The agentic AI discovery phase upstream of procurement is not a vendor-evaluation sprint to a go-decision; it is an organisational-readiness test where the deciding question is whether the procuring enterprise can clear four upstream tests (definitional clarity across the senior team, a named operational candidate workflow with measured baseline and named owner, threat-model literacy on the cross-agent and browser-resident classes, and workforce-readiness against the BCG access gap) before any vendor conversation. Gartner's January 2025 poll of 3,412 executives (19% significant, 42% conservative, 31% wait-and-see, 8% no investment) describes the phase distribution; the 39% in 'wait-and-see' or 'no investment' postures are not failing discovery but correctly identifying that the upstream tests are not yet cleared.
Claim created at publish; review on 60-day cadence. Anchor sources: McKinsey 'Seizing the agentic AI advantage' research ($2.7T paradox; 80% gen AI use without bottom-line impact); Gartner June 2025 prediction (40%+ agentic AI cancellation by end-2027); Gartner January 2025 executive poll (n=3,412; 19/42/31/8 distribution; agent-washing warning); IBM's Maryam Ashoori on agent autonomous-action definition; Vanderbilt's Jules White via Coursera Agentic AI for Leaders; Dialpad CSuite Report (91% data-quality gap, 6% workforce preparation). Sister claims: AM-030 (McKinsey 23% scaling cohort and four operational preconditions), AM-010 (five operational characteristics named-success deployments share), AM-140 (procurement-committee six pre-pilot questions), AM-007 (vendor-response split), AM-009 (browser-resident agent disclosure read), AM-006 (Atlanta Fed wage premium / BCG access gap). The four upstream tests in this claim map onto and feed forward to the AM-030 four operational preconditions and the AM-140 procurement six. Trigger conditions to revisit before next cadence: (a) subsequent Gartner or analogous executive-poll wave compressing the 19/42/31/8 distribution materially; (b) new analyst framework or academic publication explicitly proposing a discovery-phase methodology that supersedes the four upstream tests; (c) regulatory action (EU AI Act post-market monitoring, sectoral regulator) imposing a discovery-phase due-diligence requirement that reshapes the variable set.
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The claim: The agentic AI discovery phase upstream of procurement is not a vendor-evaluation sprint to a go-decision; it is an organisational-readiness test where the deciding question is whether the procuring enterprise can clear four upstream tests (definitional clarity across the senior team, a named operational candidate workflow with measured baseline and named owner, threat-model literacy on the cross-agent and browser-resident classes, and workforce-readiness against the BCG access gap) before any vendor conversation. Gartner's January 2025 poll of 3,412 executives (19% significant, 42% conservative, 31% wait-and-see, 8% no investment) describes the phase distribution; the 39% in 'wait-and-see' or 'no investment' postures are not failing discovery but correctly identifying that the upstream tests are not yet cleared.
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-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.
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…