AI washing: when artificial intelligence becomes empty marketing

As artificial intelligence advances rapidly, companies are competing to demonstrate their technological leadership. This has given rise to the term “AI washing,” which describes situations where organizations exaggerate, distort, or present their use of AI in an unclear manner.

The concept is similar to greenwashing, where companies make environmental claims without real evidence. In AI washing, the focus is on declared technological capabilities and how they are marketed.

What is AI washing?

AI washing occurs when a company tries to convey that it uses artificial intelligence more broadly or more sophisticatedly than the facts allow.

For example, this can happen when a company labels an automated feature as AI without explaining how it works, or when it presents the technology as central to its operations despite its limited use.

A 2025 study published in AMS Review defines the phenomenon as the exaggerated or incorrect representation of a company’s AI capabilities to enhance its market appeal and gain a competitive edge.

The issue isn’t necessarily about using third-party tools, but about the gap between what’s disclosed and what’s actually implemented.

How to identify AI washing

Professionals, consumers, and investors can rely on clear indicators to assess AI claims:

• Frequent use of terms like “AI-powered” or “proprietary AI” without specifying the application;
• Lack of information about where the technology is applied;
• Promises of benefits without verifiable indicators;
• Generic descriptions that fail to distinguish between AI, automation, or conventional software;
• Announcement of AI projects without evidence of actual implementation.

The SEC, the U.S. capital market regulator, has warned companies about the risks of making unfounded claims. In 2024, its chairman, Gary Gensler, stated that companies must be able to explain how and where AI is used and the basis for their claims.

Why is AI washing a problem for professionals?

The widespread adoption of AI has increased pressure on companies to demonstrate technological capability, sometimes creating a gap between external communication and internal reality.

A company might claim to use AI to boost team productivity, while in practice, the technology is limited to a pilot with no significant impact.

For professionals, understanding this difference helps assess the real impact of AI in their field and ask more objective questions before adopting a tool or joining a project.

AI washing can harm companies and investors

The issue has also been studied in the financial market. A 2026 study in Finance Research Letters analyzed U.S. companies from 2018 to 2023 and found a link between unsubstantiated AI claims and subsequent negative market reactions.

Another 2026 study examined Chinese publicly traded companies from 2010 to 2024 and identified a connection between AI washing and a higher risk of stock price decline, attributing this to increased information asymmetry and investor expectations.

In 2025, the CFA Institute published a report on the topic, recommending that investors ask what technology is used, how it was integrated into processes, and what results can be demonstrated.

How to evaluate claims about artificial intelligence

Before accepting that a product, service, or company uses AI, certain questions can help distinguish concrete application from rhetoric:

Where is AI used? A general claim doesn’t specify which process has been automated or improved.

What technology is involved? It’s important to differentiate between generative models, machine learning, traditional automation, and other technologies.

What results were achieved? Indicators, tests, and application examples help verify whether there was a measurable effect.

Who developed the solution? A company may use a model created by another, which doesn’t invalidate AI use but clarifies each organization’s role.

Is the use in production? A tool under testing, a pilot project, and a technology integrated into operations represent different adoption levels.

For those in technology or business, understanding these distinctions enables more accurate interpretation of advertisements, corporate presentations, and job postings that mention AI.