AI is an opportunity for a software company only when it creates customer value the company can adopt, defend, and turn into durable financial benefit. It is a disruption risk when competitors or new entrants can provide the same job more effectively or cheaply, weakening the company’s product or economics. The technology label alone does not tell investors which outcome is more likely; customer evidence, costs, competition, execution, and risks do.
Why AI exposure is not the same as an AI investment case
A company may use AI internally, add AI features, or announce a new product without showing that customers use it, pay for it, stay because of it, or receive a meaningfully better outcome. Those are separate claims and require separate evidence.
The technology can also cut both ways for one issuer: it may improve the company’s own products and operations while making existing software less distinctive or easier to replace. Investors need to ask which effect matters most to that company’s customers and financial results—not simply whether the company mentions AI.
For example, Trimble’s 2025 annual report says it uses AI and generative AI across products, services, and operations, including customer service, data analytics, product development, and code creation. The filing also warns that investment may not benefit the business, competitors may use AI more successfully, regulation may add costs or restrictions, outputs may be erroneous or misleading, and software solutions may become obsolete or noncompetitive. This is one issuer’s description of its exposure, not proof of a sector-wide outcome or a prediction of what will happen to Trimble. Trimble’s 2025 annual report filed with the SEC
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Compare companies using the same evidence tests
Apply the same questions to each company you are evaluating. A shared AI vocabulary is not comparable evidence: an SEC Investor Advisory Committee Disclosure Subcommittee draft dated November 18, 2025, says issuer statements about AI integration are “inconsistent and difficult to compare.” The document is a draft recommendation for committee discussion, not adopted SEC guidance or a final rule. It points to varying definitions, rapid technological change, limited measures of operational impact, and uneven adoption and training as reasons comparisons are difficult. SEC Investor Advisory Committee Disclosure Subcommittee draft recommendation
1. Customer value: what job does the AI do?
Identify the customer task the feature addresses and what changes for the user. Is the result more accurate, faster, less expensive, or otherwise meaningfully better than the prior product or an alternative? A feature description is not evidence of customer value unless the issuer explains the outcome.
2. Adoption: is it being used at scale?
Separate a demonstration, pilot, or product announcement from a generally available feature with sustained customer use. Look for evidence that customers adopt, retain, or purchase the capability. If adoption depends on major redesign, employee training, or workflow changes, those hurdles matter to how quickly any benefit might arrive.
3. Economics: what benefits and costs are established?
Look for reported effects on revenue, customer retention, productivity, or costs, and distinguish measured results from management expectations. Consider development, computing infrastructure, support, and sales costs. A launch or investment announcement by itself does not establish return on investment.
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4. Competitive position: does AI strengthen or weaken the product?
Ask whether AI deepens the value of an existing product and customer relationship, or makes the product’s core functionality easier to obtain elsewhere. Consider whether competitors can deliver similar results at lower cost or in a better workflow, and whether the issuer can keep its advantage as models and products change.
5. Execution and risk: can the company sustain the capability?
Assess whether the company can fund product development, infrastructure, data, and talent while managing privacy, security, intellectual-property, inaccurate-output, third-party model or infrastructure, regulatory, and customer-trust risks where material. Consider whether its filings describe uncertainty and alternatives as well as potential benefits.
These tests organize questions about materiality, financial results, adoption barriers, competition, and regulation. They are an investor’s analytical framework, not a formal regulator scoring system.
Signals that tilt the case toward disruption
- The core product is easier to substitute. Competitors or new entrants may offer similar functionality at lower cost or through a better workflow, reducing the incumbent’s differentiation.
- Customer uptake is unclear or costly to achieve. A feature may fail to attract sustained use or payment, or customers may need substantial training and workflow redesign before they can benefit.
- Spending is visible but benefits are not. Heavy investment without reported customer or business outcomes deserves scrutiny, particularly when technology, compliance, or competitive costs are rising.
- The public narrative outruns operating evidence. Specific, confident promotional claims that lack support in filings or other operating evidence warrant verification. Regulators warn that false AI product claims and hype can be used to manipulate investors.
Signals that support an opportunity case
- A defined customer need is served. AI improves an existing product or enables a new service in a way customers value, rather than appearing only as a headline feature.
- Deployment is connected to outcomes. The company explains where AI is used and links it to measurable product, operational, or financial effects. The SEC committee draft recommends discussing internal and consumer-facing deployment separately when material; that remains a draft recommendation.
- The advantage looks sustainable. The issuer has a credible way to fund and maintain the capability, manage model and data risks, and compete as others improve their products.
How to verify a company’s AI claims
- Start with issuer filings. Find the company’s annual and quarterly filings and compare its AI description with discussion of risks, costs, competition, and business results. The joint SEC, NASAA, and FINRA investor alert specifically recommends reviewing company disclosures and consulting EDGAR. Joint SEC, NASAA, and FINRA investor alert on AI and investment fraud
- Check what the claim actually measures. Distinguish product availability from customer adoption, adoption from willingness to pay, and management’s expected benefit from a reported financial or operational result.
- Compare like with like. Use the same questions for competitors, while noting differences in definitions and reporting. Do not assume that two companies using the term “AI” describe equivalent deployments or outcomes.
- Read promotional claims skeptically. The January 25, 2024 joint investor alert says companies might claim AI will affect operations and drive profitability, and cautions that technological hype can be used to lure investors into schemes. It represents the views of SEC staff and is not an SEC rule or regulation.
A separate 2026 SEC-filed Morgan Stanley Institutional Fund prospectus lists volatile expectations, competition, rapid obsolescence, uncertain research-and-development outcomes, and speculative agentic AI exposure as risks of investing in AI-company securities. That is a fund disclosure about investment risks, not empirical evidence about every software company. Morgan Stanley Institutional Fund prospectus filed with the SEC
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