The 87% vs 27% success-rate gap between Six-Sigma and non-Six-Sigma organisations on agentic-AI deployments reflects pre-existing measurement discipline, not the DMAIC methodology itself. Agents require a clean baseline, defect definition, documented root-cause analysis, and a change-management gate — four conditions that ISO 9001, ITIL, SRE, or HACCP practices produce just as reliably.
Re-review 10 Jun 2026: the Gravitex source page no longer carries the 87%/27% analysis and no independent corroboration of the split was found — primary-source erosion moves the claim to Partial. The interpretive spine (measurement discipline as the prerequisite) still stands on the Gartner 7 Apr 2026 finding (57% of failed I&O deployments cited 'too much too fast'), which remains current.
Correction log
- 19 Apr 2026Body rewritten from WP-era slop. Status moves from rewrite-in-progress placeholder to Up. New thesis: the causation runs the opposite direction from the vendor narrative — the measurement discipline was the prerequisite, the methodology name doesn't matter. 60-day review.
- 28 Apr 2026Slug migration to §6a-compliant URL: from-dmaic-to-ai-agents-how-traditional-optimization-methods-accelerate-agentic-ai-success → dmaic-for-agentic-ai-deployment. Body unchanged from 19 Apr rewrite, only the URL changed. Old slug 308-redirects to new. Reason: the long descriptive slug carried §6a-grade friction (88+ chars, vendor-cliche framing) and Google's quality algorithm had flagged the original URL as low-quality (per the 28 Apr 2026 GSC drilldown showing it in the 'Crawled - currently not indexed' bucket). The clean slug preserves the analytical content while removing the URL-level quality penalty.
- 10 Jun 2026Primary-source erosion on the headline statistic. The Gravitex page (gravitexgenesys.com/blog/ai-agents-lean-six-sigma-automating-dmaic) no longer carries the 87%/27% success-rate comparison — checked 10 Jun 2026, the URL now serves Six Sigma course-offering content with no AI-deployment success-rate data. A web search found no independent source corroborating the 87/27 pair. The claim's interpretive reading (the gap reflects pre-existing measurement discipline; ISO 9001/ITIL/SRE/HACCP produce the same four conditions) is unaffected and remains supported by Gartner's 7 Apr 2026 I&O survey (57% of failures cited 'too much too fast'). Status Up → Partial until the 87/27 figure can be re-anchored to a retrievable primary source.
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The claim: The 87% vs 27% success-rate gap between Six-Sigma and non-Six-Sigma organisations on agentic-AI deployments reflects pre-existing measurement discipline, not the DMAIC methodology itself. Agents require a clean baseline, defect definition, documented root-cause analysis, and a change-management gate — four conditions that ISO 9001, ITIL, SRE, or HACCP practices produce just as reliably.
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-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.
- AM-201 · Partial · 10 Jun 2026
One of four named datasets unanchored on review. The claim text names 'Stanford DEL's 12% clearing 300%+ ROI vs 88% at or below break-even' as one of four independent datasets. Full-text verification on 10 Jun 2026 found the Stanford DEL Enterprise AI Playbook contains no such distribution — it studies 51 successful deployments by design and carries no ROI-realisation failure data (full finding at AM-029, correction of 10 Jun 2026). The McKinsey (23% scaling, 17% EBIT-attribution), Gartner (28% fully paying off), and MIT NANDA (95% no measurable P&L impact) datasets verify; the claim's spine stands on three datasets rather than four. 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 from an ROI distribution. Status Up -> Partial.
Reviews coming up in Reporting
- AM-063 · Holding · next +15d (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 +15d (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 +15d (27 Jun 2026)
GPT-5 Pro's tiered-subscription model forces enterprises to classify problems by computational difficulty — $200/month…