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To research a public company’s AI exposure, compare what it sells and uses with what it actually reports: start with its latest Form 10-K, then check dated product pages, investor presentations, earnings releases and call materials. Separate current offerings and reported results from plans, forecasts and promotional claims; then weigh the opportunity against costs, dependencies and risks. The result is an evidence-based picture of a company’s AI-related business—not a shortcut to predicting its stock.
Start with the latest Form 10-K
Find the company’s most recent Form 10-K and confirm the registrant, filing date and fiscal period before taking notes. Use the filing’s contents page to navigate, and treat the document as the anchor for your analysis rather than relying on a company’s marketing description alone. For any live analysis, use the issuer’s actual current filing; illustrative examples cannot substitute for it.
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- Business: Map the products and services the company sells, the markets it serves and its stated strategy. Identify whether AI is part of a named offering, infrastructure, or a broader strategic description.
- Risk Factors: Look for stated uncertainty around adoption, competition, development costs, delays, capacity, customers, suppliers, regulation, intellectual property and cybersecurity.
- Cybersecurity: Note the company’s disclosed governance and risk controls. Read these alongside any AI-related security or data concerns rather than treating AI as a standalone topic.
- MD&A: Check management’s discussion of results and trends for evidence of financial effects, costs, investment or changing demand.
- Financial statements and market-risk disclosures: Ground claims about AI in the company’s overall financial context. A product announcement does not establish how much revenue or profit it contributes.
A mention in a filing establishes that management discussed a topic; it does not by itself show that the exposure is material or that a deployment is working.
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Use company and investor materials as dated, separate evidence
Read the company’s product pages, investor presentations, earnings releases and call materials alongside the filing. For each item, record its exact URL and publication date. These materials can be newer or more specific than a filing, but they are still company statements and should be labeled as such.
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Do not assume a website statement is incorporated into a filed report. For example, C3.ai’s FY2025 annual report says that its website and social-media content are not incorporated by reference into its Form 10-K. The relevant point for your own analysis is to preserve each statement’s channel and provenance, then compare it with the company’s filed disclosures.
Distinguish selling AI from using AI
Search for specific mechanisms, not just the word “AI.” Determine whether the company sells AI hardware, software or services; uses AI internally; or does both. A company may have meaningful operational exposure without selling an AI-branded product, while an AI product announcement alone does not prove meaningful customer adoption.
- Products and features: Record named offerings and what they do. Identify whether AI is central to the product, one feature, or general positioning.
- Internal deployments: Note disclosed uses in operations, customer support, development or other functions, and whether the company reports observed outcomes.
- Investment and capacity: Look for research and development spending, capital commitments, computing capacity, data-center buildout, energy needs and supplier dependencies.
- Customer demand: Separate disclosed customers, adoption and revenue contribution from pipeline, interest or statements about an addressable opportunity.
- Customer economics: Check whether customers can fund the infrastructure or purchases that the company’s AI opportunity depends on.
Classify every statement as a current product, observed result, target, forecast, strategy or risk. This prevents a forecast or promotional phrase from being mistaken for realized revenue.
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Assess opportunity and execution risk together
For each claimed opportunity, ask what must happen for it to affect the business and what could prevent that. Relevant dependencies can include product development, computing capacity, energy, suppliers, distribution, customer financing and the pace of customer adoption. Compare these with stated costs, competition, delays, customer concentration, regulation, intellectual-property questions, privacy and cybersecurity.
Keep the unit and period consistent when comparing companies. Useful dimensions include their role as an AI seller, provider or internal adopter; the strength of evidence; disclosed commercial traction; execution dependencies; economics; governance and risk; and the time period and geographies covered. These are comparison questions, not a universal scoring system. Companies disclose different levels of detail, and silence is not proof that a company has no exposure.
Build a dated evidence table
A compact evidence log makes claims traceable and helps keep company assertions separate from independently reported facts.
| Field | What to record |
|---|---|
| Source | Title and channel, such as Form 10-K, product page, investor presentation or earnings call material. |
| URL and date | Exact URL and publication or filing date. For a filing, also note the fiscal period. |
| Passage | The exact passage or a faithful paraphrase, with the relevant page or item number. |
| Claim type | Current product, observed result, target, forecast, strategy or risk. |
| Provenance | Company assertion or independently reported fact. Keep separate rows when both are relevant. |
Before publishing or relying on a conclusion, check newer quarterly filings, material Form 8-Ks and updated investor materials. A dated log makes it easier to spot when a company’s statements have changed.
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AMD’s FY2026 Form 10-K, filed February 4, 2026, discusses demand for AI accelerators, customer use, data-center capacity and energy constraints, as well as uncertainty about the trajectory of generative AI adoption. AMD states: “The demand for such products will in part depend on the extent to which our customers utilize generative AI solutions in a wide variety of applications, and both the near-term and long-term trajectory of such generative AI solutions is unknown.” This is AMD’s company disclosure in its filing, not an independent forecast. The example illustrates why a demand claim should be recorded together with the conditions on which it depends.
Write a conclusion that does not outrun the evidence
State what the evidence supports: the company’s AI role, the business mechanism, any reported outcomes, and the principal dependencies or risks. Then state what the available disclosures cannot establish—for example, revenue materiality, sustained customer traction, competitive advantage or valuation—unless a dated source supports that specific conclusion. Do not turn management optimism or an AI-branded product into a stronger claim than the evidence allows.
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If you are collecting public product and investor pages for this analysis, ScreenshotNeo can return a page capture with one GET request. Its cookie/consent-banner handling, popup and chat-widget removal can be turned off; these steps happen before capture. Bot checks, blank pages and failed loads are not billed, and responses indicate the page verdict and billing status. Its MCP server provides screenshot tools for AI agents, and its plans include 1,000 screenshots a month free with no card; paid plans start at $5 for 3,000. ScreenshotNeo is a website screenshot API and MCP server by Yorker Media.
cURL:
curl -G "https://api.screenshotneo.com/v1/shot" -d access_key=YOUR_API_KEY --data-urlencode url=https://stripe.com -o shot.webpSee the ScreenshotNeo API documentation for request options. The same capture can be requested in Python or Node.js:
Python:
import requests
r = requests.get("https://api.screenshotneo.com/v1/shot", params={"access_key": "YOUR_API_KEY", "url": "https://stripe.com"}, timeout=90)
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const res = await fetch(`https://api.screenshotneo.com/v1/shot?${q}`);For screenshots of company materials, replace the example URL with the page you want to capture. Sign up for ScreenshotNeo to get 1,000 free screenshots a month with no card.
Frequently Asked Questions
Does an AI mention in a 10-K prove the company has significant AI exposure?
No. It shows that management discussed AI, not that the exposure is financially material or that a deployment is successful. Assess the underlying business mechanism and evidence.
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Is an AI product announcement evidence of customer traction?
Not on its own. Look for disclosed adoption, customers, results or revenue contribution, and distinguish those from plans, pipeline and forecasts.
Can I compare companies with different levels of AI disclosure?
You can compare the same dimensions and periods, but note disclosure gaps explicitly. Non-disclosure does not establish that a company has no AI exposure.
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