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“AI revenue” is not a standard, self-explanatory number. To test a company’s claim, identify exactly what is being measured, find where it is reported, read the company’s definition and calculation, and trace any claimed AI activity through customer purchases to recognized revenue, profit and cash flow. A booking, annual recurring revenue figure, usage count, run rate or forecast must not be relabeled as historical revenue.
The method below is designed for public-company disclosures, especially U.S. filings. It helps you evaluate whether an AI claim is defined, comparable and economically supported without assuming that a marketing statement proves financial performance.
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Start by pinning down the exact claim
Write the claim down before interpreting it. Preserve the speaker’s wording rather than translating it into a more favorable metric. Record these fields:
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- Speaker and date: Identify the executive, filing, presentation or advertisement and when the statement was made.
- Period: Note the fiscal quarter, year, trailing period or point-in-time date.
- Geography and scope: Record the country, segment, product family or customer group covered.
- Metric type: Mark whether it is recognized revenue, a current run rate, bookings, annual recurring revenue (ARR), pipeline, customer adoption, usage, cost savings or a forecast.
- Denominator and base: Capture the prior-period amount or other base behind a percentage increase.
- Exact wording: Keep qualifiers such as “expected,” “potential,” “eligible,” “AI-enabled” or “in the pipeline.”
These categories are not interchangeable. A customer using an AI feature, a signed booking and revenue recognized under the applicable accounting rules occur at different points in the economic process.
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Ask what “AI” means in this business
Determine which product or operation is described as AI, who pays for it and whether the company says the use is current or proposed. An AI label may refer to a feature sold to customers, an internal automation project, a third-party model embedded in a broader service or a planned product that has not launched.
In a June 24, 2024 statement, SEC Division of Corporation Finance Director Erik Gerding listed review considerations for material AI disclosures, including that a company “has a reasonable basis for its claims when discussing artificial intelligence prospects.” The statement asks companies to define AI and explain how actual or proposed use could affect results; it is staff review guidance, not a standalone accounting rule that defines AI revenue. Read the statement at The State of Disclosure Review.
Useful questions include:
- What feature, service or process is actually AI-enabled?
- Is the customer paying a separate price, or is the feature bundled into an existing contract?
- Does the company describe customers using it now, or only product plans and potential demand?
- Which reported segment contains the activity?
- What result does management attribute to it: additional sales, retention, productivity, lower costs or something else?
Find the primary documents
For a public company, begin with the company’s filings rather than a headline or social-media post. Investor.gov recommends using the SEC’s EDGAR system, reviewing disclosures and checking promotional campaigns. Its investor alert on AI fraud is available at Artificial Intelligence (AI) and Investment Fraud.
- Search EDGAR for the issuer and date. Locate the latest Form 10-K, Form 10-Q, current reports, earnings release and investor presentation.
- Read the financial statements first. Check consolidated revenue, segment revenue, operating income and the cash-flow statement for the same periods.
- Read Management’s Discussion and Analysis (MD&A). Look for explanations of changes in revenue, margins, cash flow, capacity, demand and investment.
- Locate the AI metric’s definition. Search the filing and earnings materials for the exact term, calculation, period and scope.
- Compare the call with the filing. An earnings-call statement may use a narrower or different measure than the filed documents. Record any difference instead of treating the statements as equivalent.
- Check subsequent filings. A later quarter may revise the definition, change the reporting scope or explain whether an earlier forecast became a result.
Promotional material can provide clues, but it does not replace the company’s filed disclosure. If a claim appears only in promotion and not in the financial reporting, treat that as a reason to investigate the measurement and evidence.
Classify the number before judging it
The same company can discuss several numbers that sound like growth. Classify each one using its own definition and accounting status.
| Term | What it represents | How to evaluate it |
|---|---|---|
| Revenue reported under GAAP | Revenue recognized in the financial statements under generally accepted accounting principles. | Reconcile the AI description to the reported company or segment revenue and the applicable recognition period. |
| Non-GAAP financial measure | A financial measure that adjusts or presents results outside the comparable GAAP measure. | When Regulation G applies, look for a quantitative reconciliation to the most directly comparable GAAP measure and assess whether the presentation could mislead. |
| Company-defined operating KPI | A performance indicator management uses to run or describe the business; it is not automatically a non-GAAP financial measure. | Find the definition, calculation, period, scope, reason it is useful and any change from prior periods. |
| Bookings | Orders or contractual commitments recorded under the company’s stated policy, which may precede revenue recognition. | Ask when the bookings convert to revenue, what cancellation or delivery conditions apply and how much was recognized in the period. |
| Annual recurring revenue (ARR) | A recurring-revenue run-rate measure, normally calculated from a point in time rather than recognized for the entire year. | Read the company’s formula, included contracts, currency treatment and relationship to reported revenue. |
| Pipeline | Potential opportunities at different stages of a sales process. | Ask how opportunities are qualified, the conversion history and the time horizon; do not call pipeline revenue. |
| Run rate | An extrapolation of a recent or current level over a longer period. | Identify the starting period and assumptions; a run rate is not a report of revenue already earned. |
| Forecast or projection | Management’s expectation about future results. | Separate it from historical performance and inspect assumptions, dependencies, time horizon and supporting evidence. |
SEC guidance explains that an operating KPI is not automatically a non-GAAP financial measure; classification depends on what the measure represents and how it is calculated. For covered non-GAAP measures, the Commission’s guidance on Regulation G describes the reconciliation requirement and the prohibition on materially misleading presentation. See Conditions for Use of Non-GAAP Financial Measures.
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Interrogate an AI-specific KPI
If management supplies an AI-attributed revenue figure or another company-derived KPI, require enough information to reproduce its meaning. The SEC’s Commission guidance on MD&A metrics identifies the following disclosure questions:
- What is the precise definition?
- How is it calculated?
- What period, customers, products, segments and geography does it include?
- Why is it useful to investors and to management?
- Did the basis, estimation method or scope change from an earlier period?
- What controls does the company use over material information derived from its own data?
Read the guidance at Commission Guidance Regarding Management’s Discussion and Analysis of Financial Condition and Results of Operations. If a company introduces “AI revenue” without a calculation or prior-period basis, you cannot determine whether the change reflects genuine sales, a broader definition or a reclassification.
Build the economic bridge from AI activity to cash
A credible claim connects a product or operational use to a financial outcome. Trace the chain in order:
- Capability: Identify the AI feature, model, service or internal process.
- Customer or user: Establish who uses it and who pays for access.
- Commercial event: Determine whether the event is a sale, renewal, expansion, booking or only usage or interest.
- Recognition: Find when the related amount enters reported revenue and in which segment.
- Profit effect: Examine gross margin, operating expenses and any incremental delivery or infrastructure costs.
- Cash effect: Compare operating cash flow, capital spending and working-capital needs with the claimed growth.
For example, a statement that customers “have access to” an AI tool establishes availability, not the amount of paid adoption or recognized revenue. A large contract can create a booking while delivery and revenue recognition occur over later periods. The MD&A’s purpose is to give context about the quality and variability of earnings and cash flow; use that context rather than stopping at an adoption count. The SEC Financial Reporting Manual, Topic 9, explains this role at Financial Reporting Manual, Topic 9.
Compare growth on a like-for-like basis
Align the comparison before calculating or repeating a growth rate.
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- Units and currency: Keep units consistent and identify currency translation effects.
- Scope: Match segment, product and geography. Separate consolidated results from an individual business line.
- Base: Report the absolute amount and the denominator whenever available. A high percentage from a small base may have little effect on the company total.
- One-time effects: Identify acquisitions, divestitures, launches, contract timing, pricing changes and other unusual items.
- Definition: Confirm that the metric has the same calculation in both periods.
- Context: Compare AI-attributed claims with total and segment revenue, margins, operating cash flow and capital requirements.
If the periods or definitions differ, say the figures are not directly comparable. Do not force a ranking from numbers that measure different things.
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Keep forecasts separate from achieved results
A projection is not realized revenue. Label whether management is describing historical performance, a current run rate or a future expectation. For each forecast, record:
- the target period and whether it is a quarter, year or longer horizon;
- assumptions about customer adoption, pricing, capacity, supply, hiring or model costs;
- dependencies such as product launches, regulatory approvals, data access or third-party infrastructure;
- the evidence management provides for those assumptions; and
- what would cause the estimate to change.
The SEC’s AI disclosure statement calls for a reasonable basis for claims about AI prospects. That standard does not turn a forecast into evidence that the forecast will be met. Revisit the next filing to see which assumptions became measurable results.
Test profitability, cash generation and investment needs
Revenue growth can coexist with rising costs. Check whether the company discloses the resources required to deliver the AI offering, including computing capacity, data, personnel, customer support, acquisitions and capital spending.
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Read risk factors and contrary evidence
Search the risk factors and MD&A for information that could weaken or delay the claim. Material issues may include:
- limited delivery or computing capacity;
- dependence on a small number of customers;
- competition and rapid product substitution;
- shortages or delays in hardware, data or other inputs;
- higher model, hosting, support or compliance costs;
- privacy, security, intellectual-property or regulatory exposure; and
- changes in demand, pricing or contract terms.
Tailored discussion of how the company actually uses AI and the risks attached to that use is more informative than generic statements that AI could transform the industry. The SEC staff statement identifies tailored risk disclosure as a review consideration when the risks are material.
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Check promotional context without jumping to a fraud conclusion
Investor.gov warns that false claims about public-company AI products and services can appear in pump-and-dump schemes. Its guidance recommends reviewing the issuer’s disclosures and promotional campaigns and considering companies developing similar products when promotion appears more focused on attracting investors than on building the business.
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1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteUse that framework to identify warning signs, not to declare that a particular claim is false. A promotion that emphasizes a huge market, customer interest or an AI label while omitting definitions, filings, revenue recognition and costs deserves closer verification. Evidence of a warning sign is not proof of fraud; the next step is to compare the promotion with the company’s primary disclosures.
How to compare two companies’ AI claims
Compare only metrics with sufficiently similar definitions and periods. These are the most useful axes:
| Comparison axis | What to document |
|---|---|
| AI use | Whether the company describes current or proposed use, the product or operation involved and the customer who pays. |
| Revenue basis | Whether AI-attributed revenue is separately defined, recognized under GAAP, presented as a non-GAAP measure or supplied as another KPI. |
| Scale and growth | Absolute amount, growth rate, comparison base, fiscal period, currency and segment. |
| Company context | Consolidated and segment revenue trends, profitability and margins. |
| Cash and investment | Operating cash flow, capital needs and costs required to provide the AI service. |
| Customer evidence | Paid adoption, contract or renewal information and customer concentration where disclosed. |
| Risks and forecasts | Tailored risks, forecast assumptions, dependencies and the distinction between expectations and results. |
When one issuer reports a defined AI-related KPI and another reports only broad adoption, the figures are not directly comparable. State that limitation instead of producing a league table.
Use a repeatable claim worksheet
For each statement, complete this record before reaching a conclusion:
- Claim: Copy the exact sentence, speaker and date.
- Measure: Label it GAAP revenue, non-GAAP measure, KPI, bookings, ARR, pipeline, run rate, usage, savings or forecast.
- Definition: Quote or paraphrase the issuer’s calculation, scope and period.
- Document: Link the earnings release, 10-Q, 10-K, segment note or MD&A where the measure appears.
- Bridge: Map capability, payer, commercial event, recognition, margin and cash.
- Comparison: Align period, base, segment, currency, definition and one-time effects.
- Risks: Record capacity, concentration, competition, cost, regulatory, security and delivery constraints.
- Status: Mark the claim as historical result, current run rate or forward-looking expectation, and list what remains unverified.
Common mistakes and how to correct them
Calling adoption revenue
Mistake: Treating users, access, usage or customer announcements as dollars earned.
Correction: Find the price, paying customer, contract event and recognition period.
Treating ARR as annual recognized sales
Mistake: Presenting a point-in-time recurring run rate as revenue already earned over twelve months.
Correction: Use the issuer’s ARR formula and compare it with reported revenue for the period.
Assuming every KPI is non-GAAP
Mistake: Applying reconciliation expectations without first identifying what the metric measures.
Correction: Classify the KPI, then check whether Regulation G applies and whether the required reconciliation is provided.
Repeating a percentage without its base
Mistake: Quoting a large increase without the starting amount, period or segment.
Correction: Report the absolute amount and denominator and align the periods.
Using a forecast as proof
Mistake: Treating management’s target as an achieved result.
Correction: List assumptions and revisit later filings for actual performance.
Inferring AI revenue from investment
Mistake: Assuming that spending on models, hardware or hiring equals AI sales.
Correction: Look for a disclosed customer transaction and recognized revenue; if none is separately quantified, do not infer an amount.
Scope and limits of this method
The SEC materials provide a disciplined way to assess company definitions and disclosures, but they do not establish a universal public metric called “AI revenue.” Some companies may not separately quantify revenue attributable to AI. General AI investment, product announcements or management enthusiasm cannot fill that gap.
This framework is U.S.-focused and based on SEC filing conventions and investor guidance. Non-U.S. issuers may report under different securities regimes, accounting standards and disclosure practices. The method does not validate a company’s books or determine whether a particular claim is true; it shows what to verify, what the number actually measures and where the evidence is missing.
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Give an AI growth claim weight only after you can state its definition, calculation, period, scope and accounting status; locate it in primary company documents; connect it to a paying customer and recognized results; and reconcile the growth with margins, cash flow, investment and risks. If any link is missing, describe the claim in its narrower terms—such as pipeline, usage, bookings, run rate or forecast—instead of calling it AI revenue.
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