What Is AI Washing? Examples and How to Avoid It

What is AI Washing?

AI washing is when a company makes inflated or misleading claims about their AI capabilities to oversell to customers or inflate value to investors. The liberal use of AI speakery (also known as “buzzwords”), allows businesses to capitalize on AI hype without taking the time to develop true AI offerings. 

Legitimate AI terminology often comes into play here. Terms like generative AI, deep learning, agent assist, cognitive computing, etc. are real AI terms that describe AI subsets, models and systems. AI washing happens when terms like these are sprinkled into blog posts and other communications to describe tech products that may not use them fully or at all.

In the larger business ecosystem, there’s a storied history of overstating AI capabilities, particularly around predictive analytics and machine learning technologies. The introduction of ChatGPT in 2022 in late 2022, amplified the problem with terms like generative AI, large language models and intelligent chatbots becoming incredibly popular AI buzzwords.

As the demand for AI technology grows, the pressure for companies to use it or incorporate it into their own product offerings has grown as well. Companies that mention AI in their earnings calls tend to see better stock performance and AI-focused startups are more likely to secure funding. This creates a dynamic where companies feel compelled to claim they have “cutting edge” AI capabilities even when they don’t so they can compete with those (like Monetate) who do.

The problem is compounded when marketing departments, eager to capitalize on AI trends, may rush to promote capabilities that engineering teams haven’t fully developed. This gap between fantasy and reality can compel marketers to make overstated (or outright false) claims about what a company’s AI technology can do.

Why AI Washing is Everywhere—A Checklist 

As we touched on above, businesses of all sizes face mounting pressure to demonstrate AI capabilities. Adoption of AI tools and capabilities spiked last year across industries. This pressure comes from multiple directions including:

• Investment Incentives: AI-focused startups attract significantly more funding than non-AI startups. Global venture capital investment in AI companies exceeded $100 billion in 2024, up a staggering 80% from the previous year. This represents a dramatic shift in investment priorities, with one-third of all venture funding now directed toward AI-related companies.
• Stock Market Benefits: Public companies that highlight AI capabilities in their earnings calls consistently outperform those that don’t. This direct link between AI messaging and stock performance is a powerful incentive that motivates companies to emphasize—and sometimes overstate—their AI capabilities.
• The AI Gap: A widening divide exists between early AI adopters and more cautious organizations (e.g., leaders vs. laggards). Companies desperate to bridge this gap often become prime targets for vendors making unrealistic AI promises, leading to rushed partnerships that can’t deliver.
• Internal Misalignment: The disconnect with what AI can and can’t do often starts within organizations themselves. As noted above, marketing departments may push to promote AI capabilities before they’re ready. It’s not the time to promote your AI functionality when the engineering team is still in the stages of development. That road leads to false claims and inflated expectations. 
• Competitive Dynamics: AI competition is fierce and that’s not likely to change anytime soon. When businesses see their competitors promoting AI capabilities, they feel pressured to make similar claims regardless of their actual AI maturity. This creates an environment where AI claims frequently outpace technological reality.

Real AI Washing Examples

When companies get caught making false or misleading claims about their AI capabilities, the consequences can be severe. They may face regulatory action, steep fines, costly lawsuits and irreparable damage to their reputation. Here are some notable examples of AI washing that resulted in serious repercussions:

1. Apple’s “Intelligence” Claims

Apple faces a class-action lawsuit over its heavily marketed “Apple Intelligence” features and the claim that this significantly enhanced Siri capabilities. The company promoted these AI features as key selling points for the iPhone 16, but later admitted many wouldn’t be available until 2026. The lawsuit alleges Apple “deceived millions of consumers” who purchased new iPhones based on the promise of AI features that weren’t available. 

2. DoNotPay’s AI Legal Services

DoNotPay, a company marketing itself as an AI-powered legal service provider, got in hot water with the FTC for claiming its technology could replace traditional legal services and handle complex legal matters. After investigating, the FTC found the company had no evidence to support these claims and ordered them to notify customers that the service was more limited than they were lead to believe.

3. GitLab Misleads Investors 

GitLab, a DevSecOps platform, was accused of misleading investors by claiming it would develop AI software features as a way to boost market demand for the platform. The lawsuit, filed on behalf of Gitlab’s shareholders, claims that GitLab’s statements about incorporating AI into its platform created a false impression of the company’s capabilities. In reality, there was weak market demand for these AI features and the company faced increased expenses related to its joint venture in China.

4. AppLovin’s Alleged Ad Platform Deception

AppLovin, a software platform for advertisers, is facing a class action securities lawsuit for allegedly misleading investors about its AXON 2.0 digital ad platform. While the company claimed to use “cutting-edge AI technologies” to enhance ad targeting and expand into web-based marketing, analyst reports revealed that AppLovin was allegedly reverse engineering and exploiting advertising data from Meta Platforms. The company is also accused of artificially inflating its performance metrics through deceptive practices. Specifically, self-clicking ads and forced shadow downloads.  

5. Two Investment Firms Make False AI Claims

Two investment advisory firms were fined by the SEC for falsely claiming to use AI to improve their investment strategies. Global Predictions advertised “expert AI-driven forecasts” and claimed to be the first regulated AI financial advisor, while Delphia falsely claimed to use AI to analyze client data for investment predictions. Neither firm could substantiate their AI claims. The SEC fined Global Predictions $175,000 and Delphia $225,000 for violating marketing rules aimed at preventing deceptive practices. 

How to Spot AI Washing

As AI adoption accelerates, so will AI washing. The pressure to appear AI-capable is increasing from both sides of the table—companies that seek to use AI to improve workflows and increase revenue and those who want to jump on board the “we’ve got awesome AI capabilities” bandwagon. Here are some key strategies to help you cut through the noise and distinguish AI washing from the real deal.

1. Demand Transparency

Companies with real AI technology are typically forthcoming about their capabilities and limitations. Don’t be shy about asking for detailed documentation on AI models and processes. Also, be on the lookout for vague buzzwords and promises that seem too good to be true. Legitimate AI tools are connected to concrete use cases and measurable outcomes. Companies like Monetate have an established track record (we’ve been in the business of AI personalization for over a decade) and can clearly demonstrate how AI is methodically integrated into platform capabilities. 

2. Verify Timelines

Concrete examples are your friend when trying to determine if an AI claim is real or not. Pay close attention to how companies talk about implementation timelines and their development roadmap. Legitimate AI providers will give you specific, realistic timelines for feature rollouts. They’ll be upfront about what’s currently in development versus what’s already deployed. If a company can’t provide clear answers about when features will be available or consistently pushes back release dates, consider it a red flag.

3. Ask for Real-World Results

Don’t take marketing claims at face value. Ask for case studies, customer testimonials and concrete metrics that demonstrate a platform or technology’s effectiveness. Legitimate AI companies will have a portfolio of successful implementations that show exactly how their technology drives business value. A tech vendor should be able to clearly articulate what specific business problems their AI solves, how their AI makes decisions and what data sources inform these decisions. They should also be able to explain how they measure and validate the AI’s effectiveness. 

4. Clarify the Technical Foundation

Companies genuinely using AI will have a strong technical team behind their products. Look for evidence of data scientists, machine learning engineers and AI specialists on staff. They should be able to explain their AI architecture, training methods and how they ensure accuracy and fairness in their algorithms. Be wary of companies that can’t or won’t provide these details.