As vendors overstate AI capabilities through vague marketing and unsubstantiated claims, buyers must scrutinise products carefully to avoid falling prey to misleading deployments that erode trust in the AI industry.
AI washing has become a familiar problem as software vendors compete to attach artificial intelligence claims to products that may only use it at the margins. The term refers to marketing that overstates how much AI is actually doing, whether the system is mostly automation, thinly disguised human labour or a genuine model that adds little practical value. As Axios and the AI Now Institute have noted in earlier discussions of the issue, the danger is not only deception but also the erosion of trust in the wider AI market.
The first warning sign is vagueness. A serious vendor should be able to explain the task the system performs, the input it receives and the point at which a person takes over. When a company leans on words such as “intelligent”, “adaptive” or “AI-native” without describing the actual function, it is often selling atmosphere rather than capability. That distinction matters because buyers need to know whether AI is central to the product or merely decorative.
A second clue is when the product would barely change if the AI were removed. Many tools now include summaries, chatbots or drafting aids, but a minor feature does not justify a large premium if the core workflow remains the same. Gartner’s widely cited warning about AI washing made a similar point: some vendors appear to use the label for basic automation rather than meaningful intelligence.
Another sign is marketing that describes the roadmap as if it were the present tense. Buyers should be alert when a beta is sold as a finished product, or when a demo is presented as though every customer already has autonomous functionality. Expectations can be powerful; recent academic work on AI washing suggests that claimed AI support can shape user belief even when performance does not improve. That makes it especially important to separate live features from promises.
Performance claims also need context. Numbers such as accuracy, speed or cost reduction are meaningful only when the baseline, dataset and failure rate are clear. The US Federal Trade Commission’s action against Workado over its AI-content detector is a reminder that unsupported claims can be challenged when the evidence does not match the marketing. A buyer should ask what was measured, on which data and whether the result can be reproduced on real-world inputs.
The demo itself can mislead if it avoids messy reality. Clean examples do not show how a product handles incomplete records, unclear instructions, poor audio or unusual file formats. The same is true of claims about autonomy. The issue is not whether humans are involved, but whether that labour has been hidden. Research and reporting on AI washing have repeatedly shown that some products described as automated depend heavily on people behind the scenes.
Companies also raise suspicion when they hide behind the phrase “proprietary AI” instead of explaining where the value comes from. A product can absolutely be built on a third-party model and still be worthwhile if it adds workflow design, retrieval, monitoring, access controls or domain knowledge. But if the answer to what is distinctive amounts to a prompt and a new interface, the buyer should be cautious. That concern sits close to the broader criticism of AI washing in both academic and trade coverage.
The most responsible vendors are usually the most specific about limits. They can say where the system should not be used, when it needs human review and how it signals uncertainty. That is consistent with the practical guidance often given around AI deployment: the value of a feature lies not in a grand label but in whether it performs a defined task reliably under known conditions. If a seller cannot name a failure mode, it is probably describing an aspiration rather than a product.
For buyers, the clearest test is simple: does the AI version improve an outcome that matters enough to pay for? If it does not reduce rework, speed completion, improve retrieval or lower operating cost in a measurable way, then the label is just positioning. AI washing thrives when the marketing story is grander than the operational result. The stronger purchase is the one that can prove its worth in your own workflow, with your own data and clear limits.
Disclaimer: This content is intended for informational purposes only. Readers are advised to exercise their own judgement, conduct due diligence, or consult a qualified expert before acting on any information provided.
