The Rippling Effect of AI-Washing

AIwashing is the latest phenomenon where companies claim legacy automation tools as AI-driven to ride the GenAI wave. According to Gartner, many companies are taking advantage of this and marketing simplified workflows as intelligent, autonomous agents to attract buyers, regardless of actual AI usage.  

This tactic isn’t just misleading: it distorts the market, inflates expectations, and loses user trust. Even more concerning, out of thousands of agent-marketed products, only around 130 qualify as genuine, capable intelligent agents. The rest are technologically superficial. 

This phenomenon can also cause: 

 

Cancelled AI Project

Cancelled AI Projects

A Carnegie Mellon–Salesforce benchmark showed AI agents correctly handle only about 30–35% of multi-step tasks. For single-step tasks, the success rate is around 58%, and it has been noted that agents fail about 70% of assigned tasks. Further, humans often must oversee or fix failed tasks, so the process is technically not autonomous.  

Due to increasing costs, unclear business value, and/or inadequate risk controls, Gartner predicts that over 40% of AI projects may be scrapped by 2027. 

Reduces Credibility

AI-washed products reduce the actual capabilities of next-gen tools, making it even more difficult to distinguish true AI solutions. This lack of trust can prevent progress and undermine the work being done.   

Reduces Credibility
Economic Fallout

Economic Fallout

The financial ramifications are stark. As mentioned, with 40% of AI projects that could be cancelled, companies can stand to lose billions while pursuing the latest tech. This can cause hesitancy to invest in other leading tech or pursue more AI tools.  

It's been warned that if AI-washing continues to go unchecked, it can cause distrust, prevent progress, and stifle long-term investment. While this stems from overhype, consumers can make themselves aware of which companies are actually offering AI-powered solutions. In turn, this will motivate companies to build capable, reliable, and trustworthy agentic systems.