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AI is an opportunity for a software company only when it solves a customer problem, attracts real use, and can create durable value after development and operating costs. It is a disruption risk when competing tools weaken the company’s product or customer relationships—or when investment and confident claims outpace evidence. Assess each company’s customer outcomes, adoption, economics, competition, execution, and risks rather than treating “AI” as a verdict on the stock.
Why AI exposure is not the same as an AI opportunity
A company can use AI internally or add AI features without proving that customers value them, that the company stands apart from competitors, or that the investment will improve financial results. The same technology can strengthen an existing product and make another company’s core functionality easier to replace.
Trimble’s 2025 annual report illustrates both sides. The company says it uses AI and generative AI in products, services, and operations, including customer service, data analytics, product development, and code creation. It also cautions that its investments may not benefit the business, competitors may use AI more successfully, regulation may impose costs or restrictions, outputs can be erroneous or misleading, and software solutions may become obsolete or noncompetitive. Those disclosures describe Trimble’s stated exposures; they do not establish what will happen to the company or prove that every software issuer faces the same conditions. Trimble’s 2025 annual report
That distinction matters because an announcement, demonstration, or AI label is not evidence of customer adoption or return on investment. Investors need to connect the technology to a specific customer job and then look for evidence that the product is used, valued, and economically sustainable.
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A practical framework for comparing software companies
Apply the same questions to each issuer. The framework below adapts materiality considerations discussed in an SEC Investor Advisory Committee draft recommendation and risks described in company filings; it is an analytical tool, not a regulator scoring system.
| Test | Evidence to look for | What should prompt caution |
|---|---|---|
| Customer value | What task does the AI feature perform? Does it make the customer’s outcome meaningfully better than the prior product or another available option? | Vague claims about innovation without a clear customer problem or distinct benefit. |
| Adoption | Is the feature available to customers, used in practice, retained, or purchased? Separate pilots and demonstrations from evidence of scaled use. | Launch activity presented as proof of broad adoption, or no explanation of barriers such as training and workflow changes. |
| Economic impact | Does the issuer report effects on revenue, retention, productivity, or costs? What does it spend on development, computing, support, and sales? | Implied returns based on feature launches alone, without evidence that benefits exceed the costs. |
| Competitive position | Does AI reinforce the product’s differentiation and customer relationships, or make its core functions easier to obtain elsewhere? | Competitors offer a better workflow or similar functionality at lower cost, while the incumbent offers no clear response. |
| Execution and investment | Can the company sustain investment in products, infrastructure, data, and talent? Do its disclosures explain uncertainty and alternatives? | Heavy commitments without a credible account of customer value, execution, or financial benefit. |
| Risks and constraints | Consider privacy, security, intellectual property, inaccurate output, dependence on outside models or infrastructure, regulation, and customer trust when material. | Risk factors that could undermine adoption, raise costs, or limit the company’s ability to deliver the feature reliably. |
For context, the SEC Investor Advisory Committee Disclosure Subcommittee’s November 18, 2025 document is a draft for committee discussion, not adopted SEC guidance or a final rule. It recommends considering material effects on growth and financial results, adoption barriers, adverse developments, competition, and regulation; it also suggests distinguishing internal AI deployment from customer-facing uses when material. The draft says: “This has left investors with having to sort through issuer statements regarding AI integration into operations that are inconsistent and difficult to compare.” Its point is that a shared AI label does not make company disclosures comparable. SEC Investor Advisory Committee draft recommendation, November 18, 2025
Signals of disruption risk and opportunity
Signals consistent with disruption risk
- The company’s existing product may become less differentiated if competitors or new entrants deliver similar functionality more cheaply or through a better workflow.
- Customers do not adopt or pay for AI features, or adoption depends on expensive redesign, training, or change management.
- The company describes substantial AI spending but cannot yet show business benefit, while also identifying competitive pressure or rising technology and compliance costs.
- Public claims are more confident or specific than the company’s filings and operating evidence. Promotional AI language deserves scrutiny, especially when it substitutes for measurable customer or financial results.
Signals consistent with opportunity
- AI addresses a defined customer need within an existing product or supports a new service customers value.
- The issuer explains where the technology is deployed and connects it to measurable product, operational, or financial outcomes.
- The company can fund and maintain the capability, manage model and data risks, and defend its position as competitors improve.
These are indicators to investigate, not guarantees. A company may show opportunity and disruption signals at the same time; the relevant question is which effects are material to its customers and economics.
How to check a company’s AI claims
- Find the primary disclosure. Search the company’s filings on SEC EDGAR and read its latest annual report and relevant updates. Look for concrete descriptions of AI use, expected benefits, spending, uncertainties, and risks.
- Separate deployment from promotion. Note whether a claim concerns internal operations, a customer-facing feature, a pilot, or a generally available product. Treat those stages as different evidence, not interchangeable proof of adoption.
- Compare like with like. Check how similar businesses describe the use case, adoption, costs, and risks. Because companies use varying definitions and disclose operational effects unevenly, identical terminology may refer to different levels of deployment or evidence.
- Test the economics. Look for reported customer, revenue, retention, productivity, or cost effects and compare them with development, computing, support, and sales demands. If the issuer does not quantify an effect, do not infer it from a product announcement.
- Check the downside alongside the upside. Consider competition, adoption barriers, regulation, privacy, security, intellectual property, output accuracy, and dependence on third-party models or infrastructure where relevant.
- Be skeptical of certainty. A joint SEC, NASAA, and FINRA investor alert advises reviewing company disclosures, comparing claims with similar businesses, and consulting EDGAR. It warns that AI hype and false product claims can be used to lure investors into schemes, and that promises of guaranteed returns are a warning sign. The alert represents SEC staff views and is not an SEC rule or regulation. SEC, NASAA, and FINRA investor alert, January 25, 2024
How to interpret sector-wide warnings
A 2026 SEC-filed Morgan Stanley Institutional Fund prospectus discusses risks associated with securities of AI companies, including volatile expectations, competition, rapid obsolescence, uncertain research-and-development outcomes, and speculative agentic AI exposure. That language is a fund disclosure about investment risks; it is not an empirical finding about every software company or the sector as a whole. Morgan Stanley Institutional Fund prospectus, 2026
For an individual software issuer, company-specific evidence is more useful than a broad AI narrative. An investor should be able to explain what customer value the company claims, what shows customers are realizing it, what it costs to deliver, and what could prevent that value from lasting. If key parts are missing, the opportunity remains unproven rather than established by the presence of AI.
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