A Co-Designed Decision Checklist for Organizational AI Adoption in Distributed Branding Architectures · Final Review · Score 20/30A Co-Designed Decision Checklist for Organizational AI Adoption in Distributed Branding Architectures · Final Review · Score 20/30A Co-Designed Decision Checklist for Organizational AI Adoption in Distributed Branding Architectures · Final Review · Score 20/30A Co-Designed Decision Checklist for Organizational AI Adoption in Distributed Branding Architectures · Final Review · Score 20/30A Co-Designed Decision Checklist for Organizational AI Adoption in Distributed Branding Architectures · Final Review · Score 20/30A Co-Designed Decision Checklist for Organizational AI Adoption in Distributed Branding Architectures · Final Review · Score 20/30
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The four-theme structure is clean and balanced (32 items, 8 per theme), and the importance-weighted 1-5 scoring with Critical/High/Medium tiers plus six Fatal-Flaw vetoes is a coherent, defensible design.
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The three V1-to-V2 changes are well-motivated and fully traceable: MIT-6 (veto extended to technical/manufacturing validation), RED-3 (training elevated to Critical onboarding), and VAL-3 (client expectation/deadline calibration) each map to a stated stakeholder critique in Table 2.
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The de-skilling argument is genuinely thoughtful: the framing of preliminary sketching as an informal pedagogical incubator, and the decision to protect it structurally, is the paper's strongest conceptual contribution.
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The domain choice (distributed B2B brand identity studio with text-to-image pipelines) is concrete and deliberately differentiated from the packaging use case, with realistic operational detail (22h to 3h compression, reallocation to print-spec/typography work).
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Both full checklist artefacts (V1 and V2) are reproduced with verdict bands and the Fatal-Flaw rule, and the AI-use disclosure is candid and specific.
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The V2 divisor is wrong: with 14 Critical, 10 High, 8 Medium the maximum weighted score is 70 + 25 + 10 = 105, not 101.0; dividing by 101 means a perfect score would exceed 100%, so the "clean 100% cap" claim is false and the 86.63% headline is miscomputed (87.5/105 = 83.3%).
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The stated tier counts do not match the actual item labels: counting the importance tags in the V1 checklist yields 15 Critical / 10 High / 7 Medium, not the stated 13/11/8 on which the 102.5 divisor is built; after elevating RED-3 the V2 labels give 16/9/7, not the tabulated 14/10/8 — so the scoring spine rests on counts the checklist itself contradicts.
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The co-design is a single asynchronous Google-Forms round with N=6 and one author; there is no second iteration, no synchronous probing, and no saturation argument, so the evidence base for "calibration" is thin.
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Validation is fully simulated: the "internal committee" audit and all ratings are author-constructed for a hypothetical firm, so the Adopt verdict has no empirical or predictive standing.
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There is a tension between the narrative and the gating logic: de-skilling is framed as the central risk and RED-3 raised to Critical, yet RED-3 is not a Fatal Flaw, so a poor training score (RED-3=2) still yields Adopt — the thing claimed most important does not gate the verdict.
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The reference list is unreliable: several entries appear inaccurate or mangled (e.g., "O'Brien and Clark 2022, JAI Ethics" and "Nkanta 2024, Socio-Technical Review" do not match the corresponding real sources), and the claim that async forms ensure "highly objective" feedback is overstated.