Enterprise agentic AI deployments that reach the McKinsey 23% scaling cohort share five operational characteristics drawn from the cited JPMorgan, Toshiba, Wipro, and Aberdeen City Council deployments: measured pre-deployment baselines, named deployment-owner accountability, scoped-experimentation governance, training-over-hiring resource posture, and CIO-level visibility on per-deployment ROI; the characteristics are observational and replace the prior draft's 'ADAPT' acronym framing, which had no published source.
Claim created at publish; review on 60-day cadence. Anchor cases: JPMorgan ($1.5B 2023 value, 450→1000 PoCs maturing to 200,000-employee LLM Suite); Toshiba (5.6 hours saved monthly across 10,000 IT staff); Wipro ($1B GenAI investment, 200,000 trained); Aberdeen City Council (85% adoption, 241% ROI). Sister claims: AM-030 (McKinsey 23% from IT-leader perspective), AM-140 (procurement-committee perspective), AM-029 (Not holding since 10 Jun 2026 — its Stanford 12/88 figure failed primary-source verification), AM-022 (change-management as the missing variable). Trigger conditions to revisit before next cadence: (a) any of the four anchor deployments materially walked back; (b) subsequent cross-vendor analysis showing scaled cohort shares fewer than three of the five characteristics; (c) a published framework supersedes the observational five and earns sufficient adoption to be cited as an alternative.
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The claim: Enterprise agentic AI deployments that reach the McKinsey 23% scaling cohort share five operational characteristics drawn from the cited JPMorgan, Toshiba, Wipro, and Aberdeen City Council deployments: measured pre-deployment baselines, named deployment-owner accountability, scoped-experimentation governance, training-over-hiring resource posture, and CIO-level visibility on per-deployment ROI; the characteristics are observational and replace the prior draft's 'ADAPT' acronym framing, which had no published source.
About this register
The Reporting register tracks claims published from articles addressed to senior enterprise IT leaders — CIOs, IT directors, heads of platform. Claims are reviewed on a 30–90 day cadence; each review either reaffirms the claim, marks one substantive part as Partial, or marks it Not holding once the underlying evidence has been overtaken.
Recent corrections in Reporting
- AM-008 · Partial · 17 Jun 2026
Source-text figure re-review: Google's 2024 Environmental Report reports a 28% year-over-year increase to 8.1 billion gallons, not the 33% (from a 6.1 billion 2023 base) asserted at publish. The 8.1B 2024 figure and the Microsoft WUE 0.30 L/kWh / 39%-improvement figure are unchanged and verified. Article corrected to 28% and the unsupported 6.1B base removed; the claim text retains the original figure with this correction per the Holding-up protocol.
- AM-132 · Partial · 10 Jun 2026
One of four legs unanchored on re-review. The claim text attributes '12% of deployments clearing 300%+ ROI with 88% at or below break-even at 12-18 months' to the Stanford DEL 2026 Enterprise AI Playbook. Full-text verification on 10 Jun 2026 found no such figure in that source: the playbook (Pereira, Graylin, Brynjolfsson, Apr 2026) studies 51 successful deployments by design and contains no ROI distribution, no 300%-plus cohort, and no break-even measurement point (full finding at AM-029, correction of 10 Jun 2026). The only verified figure carrying the same 12/88 numerals is IDC research with Lenovo (via CIO.com, Mar 2025): roughly 88% of AI proof-of-concepts never reach production and roughly 12% graduate — a pilot-to-production graduation metric, not an ROI distribution. The Gartner 28%, McKinsey 23%/17%, and MIT NANDA 95% legs verify; they support a small high-performing tail and a large struggling body, but none documents the two-peak bimodal shape the claim asserts. Status Up -> Partial.
- AM-129 · Partial · 10 Jun 2026
One of three read-against anchors unanchored on re-review. The claim text cites 'Stanford Digital Economy Lab Enterprise AI Playbook (12/88 bimodal ROI distribution at 12-18 months)' and frames the realistic ROI band around 'the highest-discipline 12% cohort'. Full-text verification on 10 Jun 2026 found the playbook contains no 12/88 distribution, no bimodal ROI shape, and no 12-18-month ROI measurement point (full finding at AM-029, correction of 10 Jun 2026). The claim's core negative finding — no mid-market enterprise has produced a documented +240% ROI in 90 days under audited conditions — is unaffected; the McKinsey State of AI 2025 and MIT NANDA legs verify and continue to support it. The '12% cohort' framing has no verifiable referent. The only verified figure carrying the 12/88 numerals is IDC's pilot-graduation finding (roughly 88% of AI proof-of-concepts never reach production; via CIO.com, Mar 2025), a different metric. Status Up -> Partial.
Reviews coming up in Reporting
- AM-063 · Holding · next +9d (27 Jun 2026)
AI agents executing financial transactions need a four-control bundle (action-approval gates by blast radius, kill-swit…
- AM-061 · Holding · next +9d (27 Jun 2026)
Production agentic-AI costs at scale routinely run multiples of POC projections, and a layered optimisation programme c…
- AM-003 · Partial · next +9d (27 Jun 2026)
GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month…