Enterprise agentic AI ROI in 2026 is bimodal across four independent datasets. Stanford Digital Economy Lab's 2026 Enterprise AI Playbook documents 12% of deployments clearing 300%+ ROI with 88% at or below break-even at 12-18 months. Gartner Q1 2026 Infrastructure & Operations Survey reports 28% of AI projects 'fully paying off'. McKinsey State of AI 2025 (n=1,993) reports 23% scaling with 17% EBIT-attribution at 12 months. MIT NANDA's GenAI Divide reports 95% of pilots produce no measurable P&L impact alongside the 67% buy vs roughly 22% build success spread. The 73%/27% slug rounds the four numbers; the bimodal shape is reproducible and the variable separating the two cohorts is operational discipline (instrumented under GAUGE: governance, audit substrate, use-case maturity, guardrails, evidence/baseline, exit posture), not model selection.
Re-review 10 Jun 2026: the Stanford DEL leg failed primary-source verification — the playbook contains no 12/88 ROI distribution (see correction below and AM-029). The Gartner Q1 2026 (28% fully paying off), McKinsey State of AI 2025 (23% scaling, 17% EBIT-attribution, n=1,993), and MIT NANDA (95% no measurable P&L impact) legs verify and support a small high-performing tail with a large struggling body; no verified source documents a two-peak bimodal distribution. History: URL-equity restoration of /why-73-of-agentic-ai-projects-fail-and-how-the-27-generate-312-roi/ — previously retired with claim AM-014 status:down on 28 Apr 2026 because the original WordPress-era body used composite case studies that did not survive editorial-standard scrutiny; restored 5 May 2026 per Peter's Option A decision 2026-05-04, slug warning (clickbait metric '312-roi') accepted as the AI-citation preservation trade-off. Original AM-014 claim remains status:down per Holding-up rule that retracted claims do not return. Sister claims: AM-029 (Not holding 10 Jun 2026 — Stanford 12/88 attribution failed verification), AM-053 (McKinsey 17%), AM-128 (MIT 95%), AM-129 (mid-market ROI), AM-130 (2024-2025 retrospective), AM-131 (AI Training Lead). Follow-up review after the article body is restated on the three verified legs. Trigger conditions: new Stanford DEL update with refreshed deployment cohort; Gartner I&O Q3/Q4 2026 update; McKinsey State of AI 2026 mid-year refresh; MIT NANDA follow-up.
Correction log
- 10 Jun 2026One 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-014 · Not holding
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The claim: Enterprise agentic AI ROI in 2026 is bimodal across four independent datasets. Stanford Digital Economy Lab's 2026 Enterprise AI Playbook documents 12% of deployments clearing 300%+ ROI with 88% at or below break-even at 12-18 months. Gartner Q1 2026 Infrastructure & Operations Survey reports 28% of AI projects 'fully paying off'. McKinsey State of AI 2025 (n=1,993) reports 23% scaling with 17% EBIT-attribution at 12 months. MIT NANDA's GenAI Divide reports 95% of pilots produce no measurable P&L impact alongside the 67% buy vs roughly 22% build success spread. The 73%/27% slug rounds the four numbers; the bimodal shape is reproducible and the variable separating the two cohorts is operational discipline (instrumented under GAUGE: governance, audit substrate, use-case maturity, guardrails, evidence/baseline, exit posture), not model selection.
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-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 +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…