Per IDC research commissioned by Lenovo (CIO Playbook 2025, February 2025; global survey n=2,920), 88% of enterprise AI proof-of-concepts fail to reach production: for every 33 POCs a company launched, only four (roughly 12%) graduated to widescale deployment, and the graduation gap is an organizational-readiness outcome (unclear ROI, insufficient AI-ready data, lack of in-house AI expertise) rather than a model-capability outcome.
Minted 10 Jun 2026 as the restated claim for the why-88 article after AM-029 (Stanford DEL 12/88 bimodal ROI attribution) failed full-text verification and went down. Anchors: (1) CIO.com, Evan Schuman, 25 Mar 2025, fetched/extracted verbatim: 'Recent research from IDC, undertaken in partnership with Lenovo, found that 88% of observed POCs don't make the cut to widescale deployment' + 'For every 33 AI POCs a company launched, only four graduated to production, IDC found' + root causes 'unclear ROI, insufficient AI-ready data and a lack of in-house AI expertise' + 'low level of organizational readiness in terms of data, processes and IT infrastructure' + Ashish Nadkarni (Group VP, IDC) board-level/underfunded-POC quotes. (2) Lenovo/IDC global infographic IDC #WW242508IG (Feb 2025, PDF extracted): 'IDC CIO Playbook 2025 Survey, commissioned by Lenovo, n=2,920'; top non-adopter barriers 33.3% financial-risk/uncertain-ROI, 33% insufficient/disorganized data, 32.5% in-house expertise. (3) APJ eBook AP242508IB (Feb 2025, PDF extracted + page 7 visually verified): funnel 23 AI POCs -> 3 AI production launches -> 62% deemed successful (source line: IDC 2024 FERS Survey Wave 4, n=900 APJ ITDMs/BDMs for the playbook survey) + 'Less than 10% of total POCs were actually deemed successful, having met predefined business goals and metrics.' Scope discipline: figure is POC-to-production graduation, NOT an ROI distribution, NOT bimodal, NOT agentic-specific (fieldwork predates the 2026 agentic wave) — the restated article states all three limits explicitly. 90-day cadence. Triggers: (1) a newer IDC/Lenovo wave (CIO Playbook 2026 or FERS successor) materially moving the graduation rate; (2) independent multi-enterprise data showing agentic-era POCs graduating at materially higher rates; (3) evidence CIO.com mischaracterized the IDC research. Siblings: AM-029 (down — the superseded fabricated attribution), AM-140 (pilot-to-production vendor-reference gap), AM-128 (MIT NANDA 95%), AM-030 (McKinsey 23%).
- AM-029 · Not holding
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The claim: Per IDC research commissioned by Lenovo (CIO Playbook 2025, February 2025; global survey n=2,920), 88% of enterprise AI proof-of-concepts fail to reach production: for every 33 POCs a company launched, only four (roughly 12%) graduated to widescale deployment, and the graduation gap is an organizational-readiness outcome (unclear ROI, insufficient AI-ready data, lack of in-house AI expertise) rather than a model-capability outcome.
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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…