The AI assistant vs AI agent distinction is operationally meaningful for enterprise procurement: assistants are reactive, request-driven, human-in-the-loop systems whose deployment and ROI patterns are documented at named-customer scale (McKinsey's Lilli platform with 72% employee adoption, 500,000+ prompts processed monthly, ~30% time savings on knowledge work, six-month deployment from proof-of-concept to full rollout); agents are proactive, goal-directed, autonomous-action systems whose deployment patterns are still emerging and whose cohort-scale failure rate is documented (Gartner June 2025: 40%+ of agentic AI projects cancelled by end-2027). Assistants and agents are different procurement decisions rather than points on a continuum; an assistants-first enterprise roadmap is defensible on the documented named-success cohort, an agents-first roadmap is defensible only when the AM-004 discovery-phase tests are cleared and the AM-140 procurement-committee questions are answered.
Claim created at publish; review on 60-day cadence. Anchor sources: OpenAI agents documentation (platform.openai.com/docs/guides/agents) for the definitional anchor; Sam Altman public 2025-prediction quote ('AI agents will join the workforce and materially change the output of companies'); McKinsey Lilli platform public reporting (72% adoption, 500K+ prompts monthly, ~30% time savings, 6-month deployment); McKinsey 'Seizing the agentic AI advantage' research thread including the Gen AI Paradox figure (78% adoption / 80% no material earnings impact); Gartner June 2025 cancellation prediction (40%+ agentic AI projects cancelled by end-2027); Gartner AI Agents Implementation Guide. Sister claims: AM-004 (discovery-phase organisational-readiness test that gates agents-first decisions), AM-140 (procurement-committee six pre-pilot questions), AM-007 (vendor-response split applies to agents, less to assistants), AM-009 (browser-resident agent class), AM-010 (CIO playbook five operational characteristics — assistant maturity differs from agent maturity at all five), AM-030 (McKinsey 23% scaling cohort), AM-130 (four evidence classes for procurement readers). Trigger conditions to revisit before next cadence: (a) Sam Altman's 2025 prediction graded against year-end-2025 outcome data on actual agent-deployment scale; (b) McKinsey or analogous research wave compressing the assistant-vs-agent deployment-time and adoption gaps materially; (c) a published methodology proposing that assistants and agents be evaluated as a single procurement category rather than as distinct decisions.
/holding/AM-005/Embed this claimiframe + oEmbed
The card auto-updates when the claim's status, last-reviewed date, or correction log changes. Embedders never need to refresh — the card is rendered live from the canonical record.
Email-me when AM-005's status, next review date, or correction log changes. One email per change. No newsletter subscription, no other mail.
The claim: The AI assistant vs AI agent distinction is operationally meaningful for enterprise procurement: assistants are reactive, request-driven, human-in-the-loop systems whose deployment and ROI patterns are documented at named-customer scale (McKinsey's Lilli platform with 72% employee adoption, 500,000+ prompts processed monthly, ~30% time savings on knowledge work, six-month deployment from proof-of-concept to full rollout); agents are proactive, goal-directed, autonomous-action systems whose deployment patterns are still emerging and whose cohort-scale failure rate is documented (Gartner June 2025: 40%+ of agentic AI projects cancelled by end-2027). Assistants and agents are different procurement decisions rather than points on a continuum; an assistants-first enterprise roadmap is defensible on the documented named-success cohort, an agents-first roadmap is defensible only when the AM-004 discovery-phase tests are cleared and the AM-140 procurement-committee questions are answered.
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…