilities and worse correctness outside them."
    },
    {
      "stat": "0.8 vs 1.0 accepted proposals per hour",
      "context": "Illustrative comparison: eight accepted proposals in ten human hours versus nine accepted in nine hours, with scope and quality standard held constant."
    }
  ],
  "supportingContext": "The article proposes a practitioner-oriented productivity metric: accepted jobs divided by total human hours across all attempted jobs, including preparation, checking, correction, and the recipient's review time. The author grounds the approach in external evidence, citing the BetterUp/Stanford workslop survey and the BCG GPT-4 experiment to show why first-draft speed alone misleads. Six operational controls translate the metric into practice, including agreeing acceptance checks with recipients upfront, matching review intensity to consequence, and logging reasons work gets returned. The recommended rollout compares ten consecutive jobs before and after the new rules, tracking first-pass acceptance, returns, and post-acceptance errors. The author explicitly frames these as practical proposals consistent with NIST's voluntary AI risk guidance, not a proven productivity intervention.",
  "canonicalUrl": "https://kbanc.com/claims-library/how-much-accepted-work-are-we-producing-per-unit-of-human-attention",
  "markdownUrl": "https://kbanc.com/md/claims-library/how-much-accepted-work-are-we-producing-per-unit-of-human-attention.md",
  "jsonUrl": "https://kbanc.com/api/claims/how-much-accepted-work-are-we-producing-per-unit-of-human-attention.json",
  "source": {
    "publisher": "AI Adopters Club",
    "title": "How Much Accepted Work Are We Producing per Unit of Human Attention?",
    "url": "https://aiadopters.club/p/how-much-accepted-work-are-we-producing"
  }
}