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 (Stanford 12/88 distribution), 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-003 · Partial · 28 May 2026
Pricing/model drift: a $100/mo Pro tier now sits beside the $200 tier (added 9 Apr 2026) and the premium model is GPT-5.5 Pro. Core thesis holds; the single-$200-tier framing no longer matches. Re-verify current tiers at chatgpt.com/pricing.
- AM-002 · Not holding · 06 May 2026
URL state changed. The /the-agentic-ai-revolution-real-world-success-stories-and-strategic-insights-from-2024-2025/ slug now serves a deliberately rewritten retrospective (claimId AM-130, "Agentic AI 2024-2025 retrospective", published 04 May 2026) against audited primary sources. The 28 Apr 2026 redirect to /retractions/ has been lifted to allow that. AM-002 the claim remains Not holding — the original $3.50/dollar + 70% failure-rate framing was withdrawn and is not restored. AM-130 is a separate claim with its own evidence chain. Readers arriving at /holding/AM-002 see the withdrawal here; the article link surfaces the new piece at the URL the original lived at, with this entry as the audit trail.
- AM-121 · Holding · 2 May 2026
Klarna walk-back primary-source upgrade — added Siemiatkowski verbatim quotes via Bloomberg-cited-by-Fortune (9 May 2025) and the Uber-style freelance hiring detail via Entrepreneur. Closes the highest-priority evidence gap from the source dossier.
Reviews coming up in Reporting
- AM-020 · Holding · next +18d (18 Jun 2026)
The 40-60% TCO underestimate on enterprise agentic-AI deployments is not a cost-visibility failure — it is a cross-depa…
- AM-023 · Holding · next +18d (18 Jun 2026)
The 10 Apr 2026 Google AI Mode rollout to eight markets is the first vertical (restaurant booking) where agentic search…
- AM-013 · Holding · next +18d (18 Jun 2026)
Q1 2026 is the quarter enterprise agentic-AI crossed three thresholds simultaneously — the first at-scale in-the-wild e…