Primary research on measuring AI ROI.

The AI Value Account

A weekly newsletter with practical frameworks for connecting AI spend to business outcomes. Recent issues cover finance ownership of AI economics and the gap between AI cost visibility and return — the same distinction this site's own taxonomy is organized around.

Value Add VC

A weekly investor newsletter with an Enterprise AI Adoption Dashboard. Investor-authored — low evidence level, closer to market commentary than primary research.

CIO Dive (newsletter)

A free trade newsletter covering enterprise AI spend and cost management, including coverage of the Tokenomics Foundation launch also cited elsewhere in this index.

One Useful Thing

A Wharton professor's newsletter on how organizations use AI. Useful for adoption research commentary — cite the underlying studies Mollick covers, not the newsletter posts themselves, as this index does elsewhere for his LinkedIn commentary.

Mostly Metrics

A weekly finance newsletter from a former tech CFO, widely read by CFOs and FP&A teams. Useful for how finance leaders budget for and measure AI, more than for any single cited figure.

FinOps Weekly

A practitioner newsletter with over 7,000 readers; recent issues cover tokenomics and AI cost discipline. Low evidence level — practitioner commentary rather than original data.

Ramp Economics Lab

A monthly Ramp AI Index update built from card and bill-pay data across 70,000+ US businesses, now splitting AI spend across subscriptions, coding agents, and tokens/APIs. Built entirely from Ramp's own customer base — sample-flag it the way this index flags any single-platform data source.

Tracks AI spend across 70,000+ US businesses that use Ramp. — Ramp Economics Lab, 2026

AI to ROI

A weekly issue with an AI story, report, metric, and use case of the week, plus a Saturday news roundup. The authors also publish the Big Book of AI Metrics, a standing collection of 81 metrics. Also on Substack.

Inside the First Tokenomicon

A firsthand recap of the inaugural Tokenomicon in Amsterdam, covering the Big-T Notation, the Tokenomics Mind Map, and talks from UNICC and Infracost. Quesma sits on Tokenomics Foundation working groups — noted here as an affiliation, not a disqualifier.

State of Tokenomics

472 respondents across 11 industries, unveiled at the first Tokenomicon in Amsterdam. Proving ROI ranks as the single biggest reported challenge, ahead of attribution and pricing complexity. Defined cost ownership and router adoption both correlate with reported CFO-visible value, though the survey does not establish that either one causes it.

43% of respondents name proving ROI as their biggest AI-spend challenge. — Tokenomics Foundation, State of Tokenomics, Sept 2026

The Tokenomics Brief

J.R. Storment's Foundation-run show. Episode 17 proposes a "Total Cost of AI" framing, arguing token and API costs make up only 10 to 25% of what AI actually costs an organization. Episode 16, "Routing Wars," covers early State of Tokenomics buy-side data: over 80% of respondents are using, implementing, or evaluating model routers, with cost-per-outcome replacing cost-per-token as the tracked metric.

Token and API costs make up only 10–25% of the real, total cost of AI, per the show's framing. — The Tokenomics Brief, Ep. 17, Sept 2026

FinOpsPod

The Foundation's own practitioner show. Episode 42 covers AI cost tracking across both SaaS-based and self-hosted models.

From AI to ROI

A consultancy-run show on why pilots stall in proof-of-concept purgatory, and why efficiency gains alone don't establish that an AI deployment succeeded.

Good Decisions

A vendor-run show. One episode has two CFO hosts press ModelOp's own CEO on proving AI ROI and managing AI as a portfolio of investments — a vendor interviewing itself, useful for the questions asked more than the answers given.

AI to ROI

A weekly show hosted by Ray Rike on how enterprises measure AI ROI. Notable episodes include a deep dive on Mavvrik's AI cost-governance approach and a rundown of five AI spend metrics every CFO should track.

FOCUS for AI

Spec-level guidance for mapping token usage, model SKUs, and reserved throughput into FOCUS's standard billing columns — the technical layer underneath this site's own cost-attribution and cost-per-token glossary entries.

FOCUS 1.4 and the Road to 1.5

Trade coverage of the FinOps Foundation's FOCUS specification extending into AI cost data. Notes that the upcoming 1.5 release is expected to add provider price-list data and native token tracking to the standard.

Executive AI Leverage Report

Tracks who signs off on AI spend inside the organization, with the CFO's share of that decision rising. Built on small, room-based samples gathered at live sessions rather than a broad panel — read it as directional, not representative.

Q3 2026 CFO Survey

About 230 US CFOs. A useful counterweight to the no-ROI narrative elsewhere in this index: 80% expect profit growth, a survey-series record, and partly credit AI productivity for it. The survey does not isolate AI's share of that expectation from other factors.

JPMorgan and Accenture Join the Tokenomics Foundation

An interview with the Tokenomics Foundation's J.R. Storment on the standardization timeline, following JPMorgan and Accenture joining as members. Storment expects near-monthly framework and value-metric releases through the end of the year — a claim about pace, not yet a delivered result.

Token Economics (Tokenomics)

An AI-governance vendor's episode on tokenomics, aimed at a governance and compliance audience rather than finance.

Why AI Isn't Delivering ROI in Logistics

An industry-vertical survey of 30 logistics players — useful precisely because it is not horizontal. The gap between "AI is a priority" and "AI produced a measurable result" is unusually stark and unusually well quantified here.

97% call AI a priority; only 13% see measurable financial results. — BCG, Aug 2026

Accurate Company Valuations Using FinOps Principles

Applies FinOps cost-attribution discipline to company valuation itself — treating technology investment value as something that should be traceable the same way cloud spend is. A niche but concrete example of the "govern the assumption layer" gap this site's vendor research keeps surfacing.

FinOps X 2026 Day 2 Keynote

Covers FOCUS 1.4 and introduces a Crawl, Walk, Run maturity model for agentic AI spend — the practitioner-facing follow-on to Day 1's Tokenomics Foundation announcement.

FinOps X 2026 Day 1 Keynote: The Wild West of AI

The keynote where the Tokenomics Foundation initiative was introduced — converting energy and capital into AI tokens, then using those tokens efficiently to create value. A primary, dated snapshot of where the category's own standards body saw things heading, worth revisiting against how the year actually played out.

New Foundation Aims to Tackle AI Token Cost Management

Enterprise-IT framing of the Linux Foundation's intent-to-launch announcement for the Tokenomics Foundation, made at the FinOps X 2026 keynote. Trade-press coverage of the announcement itself, not of any measured result.

The CAIO Connect Podcast

A mid-2026 review episode on the AI productivity J-curve and the general difficulty of measuring ROI. Low evidence level — commentary rather than original data.

Platformer Interview with AWS CEO Matt Garman

Garman claims 90% of CIOs in one room reported positive AI ROI or a credible near-term path to it. An anecdotal, vendor-executive counterpoint to the more skeptical survey data elsewhere in this index — cited here via Amazon's own write-up of the interview, not an independent transcript.

90% of CIOs in one room reported positive AI ROI or a near-term path to it, per Amazon's account. — Platformer interview with AWS CEO Matt Garman, via Amazon, June 2026

Next in Tech, Episode 273: FinOps and AI

An analyst recap of the FinOps X 2026 announcements, noting that AI cost metrics remain far more opaque than cloud cost metrics were at the same stage of FinOps's maturity. Analyst-level evidence rather than primary data.

Finance Needs AI ROI

A vendor-commissioned survey of 260 finance executives, including 135 CFOs. The finding worth citing is the gap, not the framing: 87% say they need to tie AI spend to outcomes within the year, but only 22% say they can do that today. CloudZero both conducted the survey and sells the product that closes this gap — read accordingly.

87% of finance leaders need to tie AI spend to outcomes within the year; only 22% can today. — CloudZero, Finance Needs AI ROI, June 2026

Tokenomics and FinOps: How Do They Fit Together?

Positions tokenomics as a companion discipline to cloud FinOps rather than a replacement for it — one lens on unit cost, one on value management, meant to be read together. Useful for explaining to a finance audience why this site treats them as adjacent, not identical.

FinOps for AI: Tools & Services Considerations

A vendor-neutral checklist for evaluating AI cost-management tooling — what to ask about token costs, GPU costs, and vendor selection before buying. A good cross-reference against this site's own vendor directory before taking any vendor's cost-visibility claim at face value.

What Microsoft Research Tells the CFO About AI ROI

CFO-angle coverage of Microsoft's own product telemetry alongside a 20,000-user survey. The vendor data sits directly underneath the reporting — bias-flag the underlying figures the same way this index flags IDC's Microsoft-commissioned $3.70 study.

FinOps for AI Overview

The Foundation's own implementation-guidance baseline for applying FinOps discipline to AI workloads — cost allocation, GPU utilization, and usage optimization as a practice, not a single tool. Useful as the reference definition to check any vendor's "FinOps for AI" claim against.

GenAI-FinOps: How Token Pricing Really Works

A practitioner-level teardown of what advertised per-token pricing actually leaves out — pricing tiers, cached inputs, routing, and context length all move the effective cost away from the list price. Directly underwrites this site's distinction between API list price and effective token cost.

Unlocking AI Business Value with FinOps

The clearest practitioner framework for connecting AI spend to outcomes rather than just tracking it. Introduces the production → consumption → monetization model this site's glossary borrows for tokenomics terms.

Token Economics: The Atomic Unit of AI Value

The source essay for treating the token as the atomic unit of AI economics — production, consumption, and monetization as three linked layers. This site's tokenomics glossary entries are built on this framing directly.

AI ROI: The Paradox of Rising Investment

The strongest "finance reality check" in the set: payback periods running longer than boards expected, even as budgets keep rising. Separates modeled ROI from realized ROI explicitly.

Payback periods on enterprise AI programs are lengthening year over year. — Deloitte, 2026

How to Maximize AI ROI in 2026

More executive insight piece than formal report, but a clean statement of average-versus-best-in-class ROI language that shows up across the category.

AI Value

A rolling topic hub rather than a single report — the Foundation's running definition of AI value as productivity, revenue, cost efficiency, and resilience, not just direct savings. Worth checking periodically rather than citing as a dated source.

The State of AI: Global Survey 2026

The broad annual read on enterprise deployment, scaling, and function-level adoption. Useful as a horizontal baseline, weak on financial outcome detail.

Scaling remains concentrated in a minority of functions despite near-universal pilot activity. — McKinsey, 2026

Generating ROI with AI

A maturity-stage framing that separates average performers from best-in-class programs. Useful for KPI structure more than for a single headline number.

The ROI of Generative AI

A global survey of 2,500 senior leaders on time-to-value and revenue impact from generative AI deployments. Skews toward self-reported outcomes rather than audited financials.

52% of leaders report their organization has deployed AI agents. — Google Cloud, Jan 2026

2026 CFO Report

102 finance leaders at private-equity- and venture-backed firms. Ranks AI use cases by payback period and names data readiness as the top blocker to realizing it. A services firm survey of its own client base — bias-flag it the way this index flags any vendor- or advisor-run sample.

State of FinOps 2026

The Foundation's annual benchmarking survey on FinOps maturity, now extending into AI spend management and governance. A useful adoption baseline to read against the AI-specific surveys elsewhere in this index, since it is measuring a practitioner population rather than a vendor-selected one.

AI Radar 2026

2,360 executives across 16 markets. Projects AI spend to double in 2026, with 94% of respondents planning to keep investing even without immediate returns — a useful read on intent that says little about realized value.

94% of executives plan to keep investing in AI even without seeing immediate returns. — BCG, AI Radar 2026, Jan 2026

The Business Opportunity of AI

The source of the "$3.70 return per dollar spent" figure that circulates across the category, usually stripped of attribution. A survey of 2,000 enterprises, sponsored by a vendor with a direct stake in the answer — read the multiple, but reconcile it against your own invoice before citing it.

$3.70 claimed return per dollar of AI spend. — IDC / Microsoft, cited widely, 2026

29th Global CEO Survey

4,454 CEOs across 95 countries. The strongest skeptic anchor in the set, and more current than Deloitte alone: more than half of respondents report neither a cost nor a revenue benefit from AI.

56% of CEOs report neither a cost nor a revenue benefit from AI. — PwC, 29th Global CEO Survey, Jan 2026

CFOs See Little Substantial ROI From AI Spending

200 US CFOs. Only 14% see clear, measurable AI impact today, while 66% expect it within two years — a gap between current results and forward expectation that shows up across most CFO-focused surveys in this index.

Only 14% of CFOs see clear, measurable AI impact; 66% expect it within two years. — RGP, via CFO.com, Dec 2025

Gen AI in the Enterprise: From Hype to Human Capital

A companion analysis to Wharton's main adoption report, focused specifically on workforce and skills effects — including the less-comfortable finding that some employees report AI is making them feel de-skilled, not augmented.

Predictions 2026: AI Moves From Hype To Hard Hat Work

Forecasts enterprises deferring roughly a quarter of planned 2026 AI spend into 2027, because fewer than a third of decision-makers can tie AI value to financial growth. The skeptical analyst framing this site is built around — and it is Forrester's own research, not a secondary summary of Gartner.

Fewer than 1 in 3 decision-makers can tie AI value to financial growth. — Forrester, Predictions 2026

2025 AI Adoption Report

A third-year cross-sectional enterprise study with a formal ROI methodology, benchmarked across function and company size — more academically grounded than most vendor-run surveys.

82% of enterprise leaders now report positive ROI on generative AI investment. — Wharton / GBK, 2025

The Widening AI Value Gap

The actual source of the "5% future-built / 35% scaling / 60% minimal value" segmentation that gets cited around this category — often misattributed to BCG's later AI Radar report. 1,250 companies assessed against a 41-capability maturity framework, and most are stuck in the bottom tier.

Only 5% of companies are "future-built" on AI; 60% capture minimal value. — BCG, The Widening AI Value Gap, Sept 2025

95% of Enterprise GenAI Pilots Fail to Reach P&L Impact

The most-cited failure statistic in the category. Measures a different cohort and timeline than IDC's $3.70 figure — the two numbers are not contradictory once you read past the headline, they are answering different questions about different companies.

95% of enterprise GenAI pilots fail to reach P&L impact. — MIT, reported in Fortune, Aug 2025