{
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  "publisher": "WisdomTwin, Inc.",
  "brand": "WisdomTwin.ai",
  "brandDescription": "WisdomTwin gives regulated enterprises AI-native decision speed through governed AI Judgment Twins.",
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    {
      "id": "WT-100-001",
      "version": "2.0",
      "type": "Working paper / preprint",
      "peerReviewed": false,
      "title": "Judgment Latency in Regulated Enterprises: A Conceptual Framework and Research Agenda for Role-Specific AI Decision Support",
      "author": {
        "name": "Roman Bodnarchuk",
        "role": "Co-Founder and Chief Executive Officer",
        "affiliation": "WisdomTwin, Inc.",
        "location": "Toronto, Ontario, Canada",
        "email": "roman@wisdomtwin.ai",
        "orcid": "https://orcid.org/0009-0004-3113-2118"
      },
      "datePublished": "2026-09-03",
      "language": "en",
      "license": {
        "name": "Creative Commons Attribution 4.0 International (CC BY 4.0)",
        "url": "https://creativecommons.org/licenses/by/4.0/"
      },
      "doi": null,
      "doiStatus": "Pending repository deposit. No DOI has been issued.",
      "abstract": "Organizations can possess the information required for a decision while still waiting for a particular role to attend to it. This conceptual working paper defines judgment latency as the elapsed time between decision readiness and attention by the person or forum whose judgment is required. It proposes the AI Judgment Twin as a role-specific, evidence-grounded decision-support architecture, distinguishes the concept from personal simulation, and derives governance requirements for provenance, authorization, safe decline, human confirmation, monitoring, and contestability. The paper integrates research on work interruption, meetings, decision rights, organizational memory, digital and human twins, and human reliance on automation. It reproduces the assumptions behind McKinsey's 2019 estimate of about 530,000 manager-days and USD 250 million in annual wages, labels the “about one day per week” restatement as author-derived arithmetic, introduces a non-overlapping model of potentially recovered time, and proposes six falsifiable propositions and a staged empirical evaluation protocol. The proposed outcomes require empirical validation.",
      "keywords": [
        "judgment latency",
        "AI-assisted decision making",
        "organizational memory",
        "decision rights",
        "regulated enterprises",
        "human oversight",
        "provenance",
        "asynchronous management",
        "model risk",
        "human-computer interaction"
      ],
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      "suggestedCitation": "Bodnarchuk, R. (2026). Judgment latency in regulated enterprises: A conceptual framework and research agenda for role-specific AI decision support (WisdomTwin Working Paper WT-100-001, Version 2.0). WisdomTwin, Inc.",
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        {
          "version": "1.0",
          "date": "2026-09-03",
          "note": "Initial pre-publication draft; references flagged as unverified."
        },
        {
          "version": "2.0",
          "date": "2026-09-03",
          "note": "Fact-checked conceptual revision. Source caveats corrected, an unverifiable draft citation removed, McKinsey arithmetic reproduced and relabeled as author-derived, architecture claims converted to design requirements, propositions and evaluation protocol added, legal scope narrowed."
        }
      ],
      "disclosures": [
        "This is a conceptual working paper and preprint, not an empirical study and not peer reviewed.",
        "The proposed outcomes require empirical validation.",
        "The demonstrations referenced are founder-built product demonstrations.",
        "Roman Bodnarchuk is Co-Founder and CEO of WisdomTwin, Inc., is involved in developing the proposed architecture, and has a financial interest in its adoption.",
        "Generative AI assisted with source discovery, fact-checking, drafting, editing, and document production. The named author is responsible for the final text.",
        "No new empirical dataset was collected or analyzed.",
        "No external research funding was reported for preparation of the working paper."
      ],
      "notes": "The PDF is the version of record. Claim status and validation boundaries are stated in the paper."
    },
    {
      "id": "WT-WP-001",
      "version": "1.0",
      "type": "Technical whitepaper",
      "peerReviewed": false,
      "title": "Sovereign Judgment Twins: A reference architecture for governed enterprise decision support",
      "author": {
        "name": "Roman Bodnarchuk",
        "role": "Co-Founder and CEO",
        "affiliation": "WisdomTwin, Inc.",
        "email": "roman@wisdomtwin.ai",
        "orcid": "https://orcid.org/0009-0004-3113-2118"
      },
      "datePublished": "2026-09-25",
      "language": "en",
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        "name": "Creative Commons Attribution 4.0 International (CC BY 4.0), original text and figures",
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      },
      "doi": null,
      "doiStatus": "No DOI assigned.",
      "status": "Technical whitepaper. Proposed architecture. Not peer reviewed. Product outcomes require empirical validation.",
      "abstract": "Enterprise AI can retrieve information and execute work without establishing who is authorized to decide, which evidence remains valid, or how an exception should be reviewed. This whitepaper asks what additional architecture is needed to make expert decision context available while preserving organizational control. It defines a Sovereign Judgment Twin as a proposed decision-support system that combines permission-aware evidence retrieval, explicit expert-informed frameworks, enforceable authority boundaries, human disposition, and a versioned decision record. The contribution is an implementation-neutral reference architecture, a minimum decision-record schema, a sample risk-exception walkthrough, and a comparative evaluation design. The method is a targeted synthesis of primary technical sources followed by design analysis; it is not a systematic review or an empirical study. The paper treats sovereignty as a set of verifiable controls across processing, access, retention, operations, and exit. It makes no claim that a twin reproduces a person's mind or that agentic systems cannot implement the same controls. The proposed value is conditional: reduce avoidable decision delay while maintaining decision quality and accounting for review, rework, and operating effort. ChatGPT assisted with research, drafting, and document production.",
      "keywords": [
        "enterprise AI",
        "decision support",
        "AI governance",
        "institutional memory",
        "data sovereignty",
        "agentic workflows",
        "provenance",
        "human oversight",
        "judgment latency"
      ],
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      "suggestedCitation": "Bodnarchuk, R. (2026). Sovereign Judgment Twins: A reference architecture for governed enterprise decision support (WisdomTwin Technical Whitepaper WT-WP-001, Version 1.0). WisdomTwin, Inc.",
      "disclosures": [
        "Proposed architecture; not peer reviewed; not a systematic review or empirical study.",
        "The architecture and proposed outcomes require empirical validation.",
        "The walkthrough is a sample case.",
        "ChatGPT assisted with research, drafting, and document production.",
        "Roman Bodnarchuk is Co-Founder and CEO of WisdomTwin, Inc. and has a financial interest in the proposed architecture."
      ],
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