title: "Verified AI Research Archive — Studies, Surveys, and Field Experiments" slug: ai-research-archive shape: ai-note author: Josh Shepherd audience: public status: eeat-candidate eeat_score_band: 85-99 version: "2026-06" document_id: ai-research-archive corpus_id: movemental-room-public
Verified AI Research Archive
Each study below opens with a Finding paragraph — the agent should read and quote from that block first. Metadata (n, date, URL) follows. Percentages are not interchangeable across studies.
Maintenance: Re-validate AI-sector statistics annually.
Executive summary
Adoption is broad and fast (Stanford AI Index 2026), but institutional payoff is rare: roughly 95% of enterprise gen-AI pilots fail to produce meaningful financial returns (MIT NANDA 2025), while only about 5–7% of organizations in any sector studied report real capability or ROI gains (BCG, McKinsey, Virtuous). Where tasks fit model strengths, controlled experiments show large productivity lifts; where they do not, AI can make people less accurate (HBS/MIT/BCG jagged frontier, 2023). For mission orgs: adoption is everywhere; transformation is not; competence is task-contingent.
Do not conflate these studies
- "MIT 95%" = MIT NANDA GenAI Divide (2025) — pilot ROI failure, not the productivity study.
- "MIT/BCG productivity" = Dell'Anna Jagged Frontier (2023) — +12% / +25% / +40% on consulting tasks.
- "Only 5% succeed" = different studies (BCG future-built 5%, McKinsey high performers 6%, Virtuous major gains 7%) — different populations.
- "68% can't tell AI from human" = unverified; use Pew 53% not confident instead.
Tier A — Enterprise adoption and ROI
MIT NANDA — The GenAI Divide: State of AI in Business 2025 (July 2025)
Finding: Roughly 95% of corporate generative AI pilots fail to deliver meaningful financial returns, while a small minority capture disproportionate value — what the report calls the "GenAI Divide." The study is based on executive interviews, employee/leader surveys, and review of public deployments. It does not prove AI is useless; it shows that once models are "good enough," integration, workflow fit, governance, and line-leader ownership determine outcomes more than the tool itself. This is not the same study as the 2023 BCG productivity experiment.
Source: Report PDF · Aditya Challapally et al., MIT NANDA Initiative · ~150 exec interviews + surveys (confirm n in PDF)
Tags: genai-divide · mit-95-percent
BCG — The Widening AI Value Gap — Build for the Future 2025 (September 2025)
Finding: In a global study of 1,250 firms, 60% generate no material value from their AI investments, while only 5% qualify as "future-built" — firms with an AI-first operating model and explicit governance. The winners are distinguished by operating-model change, not by buying better models.
Source: BCG publication · n=1,250 firms globally
Tags: bcg-value-gap-2025 · five-percent-future-built
McKinsey — The State of AI in 2025 (November 2025)
Finding: 88% of organizations now use AI in at least one business function, but only 6% qualify as "AI high performers" who attribute significant EBIT impact to AI. Among high performers, 55% have fundamentally redesigned workflows — workflow redesign, not tool selection, is the strongest correlate of value capture.
Source: McKinsey State of AI · Online survey June–July 2025 · n=1,993 · 105 nations
Tags: mckinsey-state-of-ai-2025
McKinsey — The Economic Potential of Generative AI (2023, updated)
Finding: Generative AI could add trillions of dollars in economic value across industries by automating a large share of work activities (time spent on tasks — not a simplistic "X% of jobs eliminated" claim). The report sizes upside for boards while underscoring that realizing potential requires rewiring how work is done, not merely deploying chatbots.
Source: McKinsey — Economic potential of generative AI
Tags: mckinsey-economic-potential
McKinsey — Superagency in the Workplace (2025)
Finding: Organizations report high planned AI investment but very few mature, workflow-integrated deployments (~1% "mature" in some survey framings — confirm before narrow quoting). Most respondents see limited gen-AI revenue impact to date but remain optimistic about the next few years — a pattern of broad experimentation without institutional payoff yet.
Source: McKinsey — Superagency
Tags: mckinsey-superagency-2025
Tier A — Field experiments (productivity and harm)
Dell'Anna et al. — Navigating the Jagged Technological Frontier (HBS / MIT / BCG, 2023)
Finding: In a randomized field experiment with BCG consultants, AI (GPT-4) produced large gains on tasks inside current model capability: +12.2% more tasks completed, 25.1% faster, >40% higher quality — with the biggest lifts for below-median performers (~43% vs. ~17% for top performers). On one task deliberately outside the frontier, consultants with AI were 19 percentage points less likely to be correct than those without. The lesson: the "frontier" is jagged — similar-looking tasks can fall on opposite sides of what models handle well. Successful use requires task literacy and human verification, not access alone.
Source: SSRN / HBS Working Paper 24-013 · Sept 2023 · 18 inside-frontier tasks + 1 outside-frontier task
Tags: jagged-frontier · bcg-productivity-study
BCG — GenAI skill-stretch experiment (2024)
Finding: In a follow-on experiment with 480 consultants, generative AI helped workers stretch into tasks outside their prior expertise (e.g., data-science-style work) with large aptitude gains in some dimensions — but also produced failure modes when the tool misled on statistical reliability. Junior staff using AI without senior oversight can develop phantom expertise: confident output that looks competent but is wrong.
Source: BCG press release · n=480 consultants
Tags: jagged-frontier · phantom-expertise
Tier A — Macro benchmarks
Stanford HAI — AI Index 2026 (April 2026)
Finding: The AI Index documents very high organizational AI uptake (public summaries cite ~88% using AI in at least one function), rapid consumer adoption of generative tools (~53% population adoption within three years in chapter summaries), and uneven depth — agentic AI deployment remains early across many business functions. Gen-AI spread faster than PCs or the internet in adoption curves, but transformation lags adoption — explaining why boards feel urgency while staff see little institutional change.
Source: 2026 AI Index · Economy chapter · Confirm exact figures in chapter PDF before public cite
Tags: stanford-ai-index-2026
Tier B — Reliability and hallucinations
OpenAI — Why Language Models Hallucinate (September 2025)
Finding: Hallucinations are partly incentivized by how models are trained and evaluated: standard accuracy benchmarks reward guessing over admitting uncertainty, so models learn test-taking behavior that produces confident wrong answers. Fixing hallucinations may require changing evaluation incentives, not only adding detection layers on top.
Source: OpenAI research post · PDF
Tags: hallucination-incentives
Tier B — Credibility, detection, and content flood
Pew Research Center — AI detection confidence (U.S., June 2025)
Finding: Among U.S. adults, 76% say it is extremely or very important to tell whether pictures, videos, and text are AI- or human-made, but 53% are not too or not at all confident they can actually tell — a sharp importance vs. self-efficacy gap. This supports a credibility crisis narrative without relying on the unverified "68%" statistic.
Source: Pew — How Americans View AI · n=5,023 U.S. adults · June 9–15, 2025
Tags: pew-ai-detection-2025
All About Cookies — Retrospective surprise (U.S., February 2024)
Finding: 77% of U.S. adults report encountering something online they believed was from a real person and later learned was AI-generated. This measures retrospective surprise and pervasiveness, not controlled discrimination accuracy in a lab task.
Source: All About Cookies survey · n=1,000 · Prolific · Feb 2024
Tags: ai-content-surprise
Ahrefs — AI content in new web pages (April 2025)
Finding: Of 900,000 newly detected English web pages (one per domain), 74.2% contained some AI-flagged content by Ahrefs' in-house detector — but only 2.5% were "pure AI" and 25.8% "pure human." AI is embedded widely in new publishing, yet fully synthetic pages remain a minority; detector accuracy limits apply.
Source: Ahrefs study · Apr 2025 · detector-dependent, not peer-reviewed
Tags: ahrefs-ai-content-2025
Cooke et al. — As Good As A Coin Toss (CACM 2025)
Finding: With realistic synthetic images, video, audio, and audiovisual stimuli, ~1,300 participants detected AI-generated content at near-chance accuracy (~50%) on average; prior self-reported knowledge did not significantly help. The authors conclude it is no longer feasible to rely on unaided human perception as a defense against weaponized synthetic media.
Source: DOI 10.1145/3729417 · arXiv:2403.16760
Tags: coin-toss-detection-2025
Scientific Reports — Individual differences in AI text detection (2024)
Finding: People detect AI-written text above chance on average, but performance varies widely: fluid intelligence predicts better detection, while heavier social/smartphone use correlates with mislabeling AI as human. Text discrimination is not identical to multimodal "coin toss" results — who is judging and how the task is designed matter.
Source: DOI 10.1038/s41598-024-76218-y
Tags: ai-text-detection
ACL 2025 — Expert ChatGPT users as detectors
Finding: Frequent ChatGPT-using expert annotators can be strong AI-text detectors under certain lab protocols — so expertise exists, but it does not mean the general public can reliably spot AI content in the wild.
Source: ACL Anthology 2025.acl-long.267
Tags: expert-detection
NewsGuard / EU DisinfoLab — Industrial AI misuse
Finding: Investigative organizations document thousands of AI-dominated "news" websites growing rapidly (NewsGuard) and coordinated inauthentic behavior using generative techniques at scale (EU DisinfoLab, e.g., Doppelganger). This supports "weaponized scale" claims when tied to named org metrics — do not cite "millions of bots" without a primary count.
Source: NewsGuard · EU DisinfoLab — Doppelganger
Tags: ai-content-farms · disinformation
Edelman Trust Barometer — AI trust (2025)
Finding: Edelman's 2025 trust materials show uneven comfort with corporate AI use and geographic divergence in AI trust — useful for nuance so Movemental does not imply one global mood on AI.
Source: Edelman Trust Barometer 2025 · AI flash poll
Tags: institutional-trust
Tier C — Churches and faith sector
Exponential AI NEXT — 2026 State of AI in the Church Survey
Finding: 91% of church leaders support AI in ministry and 61% use it weekly or daily (25% daily), up from 43% weekly/daily in 2024; 64% use AI for sermon preparation. Yet only 9% have a formal AI policy, 7% an AI disclosure statement, and 73% have nothing written. 75% name theological misalignment as their top ethical concern; 60% are very concerned about voice-cloning fraud; 25% say AI scams already affected their community.
Source: Exponential AI NEXT / AIforChurchLeaders.com · cited in AI Reality Paper May 2026 · confirm URL/methodology at source
Tags: church-ai-2026
Lifeway Research — Pastors and churchgoers (fielded Sept 2025, released Apr 2026)
Finding: Among 1,003 Protestant senior pastors, 42% use AI for ministry (10% regular, 32% experimenting) and 62% are concerned about non-disclosure. Among 1,200 churchgoers, 43% disagree with their pastor using AI for sermon preparation (24% strongly) — a leader/congregation gap on acceptability.
Source: research.lifeway.com · phone survey pastors ±3.3% MOE
Tags: lifeway-ai-2026
Barna & Pushpay — State of Church Tech 2026 (March 2026)
Finding: 60% of U.S. church leaders personally use AI at least monthly, but only 33% say their church uses AI in ministry or operations. 64% believe an AI use policy is important; 5% of churches actually have one — classic adoption-ahead-of-governance pattern.
Source: Barna — church leaders AI · n=1,306 · fielded Nov–Dec 2025
Tags: barna-church-tech-2026
Barna (via NPR, July 2025) — Clergy and AI sermons
Finding: Only 12% of senior Protestant clergy are comfortable using AI to write sermons, while 43% see merit for preparation and research — a clear split between ghostwriting Sunday proclamation vs. assistive study.
Source: NPR — clergy and AI sermons
Tags: sermon-ai-ethics
Tier C — Nonprofits
Virtuous & Fundraising.AI — 2026 Nonprofit AI Adoption Report (February 2026)
Finding: 92% of nonprofits use AI in some form — the question of whether to adopt is largely settled — but only 7% report major organizational capability improvements. 81% use AI individually and ad hoc rather than through shared workflows; 47% have no AI governance policy. 79% report small-to-moderate efficiency gains (faster drafts, quicker research) without deeper transformation — individual use does not compound into institutional capability without governance and shared workflows.
Source: Virtuous report · n=346 · fielded late 2025
Tags: nonprofit-ai-2026
TechSoup × Tapp — State of AI in Nonprofits 2025
Finding: In a survey of 1,321+ nonprofits, many are still exploring AI; roughly 7% report successful operational/mission integration in benchmark language, while ~85% are exploring but only ~24% have a formal AI strategy — a large strategy gap between curiosity and institutional commitment. Larger orgs adopt faster; small shops depend on one or two overloaded decision-makers.
Source: Tapp Network summary · verify exact figures in PDF
Tags: techsoup-2025
NTEN — 2025 Data Empowerment Report
Finding: Among 220+ nonprofits, AI-specific data policies are among the least implemented governance items — nearly half have no dedicated AI data policy — even as "adopting AI tools" jumped into top operational priorities. Orgs are adopting tools before building the data governance those tools require.
Source: NTEN PDF
Tags: nten-data-empowerment-2025
Forvis Mazars — AI Governance for Nonprofit Boards (February 2026)
Finding: AI governance is now a fiduciary issue for nonprofit boards: duties of Care, Loyalty, and Obedience extend to technological oversight, so boards that ignore AI governance may fail their obligations even before harm occurs. Supporting data: 47% of nonprofits have no AI governance policy; TechSoup found 76% with no formal AI strategy; only 5% with a clear, satisfactory policy. Donor and beneficiary data is routinely pasted into consumer AI tools without contractual protection.
Source: Forvis Mazars brief
Tags: nonprofit-fiduciary-ai
Grant Thornton — 2026 AI Impact Survey
Finding: Survey of 950 organizations cited in Movemental's AI Reality Paper (May 2026) as part of the cross-sector evidence base. Confirm headline percentages at the primary source before quoting specific figures.
Source: AI Reality Paper source register · Grant Thornton 2026
Tags: grant-thornton-2026
Tier C — Seminaries and institutions
Theology and Science — AI retrospective (2026)
Finding: Generative AI use is functionally universal among students under 30, while institutional policy lags: some seminaries have thoughtful policies (Columbia Theological, Saint Meinrad, Puritan Reformed, Austin Presbyterian) but no Association of Theological Schools-wide standard, and faculty within one school often operate under different rules. Hallucinations remain a persistent technical limit; deeper questions about authority, pastoral care, and formation are largely unaddressed in policy.
Source: Theology and Science 2026 retrospective · cited in AI Reality Paper May 2026
Tags: seminary-ai-policy
ATLA / theological librarianship (2024)
Finding: Qualitative evidence shows seminaries and theological educators actively debating AI pedagogy, academic integrity, and institutional policy — the sector is catching up, but quantitative prevalence data (e.g., "% of sermons AI-assisted") does not yet exist at scale.
Source: Atla Proceedings — AI in the Seminary Classroom · Theological Librarianship AI volume
Tags: seminary-ai-debate
Tier C — Documented harms and fraud
FBI IC3 — 2025 Annual Report (AI-enabled fraud)
Finding: The FBI's 2025 Internet Crime Complaint Center report is the first to break out AI-enabled fraud as its own category: 22,364 complaints with an AI-related descriptor and $893 million in adjusted losses ($352 million among Americans 60+). Voice cloning is embedded in business-email-compromise schemes; AI-employment fraud ($13M) and AI-romance scams ($19M) are documented categories — not theoretical risks.
Source: ic3.gov · PSAs I-120324, PSA250515, PSA251219
Tags: fbi-ic3-ai-fraud-2025 · voice-cloning-fraud
AI Incident Database — Incident 1355 (pastor impersonation)
Finding: Documented multi-state pattern of scammers cloning pastors' voices from sermon footage to solicit emergency donations — cases in Alabama, Florida, Missouri, Nebraska, New York, and a Philippines megachurch. Logged as AI Incident Database incident 1355; covered by WIRED, VICE, Baptist News Global.
Source: incidentdatabase.ai/cite/1355
Tags: pastor-voice-cloning
Vatican / Pope Leo XIV — Deepfake warnings (January 2026)
Finding: Documented deepfake fraud targeting religious leaders, including Pope Leo XIV; the Pope's January message for World Day of Social Communications addressed deepfake and digital fraud risks for faith communities.
Source: Dicastery for Communication · summarized in Our Sunday Visitor News, Jan 2026
Tags: religious-leader-deepfakes
Tier C — Human psychology and AI
Fernandes et al. — AI makes you smarter but none the wiser (2026)
Finding: On demanding reasoning tasks with ChatGPT-class tools, participants performed better but calibrated worse — they overestimated how well they did. The classic Dunning–Kruger pattern was disrupted; in the publicized interpretation, higher AI literacy correlated with greater overconfidence. AI can make people feel competent while leaving them less accurate about their own accuracy — a risk for sophisticated users, not only novices.
Source: DOI 10.1016/j.chb.2025.108779 · Computers in Human Behavior vol. 175 (2026)
Tags: fernandes-metacognition-2026 · do not say "DK exactly as in textbooks"
Reference frameworks (not empirical studies)
NIST AI RMF — risk management vocabulary for boards. ISO/IEC 42001 — AI management system requirements (risk, data, lifecycle, competence). EU AI Act (2024) — external policy model; does not bind most U.S. domestic nonprofits.
Debunked claims — do not cite
| Claim | Use instead |
|---|---|
| "68% can't distinguish AI from human" | Pew: 53% not confident, 76% say it matters |
| "40–60% of blogs are AI-generated" | Ahrefs: 74.2% of new pages with some AI-detected text + detector caveat |
| "Millions of synthetic accounts" | NewsGuard / EU DisinfoLab with named metrics |
| "Detection impossible in 2–3 years" | Label as Movemental forecast, not literature |
| "Credibility collapse" | Movemental framing — define when used |
Cross-sector pattern (5–7% winners)
| Sector | Broad adoption | Real capability / ROI |
|---|---|---|
| Enterprise (MIT) | Pilots widespread | ~95% pilots fail ROI; ~5% win |
| Enterprise (BCG) | AI spend widespread | 60% no material value; 5% future-built |
| Enterprise (McKinsey) | 88% use AI | 6% high performers |
| Nonprofits (Virtuous) | 92% use AI | 7% major capability gains |
| Churches (Barna) | 60% leaders use AI monthly | 5% have AI policy |
The gap is individual use vs. institutional capability, not AI yes/no.
Tags (retrieval)
genai-divide · jagged-frontier · stanford-ai-index-2026 · mckinsey-state-of-ai-2025 · bcg-value-gap-2025 · nonprofit-ai-2026 · church-ai-2026 · techsoup-2025 · nten-data-empowerment-2025 · hallucination-incentives · pew-ai-detection-2025 · ahrefs-ai-content-2025 · coin-toss-detection-2025 · fernandes-metacognition-2026 · fbi-ic3-ai-fraud-2025 · ai-stewardship-sequence
Re-validate AI-sector statistics annually. Not legal, financial, or investment advice.