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.

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 (unnamed Fortune 500 bank '$3.2M annual savings', composite multi-hospital network) that did not survive editorial-standard scrutiny. Bing Webmaster AI Performance data 2026-04-21 → 2026-05-02 showed continued AI-citation activity on the URL across the GSC follow-up window. The retraction broke the citation chain for the 'agentic AI ROI bimodal distribution' query family. New editorial-standard body at the original slug preserves the URL while replacing fabricated case studies with named primary-source datasets (Stanford DEL, Gartner Q1 2026, McKinsey State of AI 2025, MIT NANDA GenAI Divide). Slug warning (clickbait metric '312-roi') is accepted as the intentional AI-citation preservation trade-off per Peter's Option A decision 2026-05-04. Original AM-014 claim remains status:down per Holding-up rule that retracted claims do not return — the new piece carries AM-132 to acknowledge it is editorially distinct from the retracted version. Sister claims: AM-029 (Stanford 12/88), AM-053 (McKinsey 17%), AM-128 (MIT 95%), AM-129 (mid-market ROI), AM-130 (2024-2025 retrospective), AM-131 (AI Training Lead). Cadence 60-day. Trigger conditions: new Stanford DEL update with refreshed deployment cohort; Gartner I&O Q3/Q4 2026 update with new percentages; McKinsey State of AI 2026 mid-year refresh; MIT NANDA follow-up to the 2025 report; any landmark published case-study comparing the high-performing vs struggling cohort across named enterprises.